feat(simulation): auto-build source mesh collision

apps/control-station/src/core/laboratory/advancedIndex.ts

@@ -48,13 +48,9 @@ import { fetchM48SFixedClassDetectorResult } from "./m48sFixedClassDetector";
 import { fetchM48TRiskQualityResult } from "./m48tRiskQuality";
 import { fetchM49TgsFailClosedResult } from "./m49TgsFailClosed";
 import { fetchM49TgsFullShadowResult } from "./m49TgsFullShadow";
-import {
-  fetchVegetationBenchmarkResult,
-  fetchVegetationShadowResult,
-} from "./vegetationShadow";
+import { fetchVegetationShadowResult } from "./vegetationShadow";
 
 export type AdvancedLaboratoryWorkId =
-  | "lab-v1-vegetation-benchmark"
   | "lab-v1-vegetation-shadow"
   | "m48-object-centric-quality"
   | "m48-small-static-passage-regression"
@@ -106,7 +102,6 @@ export interface AdvancedLaboratoryIndexItem {
 }
 
 const WORK_IDS: readonly AdvancedLaboratoryWorkId[] = [
-  "lab-v1-vegetation-benchmark",
   "lab-v1-vegetation-shadow",
   "m48-object-centric-quality",
   "m48-small-static-passage-regression",
@@ -153,7 +148,6 @@ const WORK_IDS: readonly AdvancedLaboratoryWorkId[] = [
 ];
 
 const RESULT_PREFIX: Readonly<Record<AdvancedLaboratoryWorkId, string>> = {
-  "lab-v1-vegetation-benchmark": "lab-v1-vegetation-benchmark",
   "lab-v1-vegetation-shadow": "lab-v1-vegetation-shadow",
   "m48-object-centric-quality": "m48-object-quality-(?:pack|result)",
   "m48-small-static-passage-regression": "m48-small-static-passage-regression",
@@ -207,7 +201,6 @@ export function isAdvancedLaboratoryWorkId(
 
 export function emptyAdvancedLaboratoryResults(): AdvancedLaboratoryResults {
   return {
-    vegetationBenchmark: null,
     vegetationShadow: null,
     m47Graph: null,
     m48: null,
@@ -342,8 +335,7 @@ export function advancedLaboratoryResultAvailable(
   workId: AdvancedLaboratoryWorkId,
   results: AdvancedLaboratoryResults,
 ): boolean {
-  return workId === "lab-v1-vegetation-benchmark" ? results.vegetationBenchmark !== null
-    : workId === "lab-v1-vegetation-shadow" ? results.vegetationShadow !== null
+  return workId === "lab-v1-vegetation-shadow" ? results.vegetationShadow !== null
     : workId === "m48-object-centric-quality" ? results.m48 !== null
     : workId === "m48-small-static-passage-regression" ? results.m48SmallStatic !== null
     : workId === "m48-static-occupancy-qualification" ? results.m48StaticOccupancy !== null
@@ -401,10 +393,7 @@ export async function fetchAdvancedLaboratoryResult(
   } = {},
 ): Promise<AdvancedLaboratoryResults> {
   const results = emptyAdvancedLaboratoryResults();
-  if (workId === "lab-v1-vegetation-benchmark") {
-    if (!resultId) throw new AdvancedLaboratoryContractError("Vegetation benchmark identity не выбрана.");
-    results.vegetationBenchmark = await fetchVegetationBenchmarkResult(resultId, { fetcher, signal });
-  } else if (workId === "lab-v1-vegetation-shadow") {
+  if (workId === "lab-v1-vegetation-shadow") {
     if (!resultId) throw new AdvancedLaboratoryContractError("Vegetation LAB identity не выбрана.");
     results.vegetationShadow = await fetchVegetationShadowResult(resultId, { fetcher, signal });
   } else if (workId === "m48-object-centric-quality") {

apps/control-station/src/core/laboratory/advancedLaboratoryResults.ts

@@ -45,7 +45,6 @@ import type { M49TgsFullShadowResult } from "./m49TgsFullShadow";
 import type { VegetationShadowResult } from "./vegetationShadow";
 
 export interface AdvancedLaboratoryResults {
-  vegetationBenchmark: VegetationShadowResult | null;
   vegetationShadow: VegetationShadowResult | null;
   m47Graph: M47ReferenceGraphLabResult | null;
   m48: M48AdvancedResult | null;

apps/control-station/src/core/laboratory/advancedResults.ts

@@ -967,7 +967,6 @@ export async function fetchAdvancedLaboratoryResults({
   const e39 = settledCatalogValue(settled[7]);
   const e40 = settledCatalogValue(settled[8]);
   return {
-    vegetationBenchmark: null,
     vegetationShadow: null,
     m47Graph: null, m48: null, m48SmallStatic: null, m48StaticOccupancy: null,
     m48r3StaticOccupancy: null,

apps/control-station/src/core/laboratory/vegetationShadow.ts

@@ -1,7 +1,6 @@
 import type { LaboratoryFetch } from "./advancedResults";
 
 const RESULT_ID = /^lab-v1-vegetation-shadow-[a-f0-9]{64}$/;
-const BENCHMARK_RESULT_ID = /^lab-v1-vegetation-benchmark-[a-f0-9]{64}$/;
 const SHA256 = /^[a-f0-9]{64}$/;
 const CANDIDATES = ["ddrnet", "ppliteseg"] as const;
 const ROUTE_MODES = ["source", "ddrnet", "ppliteseg", "urban", "rural", "offroad"] as const;
@@ -56,9 +55,7 @@ export interface VegetationVideoSemanticClass {
   classId: number;
   label: string;
   colorRgb: readonly [number, number, number];
-  disposition: "labeled" | "ambiguous" | "prediction" | "undefined";
-  materialClass: string | null;
-  evidenceState: string | null;
+  disposition: "prediction" | "undefined";
 }
 
 export interface VegetationRouteVideo {
@@ -70,77 +67,8 @@ export interface VegetationRouteVideo {
   height: 600;
   centerCropXyxy: readonly [100, 0, 700, 600];
   outsideCropState: "undefined";
-  viewKind: "fine-semantic-prediction" | "coarse-material-policy-review";
-  linkedTgsResultId: string | null;
   taxonomy: readonly VegetationVideoSemanticClass[];
   aggregatePredictionPixels: readonly number[];
-  policyPresets: Readonly<Record<string, Readonly<Record<string, string>>>> | null;
-  fusionMode: "synchronised-multilayer-review" | null;
-  validFovMaskSha256: string | null;
-}
-
-export interface VegetationMixedRouteCase {
-  caseId: string;
-  phase: "rural" | "transition" | "urban";
-  sourceSequence: number;
-  sessionSeconds: number;
-  assets: Readonly<Record<"source" | "city" | "vegetation" | "tgs", string>>;
-  tgs: {
-    groundCells: number;
-    occupiedCells: number;
-    rejectedCells: number;
-    unobservedCells: number;
-  };
-}
-
-export interface VegetationMixedRouteReview {
-  sourceId: "RAVNOVES004TREE";
-  sessionId: string;
-  packId: string;
-  frameCount: 10;
-  models: {
-    city: { name: string; inferenceFps: number; endToEndP95Ms: number };
-    vegetation: { name: string; latencyP95Ms: number };
-    tgs: { name: string; latencyP95Ms: number; cellSizeM: number; radiusM: number };
-  };
-  cases: readonly VegetationMixedRouteCase[];
-}
-
-export interface VegetationFullRouteLayer {
-  name: string;
-  resultId: string;
-  frameCount: 6830;
-  taxonomy: readonly VegetationVideoSemanticClass[];
-  inferenceFps: number;
-  latencyP95Ms: number;
-  peakReservedVramBytes: number;
-}
-
-export interface VegetationFullRouteReview {
-  sourceId: "RAVNOVES004TREE";
-  sessionId: "20260828T130511Z_viewer_live";
-  sourceJobId: "recorded-camera-eb2783c5480d56bda07c8af0";
-  sourceJobInputSha256: string;
-  sourceStreamSha256: string;
-  recordedMediaSourceId: "recorded.camera.6a3945242828a038";
-  recordedMediaGenerationSha256: string;
-  frameCount: 6830;
-  width: 800;
-  height: 600;
-  timelineStartSeconds: number;
-  timelineEndSeconds: number;
-  timelineArtifact: {
-    sha256: string;
-    byteLength: number;
-  };
-  frameSourceTimesNs: readonly number[];
-  decodeRepair: {
-    repairedFrameCount: 1;
-    sequence: 6092;
-    method: "duplicate-previous-decoded-frame";
-  };
-  city: VegetationFullRouteLayer;
-  vegetation: VegetationFullRouteLayer;
 }
 
 export interface VegetationShadowResult {
@@ -152,8 +80,6 @@ export interface VegetationShadowResult {
   routeCases: readonly VegetationVisualCase[];
   validationCases: readonly VegetationVisualCase[];
   routeVideo: VegetationRouteVideo | null;
-  routeReview: VegetationMixedRouteReview | null;
-  routeFullReview: VegetationFullRouteReview | null;
   limitations: readonly string[];
   visualShadowReady: true;
   missionPolicyReadyForConfiguration: true;
@@ -260,7 +186,6 @@ function visualCaseValue(
   value: unknown,
   resultId: string,
   expectedKind: "goose" | "ravnoves",
-  endpointRoot: string,
 ): VegetationVisualCase {
   const row = objectValue(value, `vegetation.${expectedKind}.case`);
   exact(row.source_kind, expectedKind, "vegetation.case.source_kind");
@@ -279,7 +204,7 @@ function visualCaseValue(
     if (!SHA256.test(sha256) || !path.startsWith(`visual/${expectedKind}/${caseId}/`)) {
       throw new VegetationShadowContractError(`vegetation.case.assets.${key}: proof invalid.`);
     }
-    projected[key] = `${endpointRoot}/${encodeURIComponent(resultId)}/assets/${path
+    projected[key] = `/api/v1/laboratory/vegetation-shadow/${encodeURIComponent(resultId)}/assets/${path
       .split("/")
       .map(encodeURIComponent)
       .join("/")}`;
@@ -332,9 +257,6 @@ function routeVideoValue(value: unknown): VegetationRouteVideo | null {
     "vegetation.route_video.m47_reference_graph_result_id",
   );
   const baseM4ResultId = textValue(row.base_m4_result_id, "vegetation.route_video.base_m4_result_id");
-  const viewKind = row.view_kind === undefined
-    ? "fine-semantic-prediction"
-    : textValue(row.view_kind, "vegetation.route_video.view_kind");
   if (
     !/^lab-v1-ravnoves-video-ddrnet-[a-f0-9]{64}$/.test(workerResultId)
     || !/^m47-reference-graph-lab-[a-f0-9]{64}$/.test(m47ReferenceGraphResultId)
@@ -342,15 +264,6 @@ function routeVideoValue(value: unknown): VegetationRouteVideo | null {
   ) {
     throw new VegetationShadowContractError("vegetation.route_video: identity invalid.");
   }
-  if (viewKind !== "fine-semantic-prediction" && viewKind !== "coarse-material-policy-review") {
-    throw new VegetationShadowContractError("vegetation.route_video: view kind invalid.");
-  }
-  const linkedTgsResultId = viewKind === "coarse-material-policy-review"
-    ? textValue(row.linked_tgs_result_id, "vegetation.route_video.linked_tgs_result_id")
-    : null;
-  if (linkedTgsResultId && !/^m49-tgs-full-shadow-[a-f0-9]{64}$/.test(linkedTgsResultId)) {
-    throw new VegetationShadowContractError("vegetation.route_video: TGS identity invalid.");
-  }
   exact(row.frame_count, 4489, "vegetation.route_video.frame_count");
   exact(row.width, 800, "vegetation.route_video.width");
   exact(row.height, 600, "vegetation.route_video.height");
@@ -366,9 +279,11 @@ function routeVideoValue(value: unknown): VegetationRouteVideo | null {
     throw new VegetationShadowContractError("vegetation.route_video: crop contract changed.");
   }
   const taxonomy = objectValue(row.taxonomy, "vegetation.route_video.taxonomy");
-  exact(taxonomy.schema_version, viewKind === "coarse-material-policy-review"
-    ? "missioncore.lab-v1-terrain-policy-taxonomy/v1"
-    : "missioncore.lab-v1-vegetation-taxonomy/v1", "vegetation.route_video.taxonomy.schema");
+  exact(
+    taxonomy.schema_version,
+    "missioncore.lab-v1-vegetation-taxonomy/v1",
+    "vegetation.route_video.taxonomy.schema",
+  );
   const classes = arrayValue(taxonomy.classes, "vegetation.route_video.taxonomy.classes")
     .map((value, expectedId): VegetationVideoSemanticClass => {
       const item = objectValue(value, `vegetation.route_video.taxonomy[${expectedId}]`);
@@ -381,95 +296,36 @@ function routeVideoValue(value: unknown): VegetationRouteVideo | null {
       if (color.length !== 3 || color.some((channel) => channel > 255)) {
         throw new VegetationShadowContractError("vegetation.route_video: taxonomy color invalid.");
       }
-      const disposition = item.disposition;
-      if (
-        disposition !== "labeled"
-        && disposition !== "ambiguous"
-        && disposition !== "prediction"
-        && disposition !== "undefined"
-      ) {
+      const disposition: VegetationVideoSemanticClass["disposition"] = expectedId === 0
+        ? "undefined"
+        : "prediction";
+      if (item.disposition !== disposition) {
         throw new VegetationShadowContractError("vegetation.route_video: taxonomy disposition changed.");
       }
-      if (
-        viewKind === "fine-semantic-prediction"
-        && disposition !== (expectedId === 0 ? "undefined" : "prediction")
-      ) {
-        throw new VegetationShadowContractError("vegetation.route_video: fine taxonomy disposition changed.");
-      }
-      const materialClass = item.material_class === null || item.material_class === undefined
-        ? null
-        : textValue(item.material_class, `vegetation.route_video.material[${expectedId}]`);
-      const evidenceState = item.evidence_state === null || item.evidence_state === undefined
-        ? null
-        : textValue(item.evidence_state, `vegetation.route_video.evidence[${expectedId}]`);
       return {
         classId,
         label: textValue(item.label, `vegetation.route_video.label[${expectedId}]`),
         colorRgb: color as unknown as readonly [number, number, number],
         disposition,
-        materialClass,
-        evidenceState,
       };
     });
-  const expectedClassCount = viewKind === "coarse-material-policy-review" ? 10 : 64;
-  if (classes.length !== expectedClassCount) {
-    throw new VegetationShadowContractError("vegetation.route_video: taxonomy size changed.");
-  }
-  if (
-    viewKind === "coarse-material-policy-review"
-    && (classes[9]?.disposition !== "undefined" || classes[9]?.evidenceState !== "UNOBSERVED")
-  ) {
-    throw new VegetationShadowContractError("vegetation.route_video: valid-FOV class changed.");
+  if (classes.length !== 64) {
+    throw new VegetationShadowContractError("vegetation.route_video: taxonomy must contain 64 classes.");
   }
   const aggregatePredictionPixels = arrayValue(
     row.aggregate_prediction_pixels,
     "vegetation.route_video.aggregate_prediction_pixels",
   ).map((value, index) => integerValue(value, `vegetation.route_video.pixels[${index}]`));
-  if (aggregatePredictionPixels.length !== expectedClassCount) {
+  if (aggregatePredictionPixels.length !== 64) {
     throw new VegetationShadowContractError("vegetation.route_video: class accounting changed.");
   }
   const maskArchive = objectValue(row.mask_archive, "vegetation.route_video.mask_archive");
-  exact(maskArchive.path, viewKind === "coarse-material-policy-review"
-    ? "video/coarse-material-policy-masks.zip"
-    : "video/ddrnet-semantic-masks.zip", "vegetation.route_video.mask_archive.path");
+  exact(maskArchive.path, "video/ddrnet-semantic-masks.zip", "vegetation.route_video.mask_archive.path");
   const archiveSha256 = textValue(maskArchive.sha256, "vegetation.route_video.mask_archive.sha256");
   if (!SHA256.test(archiveSha256)) {
     throw new VegetationShadowContractError("vegetation.route_video: archive digest invalid.");
   }
   integerValue(maskArchive.byte_length, "vegetation.route_video.mask_archive.byte_length");
-  let policyPresets: VegetationRouteVideo["policyPresets"] = null;
-  let fusionMode: VegetationRouteVideo["fusionMode"] = null;
-  let validFovMaskSha256: string | null = null;
-  if (viewKind === "coarse-material-policy-review") {
-    const validFov = objectValue(row.valid_fov, "vegetation.route_video.valid_fov");
-    exact(validFov.mask_path, "video/valid-fov-mask.png", "vegetation.route_video.valid_fov.path");
-    validFovMaskSha256 = textValue(
-      validFov.mask_sha256,
-      "vegetation.route_video.valid_fov.sha256",
-    );
-    if (!SHA256.test(validFovMaskSha256)) {
-      throw new VegetationShadowContractError("vegetation.route_video: valid-FOV digest invalid.");
-    }
-    exact(validFov.outside_valid_fov_class_id, 9, "vegetation.route_video.valid_fov.class_id");
-    const policy = objectValue(row.policy, "vegetation.route_video.policy");
-    const presets = objectValue(policy.presets, "vegetation.route_video.policy.presets");
-    policyPresets = Object.fromEntries(Object.entries(presets).map(([presetId, rawRules]) => {
-      const rules = objectValue(rawRules, `vegetation.route_video.policy.${presetId}`);
-      return [presetId, Object.fromEntries(Object.entries(rules).map(([material, action]) => [
-        material,
-        textValue(action, `vegetation.route_video.policy.${presetId}.${material}`),
-      ]))];
-    }));
-    const fusion = objectValue(row.fusion, "vegetation.route_video.fusion");
-    exact(fusion.pixel_raster_fusion, false, "vegetation.route_video.fusion.pixel_raster_fusion");
-    exact(fusion.camera_semantic_temporal_filter, "none", "vegetation.route_video.fusion.camera_filter");
-    exact(
-      fusion.mode,
-      "synchronised-multilayer-review",
-      "vegetation.route_video.fusion.mode",
-    );
-    fusionMode = "synchronised-multilayer-review";
-  }
   return {
     workerResultId,
     m47ReferenceGraphResultId,
@@ -479,364 +335,12 @@ function routeVideoValue(value: unknown): VegetationRouteVideo | null {
     height: 600,
     centerCropXyxy: [100, 0, 700, 600],
     outsideCropState: "undefined",
-    viewKind,
-    linkedTgsResultId,
     taxonomy: classes,
     aggregatePredictionPixels,
-    policyPresets,
-    fusionMode,
-    validFovMaskSha256,
   };
 }
 
-function mixedRouteReviewValue(
-  value: unknown,
-  resultId: string,
-  endpointRoot: string,
-): VegetationMixedRouteReview | null {
-  if (value === null || value === undefined) return null;
-  const row = objectValue(value, "vegetation.route_review");
-  exact(row.source_id, "RAVNOVES004TREE", "vegetation.route_review.source_id");
-  exact(row.frame_count, 10, "vegetation.route_review.frame_count");
-  exact(row.ground_truth, false, "vegetation.route_review.ground_truth");
-  exact(
-    row.selection_policy,
-    "same-scene-camera-lidar-aligned-review-islands/v1",
-    "vegetation.route_review.selection_policy",
-  );
-  const packId = textValue(row.pack_id, "vegetation.route_review.pack_id");
-  if (!/^mixed-route-review-pack-[a-f0-9]{64}$/.test(packId)) {
-    throw new VegetationShadowContractError("vegetation.route_review.pack_id: identity invalid.");
-  }
-  const models = objectValue(row.models, "vegetation.route_review.models");
-  const city = objectValue(models.city, "vegetation.route_review.models.city");
-  const vegetation = objectValue(models.vegetation, "vegetation.route_review.models.vegetation");
-  const tgsModel = objectValue(models.tgs, "vegetation.route_review.models.tgs");
-  exact(city.frames, 10, "vegetation.route_review.models.city.frames");
-  exact(vegetation.frames, 10, "vegetation.route_review.models.vegetation.frames");
-  exact(tgsModel.frames, 10, "vegetation.route_review.models.tgs.frames");
-  const cases = arrayValue(row.cases, "vegetation.route_review.cases").map((raw, index) => {
-    const item = objectValue(raw, `vegetation.route_review.cases[${index}]`);
-    const caseId = textValue(item.case_id, `vegetation.route_review.cases[${index}].case_id`);
-    if (caseId !== `route-${String(index + 1).padStart(2, "0")}`) {
-      throw new VegetationShadowContractError("vegetation.route_review.case order changed.");
-    }
-    const phaseValue = item.phase;
-    if (phaseValue !== "rural" && phaseValue !== "transition" && phaseValue !== "urban") {
-      throw new VegetationShadowContractError("vegetation.route_review.phase changed.");
-    }
-    const phase: VegetationMixedRouteCase["phase"] = phaseValue;
-    const assets = objectValue(item.assets, `vegetation.route_review.cases[${index}].assets`);
-    const projected = Object.fromEntries(["source", "city", "vegetation", "tgs"].map((key) => {
-      const descriptor = objectValue(assets[key], `vegetation.route_review.assets.${key}`);
-      const path = textValue(descriptor.path, `vegetation.route_review.assets.${key}.path`);
-      const digest = textValue(descriptor.sha256, `vegetation.route_review.assets.${key}.sha256`);
-      if (!SHA256.test(digest) || !path.startsWith(`route-review/${caseId}/`)) {
-        throw new VegetationShadowContractError(`vegetation.route_review.assets.${key}: proof invalid.`);
-      }
-      return [key, `${endpointRoot}/${encodeURIComponent(resultId)}/assets/${path
-        .split("/").map(encodeURIComponent).join("/")}`];
-    })) as Record<"source" | "city" | "vegetation" | "tgs", string>;
-    const tgs = objectValue(item.tgs, `vegetation.route_review.cases[${index}].tgs`);
-    const groundCells = integerValue(tgs.ground_cells, "vegetation.route_review.tgs.ground");
-    const occupiedCells = integerValue(tgs.occupied_cells, "vegetation.route_review.tgs.occupied");
-    const rejectedCells = integerValue(tgs.rejected_cells, "vegetation.route_review.tgs.rejected");
-    const unobservedCells = integerValue(tgs.unobserved_cells, "vegetation.route_review.tgs.unobserved");
-    if (groundCells + occupiedCells + rejectedCells + unobservedCells !== 2244) {
-      throw new VegetationShadowContractError("vegetation.route_review.tgs cell accounting changed.");
-    }
-    return {
-      caseId,
-      phase,
-      sourceSequence: integerValue(item.source_sequence, "vegetation.route_review.source_sequence"),
-      sessionSeconds: numberValue(item.session_seconds, "vegetation.route_review.session_seconds"),
-      assets: projected,
-      tgs: { groundCells, occupiedCells, rejectedCells, unobservedCells },
-    };
-  });
-  if (cases.length !== 10) {
-    throw new VegetationShadowContractError("vegetation.route_review.cases: expected 10 aligned islands.");
-  }
-  return {
-    sourceId: "RAVNOVES004TREE",
-    sessionId: textValue(row.session_id, "vegetation.route_review.session_id"),
-    packId,
-    frameCount: 10,
-    models: {
-      city: {
-        name: textValue(city.name, "vegetation.route_review.models.city.name"),
-        inferenceFps: numberValue(city.inference_fps, "vegetation.route_review.models.city.fps"),
-        endToEndP95Ms: numberValue(city.end_to_end_p95_ms, "vegetation.route_review.models.city.p95"),
-      },
-      vegetation: {
-        name: textValue(vegetation.name, "vegetation.route_review.models.vegetation.name"),
-        latencyP95Ms: numberValue(vegetation.latency_p95_ms, "vegetation.route_review.models.vegetation.p95"),
-      },
-      tgs: {
-        name: textValue(tgsModel.name, "vegetation.route_review.models.tgs.name"),
-        latencyP95Ms: numberValue(tgsModel.latency_p95_ms, "vegetation.route_review.models.tgs.p95"),
-        cellSizeM: numberValue(tgsModel.cell_size_m, "vegetation.route_review.models.tgs.cell"),
-        radiusM: numberValue(tgsModel.radius_m, "vegetation.route_review.models.tgs.radius"),
-      },
-    },
-    cases,
-  };
-}
-
-function fullRouteTaxonomyValue(
-  value: unknown,
-  label: string,
-  schema: string,
-  classCount: number,
-): readonly VegetationVideoSemanticClass[] {
-  const taxonomy = objectValue(value, `${label}.taxonomy`);
-  exact(taxonomy.schema_version, schema, `${label}.taxonomy.schema`);
-  const classes = arrayValue(taxonomy.classes, `${label}.taxonomy.classes`).map(
-    (raw, expectedId): VegetationVideoSemanticClass => {
-      const item = objectValue(raw, `${label}.taxonomy[${expectedId}]`);
-      const classId = integerValue(item.class_id, `${label}.class_id[${expectedId}]`);
-      if (classId !== expectedId) {
-        throw new VegetationShadowContractError(`${label}: taxonomy order changed.`);
-      }
-      const color = arrayValue(item.color_rgb, `${label}.color[${expectedId}]`)
-        .map((channel, index) => integerValue(channel, `${label}.color[${expectedId}][${index}]`));
-      if (color.length !== 3 || color.some((channel) => channel > 255)) {
-        throw new VegetationShadowContractError(`${label}: taxonomy color invalid.`);
-      }
-      const disposition = item.disposition;
-      if (
-        disposition !== "labeled"
-        && disposition !== "ambiguous"
-        && disposition !== "prediction"
-        && disposition !== "undefined"
-      ) {
-        throw new VegetationShadowContractError(`${label}: taxonomy disposition changed.`);
-      }
-      return {
-        classId,
-        label: textValue(item.label, `${label}.label[${expectedId}]`),
-        colorRgb: color as unknown as readonly [number, number, number],
-        disposition,
-        materialClass: item.material_class === null || item.material_class === undefined
-          ? null
-          : textValue(item.material_class, `${label}.material[${expectedId}]`),
-        evidenceState: item.evidence_state === null || item.evidence_state === undefined
-          ? null
-          : textValue(item.evidence_state, `${label}.evidence[${expectedId}]`),
-      };
-    },
-  );
-  if (classes.length !== classCount) {
-    throw new VegetationShadowContractError(`${label}: taxonomy size changed.`);
-  }
-  return classes;
-}
-
-function fullRouteLayerValue(
-  value: unknown,
-  layer: "city" | "vegetation",
-): VegetationFullRouteLayer {
-  const label = `vegetation.route_full_review.layers.${layer}`;
-  const row = objectValue(value, label);
-  const resultId = textValue(row.result_id, `${label}.result_id`);
-  const identity = layer === "city"
-    ? /^result-[a-f0-9]{64}$/
-    : /^lab-v1-ravnoves-video-ddrnet-[a-f0-9]{64}$/;
-  if (!identity.test(resultId)) {
-    throw new VegetationShadowContractError(`${label}: identity invalid.`);
-  }
-  exact(row.frame_count, 6830, `${label}.frame_count`);
-  const archive = objectValue(row.mask_archive, `${label}.mask_archive`);
-  exact(
-    archive.path,
-    layer === "city" ? "video/eomt-semantic-masks.zip" : "video/ddrnet-semantic-masks.zip",
-    `${label}.mask_archive.path`,
-  );
-  const digest = textValue(archive.sha256, `${label}.mask_archive.sha256`);
-  if (!SHA256.test(digest)) {
-    throw new VegetationShadowContractError(`${label}: archive digest invalid.`);
-  }
-  integerValue(archive.byte_length, `${label}.mask_archive.byte_length`);
-  return {
-    name: textValue(row.name, `${label}.name`),
-    resultId,
-    frameCount: 6830,
-    taxonomy: fullRouteTaxonomyValue(
-      row.taxonomy,
-      label,
-      layer === "city"
-        ? "missioncore.recorded-eomt-taxonomy/v1"
-        : "missioncore.lab-v1-vegetation-taxonomy/v1",
-      layer === "city" ? 16 : 64,
-    ),
-    inferenceFps: numberValue(row.inference_fps, `${label}.inference_fps`),
-    latencyP95Ms: numberValue(row.latency_p95_ms, `${label}.latency_p95_ms`),
-    peakReservedVramBytes: integerValue(
-      row.peak_reserved_vram_bytes,
-      `${label}.peak_reserved_vram_bytes`,
-    ),
-  };
-}
-
-function fullRouteReviewValue(value: unknown): VegetationFullRouteReview | null {
-  if (value === null || value === undefined) return null;
-  const row = objectValue(value, "vegetation.route_full_review");
-  exact(row.source_id, "RAVNOVES004TREE", "vegetation.route_full_review.source_id");
-  exact(
-    row.session_id,
-    "20260828T130511Z_viewer_live",
-    "vegetation.route_full_review.session_id",
-  );
-  exact(
-    row.source_job_id,
-    "recorded-camera-eb2783c5480d56bda07c8af0",
-    "vegetation.route_full_review.source_job_id",
-  );
-  exact(row.frame_count, 6830, "vegetation.route_full_review.frame_count");
-  exact(row.width, 800, "vegetation.route_full_review.width");
-  exact(row.height, 600, "vegetation.route_full_review.height");
-  exact(row.ground_truth, false, "vegetation.route_full_review.ground_truth");
-  const sourceJobInputSha256 = textValue(
-    row.source_job_input_sha256,
-    "vegetation.route_full_review.source_job_input_sha256",
-  );
-  const sourceStreamSha256 = textValue(
-    row.source_stream_sha256,
-    "vegetation.route_full_review.source_stream_sha256",
-  );
-  exact(
-    sourceJobInputSha256,
-    "eb2783c5480d56bda07c8af008dff5344d19dc550ef70fe2075d6f098f7cc715",
-    "vegetation.route_full_review.source_job_input_sha256",
-  );
-  exact(
-    sourceStreamSha256,
-    "e5eb017e2cc0f546736eda5235ca157b501913093cb64af5e548e335417e1bac",
-    "vegetation.route_full_review.source_stream_sha256",
-  );
-  exact(
-    row.recorded_media_source_id,
-    "recorded.camera.6a3945242828a038",
-    "vegetation.route_full_review.recorded_media_source_id",
-  );
-  const recordedMediaGenerationSha256 = textValue(
-    row.recorded_media_generation_sha256,
-    "vegetation.route_full_review.recorded_media_generation_sha256",
-  );
-  exact(
-    recordedMediaGenerationSha256,
-    "b073ea1e7babf1c77a664e1a5b95e3702d0e05b0e34c1e85a7c67a6f8b392ded",
-    "vegetation.route_full_review.recorded_media_generation_sha256",
-  );
-  if (
-    !SHA256.test(sourceJobInputSha256)
-    || !SHA256.test(sourceStreamSha256)
-    || !SHA256.test(recordedMediaGenerationSha256)
-  ) {
-    throw new VegetationShadowContractError("vegetation.route_full_review: source digest invalid.");
-  }
-  const timelineStartSeconds = numberValue(
-    row.timeline_start_seconds,
-    "vegetation.route_full_review.timeline_start_seconds",
-  );
-  const timelineEndSeconds = numberValue(
-    row.timeline_end_seconds,
-    "vegetation.route_full_review.timeline_end_seconds",
-  );
-  if (timelineEndSeconds <= timelineStartSeconds) {
-    throw new VegetationShadowContractError("vegetation.route_full_review: timeline invalid.");
-  }
-  const timeline = objectValue(row.timeline, "vegetation.route_full_review.timeline");
-  exact(
-    timeline.path,
-    "video/frame-source-times-ns.bin",
-    "vegetation.route_full_review.timeline.path",
-  );
-  exact(
-    timeline.encoding,
-    "uint64-le-nanoseconds",
-    "vegetation.route_full_review.timeline.encoding",
-  );
-  exact(timeline.frame_count, 6830, "vegetation.route_full_review.timeline.frame_count");
-  const timelineSha256 = textValue(
-    timeline.sha256,
-    "vegetation.route_full_review.timeline.sha256",
-  );
-  if (!SHA256.test(timelineSha256)) {
-    throw new VegetationShadowContractError("vegetation.route_full_review: timeline digest invalid.");
-  }
-  const timelineByteLength = integerValue(
-    timeline.byte_length,
-    "vegetation.route_full_review.timeline.byte_length",
-  );
-  exact(timelineByteLength, 6830 * 8, "vegetation.route_full_review.timeline.byte_length");
-  const decodeRepair = objectValue(
-    row.decode_repair,
-    "vegetation.route_full_review.decode_repair",
-  );
-  exact(decodeRepair.repaired_frame_count, 1, "vegetation.route_full_review.decode_repair.count");
-  exact(decodeRepair.sequence, 6092, "vegetation.route_full_review.decode_repair.sequence");
-  exact(
-    decodeRepair.method,
-    "duplicate-previous-decoded-frame",
-    "vegetation.route_full_review.decode_repair.method",
-  );
-  const repairProofs = objectValue(
-    decodeRepair.proofs,
-    "vegetation.route_full_review.decode_repair.proofs",
-  );
-  for (const [key, expectedPath] of Object.entries({
-    eomt: "proofs/decode_repair.json",
-    ddrnet: "proofs/ddrnet_decode_repair.json",
-  })) {
-    const proof = objectValue(
-      repairProofs[key],
-      `vegetation.route_full_review.decode_repair.proofs.${key}`,
-    );
-    exact(
-      proof.path,
-      expectedPath,
-      `vegetation.route_full_review.decode_repair.proofs.${key}.path`,
-    );
-    const digest = textValue(
-      proof.sha256,
-      `vegetation.route_full_review.decode_repair.proofs.${key}.sha256`,
-    );
-    if (!SHA256.test(digest)) {
-      throw new VegetationShadowContractError("vegetation.route_full_review: repair proof invalid.");
-    }
-  }
-  const layers = objectValue(row.layers, "vegetation.route_full_review.layers");
-  return {
-    sourceId: "RAVNOVES004TREE",
-    sessionId: "20260828T130511Z_viewer_live",
-    sourceJobId: "recorded-camera-eb2783c5480d56bda07c8af0",
-    sourceJobInputSha256,
-    sourceStreamSha256,
-    recordedMediaSourceId: "recorded.camera.6a3945242828a038",
-    recordedMediaGenerationSha256,
-    frameCount: 6830,
-    width: 800,
-    height: 600,
-    timelineStartSeconds,
-    timelineEndSeconds,
-    timelineArtifact: { sha256: timelineSha256, byteLength: timelineByteLength },
-    frameSourceTimesNs: [],
-    decodeRepair: {
-      repairedFrameCount: 1,
-      sequence: 6092,
-      method: "duplicate-previous-decoded-frame",
-    },
-    city: fullRouteLayerValue(layers.city, "city"),
-    vegetation: fullRouteLayerValue(layers.vegetation, "vegetation"),
-  };
-}
-
-function parseResult(
-  value: unknown,
-  resultId: string,
-  endpointRoot: string,
-): VegetationShadowResult {
+function parseResult(value: unknown, resultId: string): VegetationShadowResult {
   const payload = objectValue(value, "Vegetation LAB");
   exact(payload.schema_version, "missioncore.lab-v1-vegetation-shadow/v1", "vegetation.schema");
   exact(payload.result_id, resultId, "vegetation.result_id");
@@ -872,15 +376,10 @@ function parseResult(
     "vegetation.authority.camera_semantics_can_clear_rigid_geometry",
   );
   const routeCases = arrayValue(catalogs.ravnoves, "vegetation.catalogs.ravnoves")
-    .map((item) => visualCaseValue(item, resultId, "ravnoves", endpointRoot));
+    .map((item) => visualCaseValue(item, resultId, "ravnoves"));
   const validationCases = arrayValue(catalogs.goose, "vegetation.catalogs.goose")
-    .map((item) => visualCaseValue(item, resultId, "goose", endpointRoot));
-  const routeReview = mixedRouteReviewValue(payload.route_review, resultId, endpointRoot);
-  const routeFullReview = fullRouteReviewValue(payload.route_full_review);
-  if (
-    routeCases.length !== 0
-    || (routeReview || routeFullReview ? validationCases.length !== 0 : validationCases.length !== 12)
-  ) {
+    .map((item) => visualCaseValue(item, resultId, "goose"));
+  if (routeCases.length !== 0 || validationCases.length !== 12) {
     throw new VegetationShadowContractError("vegetation.catalogs: ожидалось 12 truth-backed GOOSE случаев без route viewer.");
   }
   return {
@@ -892,8 +391,6 @@ function parseResult(
     routeCases,
     validationCases,
     routeVideo: routeVideoValue(payload.route_video),
-    routeReview,
-    routeFullReview,
     limitations: arrayValue(payload.limitations, "vegetation.limitations")
       .map((item, index) => textValue(item, `vegetation.limitations[${index}]`)),
     visualShadowReady: true,
@@ -914,23 +411,6 @@ export function vegetationVideoMaskUrl(resultId: string, sequence: number): stri
   return `/api/v1/laboratory/vegetation-shadow/${encodeURIComponent(resultId)}/masks/${sequence}`;
 }
 
-export function vegetationFullRouteMaskUrl(
-  resultId: string,
-  layer: "city" | "vegetation",
-  sequence: number,
-): string {
-  if (
-    !RESULT_ID.test(resultId)
-    || (layer !== "city" && layer !== "vegetation")
-    || !Number.isInteger(sequence)
-    || sequence < 0
-    || sequence >= 6830
-  ) {
-    throw new VegetationShadowContractError("Vegetation full-route mask identity недопустима.");
-  }
-  return `/api/v1/laboratory/vegetation-shadow/${encodeURIComponent(resultId)}/route-masks/${layer}/${sequence}`;
-}
-
 export async function fetchVegetationShadowResult(
   resultId: string,
   {
@@ -948,74 +428,5 @@ export async function fetchVegetationShadowResult(
   if (!response.ok) {
     throw new VegetationShadowContractError(`Vegetation LAB недоступна: HTTP ${response.status}.`);
   }
-  const result = parseResult(
-    await response.json(),
-    resultId,
-    "/api/v1/laboratory/vegetation-shadow",
-  );
-  if (!result.routeFullReview) return result;
-  const timelineResponse = await fetcher(
-    `/api/v1/laboratory/vegetation-shadow/${encodeURIComponent(resultId)}/route-timeline`,
-    { method: "GET", headers: { Accept: "application/octet-stream" }, signal },
-  );
-  if (!timelineResponse.ok) {
-    throw new VegetationShadowContractError(
-      `Vegetation LAB timeline недоступна: HTTP ${timelineResponse.status}.`,
-    );
-  }
-  if (
-    timelineResponse.headers.get("etag")
-    !== `"${result.routeFullReview.timelineArtifact.sha256}"`
-  ) {
-    throw new VegetationShadowContractError("Vegetation LAB timeline digest изменён.");
-  }
-  const timelinePayload = await timelineResponse.arrayBuffer();
-  if (timelinePayload.byteLength !== result.routeFullReview.timelineArtifact.byteLength) {
-    throw new VegetationShadowContractError("Vegetation LAB timeline size изменён.");
-  }
-  const timelineView = new DataView(timelinePayload);
-  const frameSourceTimesNs = Array.from({ length: result.routeFullReview.frameCount }, (_, index) => {
-    const value = Number(timelineView.getBigUint64(index * 8, true));
-    if (!Number.isSafeInteger(value)) {
-      throw new VegetationShadowContractError("Vegetation LAB timeline содержит unsafe time.");
-    }
-    return value;
-  });
-  if (
-    frameSourceTimesNs[0] !== Math.round(result.routeFullReview.timelineStartSeconds * 1_000_000_000)
-    || frameSourceTimesNs.some((time, index) => index > 0 && time <= frameSourceTimesNs[index - 1]!)
-  ) {
-    throw new VegetationShadowContractError("Vegetation LAB timeline нарушена.");
-  }
-  return {
-    ...result,
-    routeFullReview: { ...result.routeFullReview, frameSourceTimesNs },
-  };
-}
-
-export async function fetchVegetationBenchmarkResult(
-  resultId: string,
-  {
-    fetcher = fetch,
-    signal,
-  }: { fetcher?: LaboratoryFetch; signal?: AbortSignal } = {},
-): Promise<VegetationShadowResult> {
-  if (!BENCHMARK_RESULT_ID.test(resultId)) {
-    throw new VegetationShadowContractError("Vegetation benchmark identity недопустима.");
-  }
-  const endpointRoot = "/api/v1/laboratory/vegetation-benchmark";
-  const response = await fetcher(
-    `${endpointRoot}/${encodeURIComponent(resultId)}`,
-    { method: "GET", headers: { Accept: "application/json" }, signal },
-  );
-  if (!response.ok) {
-    throw new VegetationShadowContractError(
-      `Vegetation benchmark недоступен: HTTP ${response.status}.`,
-    );
-  }
-  const result = parseResult(await response.json(), resultId, endpointRoot);
-  if (result.routeVideo) {
-    throw new VegetationShadowContractError("Vegetation benchmark содержит route video.");
-  }
-  return result;
+  return parseResult(await response.json(), resultId);
 }

apps/control-station/src/styles/m4-replay-threat.css

@@ -81,21 +81,6 @@
   justify-content: flex-end;
 }
 
-.m4-replay-threat-visual__pane-toolbar[data-pane-toolbar="media"][data-multi-semantic="true"] {
-  flex-wrap: wrap;
-}
-
-.m4-replay-threat-visual__pane-toolbar[data-pane-toolbar="media"][data-multi-semantic="true"]
-  > .m4-replay-threat-visual__pane-layer-controls {
-  flex: 1 0 100%;
-  justify-content: flex-start;
-}
-
-.m4-replay-threat-visual__pane-toolbar[data-pane-toolbar="media"][data-multi-semantic="true"]
-  > .m4-replay-threat-visual__pane-mode-controls {
-  margin-left: auto;
-}
-
 .m4-replay-threat-evidence-viewer[data-mode-controls="content"]:has(
   .m4-replay-threat-visual__review-controls
 ) .m4-replay-threat-visual__pane-toolbar[data-pane-toolbar="media"] {

apps/control-station/src/workspaces/laboratory/AdvancedLaboratoryResult.tsx

@@ -51,7 +51,6 @@ import { M48TRiskQualityResultView } from "./M48TRiskQualityResult";
 import { M49TgsFailClosedResultView } from "./M49TgsFailClosedResult";
 import { M49TgsFullShadowResultView } from "./M49TgsFullShadowResult";
 import { VegetationShadowResultView } from "./VegetationShadowResult";
-import { VegetationBenchmarkResultView } from "./VegetationBenchmarkResult";
 
 export { isAdvancedLaboratoryWorkId };
 export type { AdvancedLaboratoryWorkId };
@@ -94,9 +93,6 @@ export function AdvancedLaboratoryResult({
   failedSessionId: string | null;
   replayError: string | null;
 }) {
-  if (workId === "lab-v1-vegetation-benchmark" && results.vegetationBenchmark) {
-    return <VegetationBenchmarkResultView rigLabel={rigLabel} result={results.vegetationBenchmark} />;
-  }
   if (workId === "lab-v1-vegetation-shadow" && results.vegetationShadow) {
     return <VegetationShadowResultView rigLabel={rigLabel} result={results.vegetationShadow} />;
   }

apps/control-station/src/workspaces/laboratory/M49TgsFullShadowEvidence.tsx

@@ -22,7 +22,6 @@ import {
 import {
   M4ReplayThreatVisual,
   type M4ReplayClassifiedSpatialFrame,
-  type M4ReplayThreatSemanticLayer,
 } from "./M4ReplayThreatVisual";
 
 const CLASSES: readonly RecordedEvidenceSemanticClass[] = [
@@ -43,15 +42,7 @@ function message(error: unknown): string {
     : "Полный TGS spatial frame недоступен.";
 }
 
-export function M49TgsFullShadowEvidence({
-  result,
-  semanticOverride,
-  evidenceLabel = "M49 · full TGS shadow",
-}: {
-  result: M49TgsFullShadowResult;
-  semanticOverride?: M4ReplayThreatSemanticLayer;
-  evidenceLabel?: string;
-}) {
+export function M49TgsFullShadowEvidence({ result }: { result: M49TgsFullShadowResult }) {
   const [activeSequence, setActiveSequence] = useState<number | null>(null);
   const [semantic, setSemantic] = useState<E47SemanticSlamResult | null>(null);
   const [semanticError, setSemanticError] = useState<string | null>(null);
@@ -200,31 +191,18 @@ export function M49TgsFullShadowEvidence({
   const handleSequenceChange = useCallback((sequence: number | null) => {
     setActiveSequence(sequence);
   }, []);
-  const semanticLayers = useMemo<readonly M4ReplayThreatSemanticLayer[]>(() => [
-    ...(semantic ? [{
-      id: "urban",
-      controlLabel: "ГОРОД · EoMT",
-      resultId: semantic.resultId,
-      taxonomy: semantic.taxonomy,
-      label: "EoMT Cityscapes semantic · recorded video",
-      maskAriaLabel: "EoMT urban semantic prediction",
-    }] : []),
-    ...(semanticOverride ? [{
-      ...semanticOverride,
-      id: semanticOverride.id ?? "vegetation",
-      controlLabel: semanticOverride.controlLabel ?? "ПРИРОДА · DDRNet",
-    }] : []),
-  ], [semantic, semanticOverride]);
 
   return (
     <>
       <M4ReplayThreatVisual
         resultId={result.source.linkedVisualResultId}
-        semanticLayers={semanticLayers}
-        initialSemanticLayerId={semanticOverride ? "vegetation" : "urban"}
+        semantic={semantic ? {
+          resultId: semantic.resultId,
+          taxonomy: semantic.taxonomy,
+        } : undefined}
         showReviewAnchorBoxes={false}
         reviewLabel="4 489 source-paced TGS frames"
-        evidenceLabel={evidenceLabel}
+        evidenceLabel="M49 · full TGS shadow"
         initialSpatialMode="3d"
         onActiveSequenceChange={handleSequenceChange}
         classifiedSpatialLayer={{

apps/control-station/src/workspaces/laboratory/M4ReplayThreatVisual.tsx

@@ -96,8 +96,6 @@ function SpatialState({ message: text }: { message: string }) {
 }
 
 export interface M4ReplayThreatSemanticLayer {
-  id?: string;
-  controlLabel?: string;
   resultId: string;
   spatialResultId?: string | null;
   maskUrl?: (sequence: number) => string;
@@ -157,8 +155,6 @@ const EMPTY_REVIEW_ANCHORS: readonly M4ReplayThreatReviewAnchor[] = [];
 export function M4ReplayThreatVisual({
   resultId,
   semantic,
-  semanticLayers,
-  initialSemanticLayerId,
   reviewAnchors = EMPTY_REVIEW_ANCHORS,
   showReviewAnchorBoxes = true,
   reviewLabel = "Контрольные примеры M4.8R1",
@@ -172,8 +168,6 @@ export function M4ReplayThreatVisual({
 }: {
   resultId: string;
   semantic?: M4ReplayThreatSemanticLayer;
-  semanticLayers?: readonly M4ReplayThreatSemanticLayer[];
-  initialSemanticLayerId?: string;
   reviewAnchors?: readonly M4ReplayThreatReviewAnchor[];
   showReviewAnchorBoxes?: boolean;
   reviewLabel?: string;
@@ -205,35 +199,6 @@ export function M4ReplayThreatVisual({
   ));
   const [expanded, setExpanded] = useState(false);
   const [selectedReviewAnchorIndex, setSelectedReviewAnchorIndex] = useState(0);
-  const availableSemanticLayers = useMemo<readonly M4ReplayThreatSemanticLayer[]>(
-    () => semanticLayers?.length ? semanticLayers : semantic ? [semantic] : [],
-    [semantic, semanticLayers],
-  );
-  const semanticLayerIdentity = availableSemanticLayers
-    .map((layer, index) => layer.id ?? `${layer.resultId}:${index}`)
-    .join("|");
-  const [selectedSemanticLayerId, setSelectedSemanticLayerId] = useState(
-    initialSemanticLayerId ?? "",
-  );
-  useEffect(() => {
-    if (!availableSemanticLayers.length) {
-      setSelectedSemanticLayerId("");
-      return;
-    }
-    const selectedStillExists = availableSemanticLayers.some(
-      (layer, index) => (layer.id ?? `${layer.resultId}:${index}`) === selectedSemanticLayerId,
-    );
-    if (selectedStillExists) return;
-    const preferred = initialSemanticLayerId
-      ? availableSemanticLayers.find((layer) => layer.id === initialSemanticLayerId)
-      : null;
-    const next = preferred ?? availableSemanticLayers[0]!;
-    const nextIndex = availableSemanticLayers.indexOf(next);
-    setSelectedSemanticLayerId(next.id ?? `${next.resultId}:${nextIndex}`);
-  }, [availableSemanticLayers, initialSemanticLayerId, semanticLayerIdentity, selectedSemanticLayerId]);
-  const activeSemantic = availableSemanticLayers.find(
-    (layer, index) => (layer.id ?? `${layer.resultId}:${index}`) === selectedSemanticLayerId,
-  ) ?? availableSemanticLayers[0];
   const metricSceneRef = useRef<LaboratoryMetricEvidenceSceneHandle | null>(null);
   const metadata = useM4ThreatTimelineMetadata(resultId, timelineEndpointRoot);
   const playbackRange = useMemo(() => metadata.timeline ? ({
@@ -333,21 +298,19 @@ export function M4ReplayThreatVisual({
     sequence: frame?.sequence ?? null,
     endpointRoot: timelineEndpointRoot,
   });
-  const semanticSpatialResultId = activeSemantic
-    ? activeSemantic.spatialResultId === undefined
-      ? activeSemantic.resultId
-      : activeSemantic.spatialResultId
+  const semanticSpatialResultId = semantic
+    ? semantic.spatialResultId === undefined ? semantic.resultId : semantic.spatialResultId
     : null;
   const spatialSemanticTaxonomy = useMemo<readonly E47SemanticClass[]>(
-    () => semanticSpatialResultId && activeSemantic
-      ? activeSemantic.taxonomy.map((item) => ({
+    () => semanticSpatialResultId && semantic
+      ? semantic.taxonomy.map((item) => ({
           classId: item.classId,
           label: item.label,
           disposition: item.disposition === "ambiguous" ? "ambiguous" : "labeled",
           colorRgb: item.colorRgb,
         }))
       : [],
-    [activeSemantic, semanticSpatialResultId],
+    [semantic, semanticSpatialResultId],
   );
   const semanticTimeline = useE47SemanticTimelineFrame({
     resultId: semanticSpatialResultId,
@@ -415,22 +378,22 @@ export function M4ReplayThreatVisual({
     );
   }, [frame, metadata.timeline, showReferenceMediaLayers, showStaticObstacles]);
   const activeBoxes = useMemo(
-    () => !showReferenceMediaLayers ? [] : [
+    () => classifiedSpatialLayer || !showReferenceMediaLayers ? [] : [
       ...boxes(frame?.cameraProposals ?? []),
       ...staticObstacleBoxes,
       ...reviewAnchorBoxes,
     ],
-    [frame, reviewAnchorBoxes, showReferenceMediaLayers, staticObstacleBoxes],
+    [classifiedSpatialLayer, frame, reviewAnchorBoxes, showReferenceMediaLayers, staticObstacleBoxes],
   );
   const semanticClasses = useMemo<readonly RecordedEvidenceSemanticClass[]>(
-    () => activeSemantic?.taxonomy.map((item) => ({
+    () => semantic?.taxonomy.map((item) => ({
       id: item.classId,
       label: `semantic: ${item.label}`,
     })) ?? [],
-    [activeSemantic?.taxonomy],
+    [semantic?.taxonomy],
   );
   const semanticPalette = useMemo<readonly RecordedEvidenceSemanticPaletteEntry[]>(
-    () => activeSemantic?.taxonomy.map((item) => ({
+    () => semantic?.taxonomy.map((item) => ({
       classId: item.classId,
       color: item.disposition === "undefined"
         ? { kind: "transparent" as const }
@@ -441,7 +404,7 @@ export function M4ReplayThreatVisual({
         ? 0
         : item.disposition === "ambiguous" ? 0.52 : 0.92,
     })) ?? [],
-    [activeSemantic?.taxonomy],
+    [semantic?.taxonomy],
   );
   const semanticFrame = semanticTimeline.activeFrame?.sequence === frame?.sequence
     ? semanticTimeline.activeFrame
@@ -459,7 +422,7 @@ export function M4ReplayThreatVisual({
       && lastSpatialSemanticFrameRef.current.frame.sequence === spatialFrame?.sequence
       ? lastSpatialSemanticFrameRef.current.frame
       : null;
-  const semanticIntegrityError = activeSemantic && spatialFrame && spatialSemanticFrame && (
+  const semanticIntegrityError = semantic && spatialFrame && spatialSemanticFrame && (
     spatialSemanticFrame.sourcePointCount !== spatialFrame.pointCloudSourceCount
     || spatialFrame.pointCloudSampleCount !== spatialFrame.pointCloudSourceCount
     || spatialFrame.pointCloudBodyXyzM.length !== spatialFrame.pointCloudSourceCount
@@ -468,7 +431,7 @@ export function M4ReplayThreatVisual({
     : null;
   const alignedSemanticPointIds = useMemo<readonly (number | null)[] | undefined>(() => {
     if (
-      !activeSemantic
+      !semantic
       || !showSpatialSemantic
       || !spatialFrame
       || !spatialSemanticFrame
@@ -478,7 +441,7 @@ export function M4ReplayThreatVisual({
       const status = spatialSemanticFrame.statusCodes[index];
       return status === 2 || status === 3 ? classId : null;
     });
-  }, [activeSemantic, semanticIntegrityError, showSpatialSemantic, spatialFrame, spatialSemanticFrame]);
+  }, [semantic, semanticIntegrityError, showSpatialSemantic, spatialFrame, spatialSemanticFrame]);
   const activeSpatialFrame = spatialFrame?.sequence === timelineFrame.activeSequence
     ? spatialFrame
     : null;
@@ -647,19 +610,19 @@ export function M4ReplayThreatVisual({
     },
   ), [metadata.timeline, spatialFrame, timelineFrame.availableFrames]);
   const semanticOverlay: RecordedEvidenceSemanticOverlay | undefined =
-    activeSemantic && showMediaSemantic && frame
+    semantic && showMediaSemantic && frame
       ? {
-          src: activeSemantic.maskUrl?.(frame.sequence)
-            ?? e47SemanticMaskUrl(activeSemantic.resultId, frame.sequence),
+          src: semantic.maskUrl?.(frame.sequence)
+            ?? e47SemanticMaskUrl(semantic.resultId, frame.sequence),
           prefetchSrcs: Array.from({ length: 12 }, (_, index) => index + 1)
             .map((offset) => frame.sequence + offset)
             .filter((sequence) => sequence < (metadata.timeline?.frameCount ?? 0))
-            .map((sequence) => activeSemantic.maskUrl?.(sequence)
-              ?? e47SemanticMaskUrl(activeSemantic.resultId, sequence)),
+            .map((sequence) => semantic.maskUrl?.(sequence)
+              ?? e47SemanticMaskUrl(semantic.resultId, sequence)),
           classes: semanticClasses,
           palette: semanticPalette,
           opacity: 0.9,
-          ariaLabel: `${activeSemantic.maskAriaLabel ?? "Semantic prediction"} frame ${frame.sequence + 1}`,
+          ariaLabel: `${semantic.maskAriaLabel ?? "Semantic prediction"} frame ${frame.sequence + 1}`,
         }
       : undefined;
   const accumulatedCameraPoints = cameraPointOverlay.overlay?.sequence === frame?.sequence
@@ -729,7 +692,7 @@ export function M4ReplayThreatVisual({
     </div>
   );
 
-  const mediaLayerControls = activeSemantic
+  const mediaLayerControls = semantic
     || (showReferenceMediaLayers && metadata.timeline?.cameraPointDelivery)
     || (showReferenceMediaLayers && metadata.timeline?.cameraObstacleProjectionDelivery) ? (
     <div
@@ -737,7 +700,7 @@ export function M4ReplayThreatVisual({
       role="group"
       aria-label="Слои камеры и видео"
     >
-      {activeSemantic ? (
+      {semantic ? (
         <Button
           size="compact"
           shape="pill"
@@ -748,20 +711,6 @@ export function M4ReplayThreatVisual({
           SEMANTICS
         </Button>
       ) : null}
-      {availableSemanticLayers.length > 1 ? (
-        <SegmentedControl
-          value={selectedSemanticLayerId}
-          items={availableSemanticLayers.map((layer, index) => ({
-            value: layer.id ?? `${layer.resultId}:${index}`,
-            label: layer.controlLabel ?? layer.label ?? `SEMANTIC ${index + 1}`,
-          }))}
-          label="Источник семантики"
-          onChange={(value) => {
-            setSelectedSemanticLayerId(value);
-            setShowMediaSemantic(true);
-          }}
-        />
-      ) : null}
       {showReferenceMediaLayers && metadata.timeline?.cameraPointDelivery ? (
         <Button
           size="compact"
@@ -1073,7 +1022,6 @@ export function M4ReplayThreatVisual({
           <div
             className="m4-replay-threat-visual__pane-toolbar"
             data-pane-toolbar="media"
-            data-multi-semantic={availableSemanticLayers.length > 1 ? "true" : undefined}
           >
             {mediaLayerControls}
             {mediaModeControls}
@@ -1290,8 +1238,8 @@ export function M4ReplayThreatVisual({
   return (
     <div className="l3-visual-audit m4-replay-threat-visual">
       <LaboratoryEvidenceViewer
-        label={activeSemantic
-          ? activeSemantic.label ?? "Semantic diagnostic replay"
+        label={semantic
+          ? semantic.label ?? "Semantic diagnostic replay"
           : `${evidenceLabel} recorded-realtime replay`}
         className="m4-replay-threat-evidence-viewer"
         mode={mediaMode ?? "none"}

apps/control-station/src/workspaces/laboratory/VegetationBenchmarkResult.tsx

@@ -1,147 +0,0 @@
-import {
-  LaboratoryEvidence,
-  LaboratoryResultSummary,
-  LaboratorySummary,
-  LaboratoryWorkTemplate,
-} from "../../components/laboratory/LaboratoryPresentation";
-import type { VegetationShadowResult } from "../../core/laboratory/vegetationShadow";
-import {
-  M48MaskComparisonVisual,
-  type M48MaskComparisonCase,
-} from "./M48FailureAtlasVisual";
-
-function decimal(value: number, digits = 1): string {
-  return value.toLocaleString("ru-RU", { maximumFractionDigits: digits });
-}
-
-const VEGETATION_LABELS: Readonly<Record<string, string>> = {
-  high_grass: "Высокая трава",
-  low_grass: "Низкая трава",
-  bush: "Куст",
-  tree_trunk: "Ствол дерева",
-  tree_crown: "Крона дерева",
-  hedge: "Живая изгородь",
-  forest: "Лесная растительность",
-  crops: "Посевы",
-};
-
-function comparisonCases(result: VegetationShadowResult): readonly M48MaskComparisonCase[] {
-  return result.validationCases.map((item) => {
-    const focus = item.focus!;
-    return {
-      caseId: item.caseId,
-      title: `${VEGETATION_LABELS[focus.className] ?? focus.className} · truth ${decimal(focus.truthFraction * 100, 1)}% кадра`,
-      sourceUrl: item.assets.source,
-      truthUrl: item.assets.truth,
-      predictions: {
-        ddrnet: item.assets.ddrnet,
-        ppliteseg: item.assets.ppliteseg,
-      },
-      errors: {
-        ddrnet: item.assets.ddrnet_error,
-        ppliteseg: item.assets.ppliteseg_error,
-      },
-    };
-  });
-}
-
-export function VegetationBenchmarkResultView({
-  rigLabel,
-  result,
-}: {
-  rigLabel: string;
-  result: VegetationShadowResult;
-}) {
-  const selected = result.candidates.find(
-    (candidate) => candidate.candidate === result.selectedCandidate,
-  )!;
-  const alternative = result.candidates.find(
-    (candidate) => candidate.candidate !== result.selectedCandidate,
-  )!;
-
-  return (
-    <LaboratoryWorkTemplate
-      summary={(
-        <LaboratorySummary
-          title="M4.8 · архивный benchmark растительности"
-          description="Отдельный truth-backed контур GOOSE для сравнения готовых fine-64 весов. Он не является частью RAVNOVES00 realtime LAB и открывается автономно без Worker 006."
-          status="ARCHIVE ANALYSIS · model qualification only · commands OFF"
-          statusTone="warning"
-          facts={[
-            { label: "Источник", value: "GOOSE validation · 962 размеченных кадра · 12 hard cases" },
-            { label: "Сравнение", value: "DDRNet-39 vs PPLiteSeg · official fine-64 weights" },
-            { label: "Кейсы", value: "трава · куст · ствол · крона · изгородь · лес · посевы" },
-            { label: "Authority", value: `${rigLabel} · MODEL QUALIFICATION ONLY · commands OFF` },
-          ]}
-          brief={{
-            question: "Какие готовые веса лучше различают проезжаемую траву, кусты и стволы на размеченных off-road кадрах?",
-            approach: "Обе модели прогнаны на 962 кадрах, а 12 визуальных кейсов выбраны детерминированно по truth-поддержке восьми растительных классов. Viewer показывает source, ручной truth, prediction и error.",
-            principalResult: `${selected.loadedModelName} лидирует по vegetation IoU: ${decimal(selected.vegetationMeanIouPercent, 2)}% против ${decimal(alternative.vegetationMeanIouPercent, 2)}%.`,
-            limitation: "GOOSE — внешний размеченный домен. Результат выбирает стартовые веса, но не доказывает качество на fisheye RAVNOVES00 и не даёт navigation authority.",
-          }}
-          method={{
-            completeness: "complete",
-            executionClass: "ai-inference",
-            pipelineId: "goose-fine64-ready-weights-benchmark-archive/v1",
-            components: result.candidates.map((candidate) => ({
-              kind: "model" as const,
-              name: candidate.loadedModelName,
-              version: candidate.candidate,
-              role: candidate.candidate === result.selectedCandidate
-                ? "selected vegetation candidate"
-                : "comparison candidate",
-              identitySha256: candidate.checkpointSha256,
-            })),
-          }}
-        />
-      )}
-      evidence={(
-        <LaboratoryEvidence
-          eyebrow="M4.8 · GOOSE VEGETATION HARD CASES"
-          title="TRUTH — ручная разметка · PREDICTION — ответ модели · ERROR — расхождение"
-          kind="diagnostic-model"
-          resizable
-        >
-          <M48MaskComparisonVisual
-            cases={comparisonCases(result)}
-            initialCandidate={result.selectedCandidate}
-          />
-        </LaboratoryEvidence>
-      )}
-      result={(
-        <LaboratoryResultSummary
-          title="DDRNet выбран как стартовый vegetation candidate"
-          status={`${selected.loadedModelName} · перенос на ровер не доказан`}
-          statusTone="warning"
-          metrics={[
-            {
-              label: "GOOSE mIoU",
-              value: `${decimal(selected.meanIouPercent, 2)}% / ${decimal(alternative.meanIouPercent, 2)}%`,
-              hint: `${selected.candidate} / ${alternative.candidate} · полный validation split`,
-            },
-            {
-              label: "Vegetation IoU",
-              value: `${decimal(selected.vegetationMeanIouPercent, 2)}% / ${decimal(alternative.vegetationMeanIouPercent, 2)}%`,
-              hint: "grass/vegetation/bush/tree и родственные fine-64 labels",
-            },
-            {
-              label: "Worker shadow p95",
-              value: `${decimal(selected.shadowLatencyP95Ms, 2)} / ${decimal(alternative.shadowLatencyP95Ms, 2)} ms`,
-              hint: "чистый inference · одна тяжёлая модель за раз",
-            },
-            {
-              label: "Peak VRAM",
-              value: `${decimal(selected.peakReservedVramBytes / 1024 ** 3, 2)} / ${decimal(alternative.peakReservedVramBytes / 1024 ** 3, 2)} GiB`,
-              hint: `${selected.candidate} / ${alternative.candidate} · RTX 4090`,
-            },
-          ]}
-          conclusion={{
-            proved: "Обе готовые fine-64 модели воспроизводимо запускаются; DDRNet лучше по aggregate vegetation IoU.",
-            notProved: "Не доказаны accuracy на нашем fisheye, temporal stability, collision safety и физическое поведение ровера.",
-            decision: "Хранить как архив квалификации весов. Проверку на RAVNOVES00 вести только в основной многослойной LAB.",
-          }}
-        />
-      )}
-    />
-  );
-}

apps/control-station/src/workspaces/laboratory/VegetationShadowResult.tsx

@@ -1,8 +1,3 @@
-import { useEffect, useMemo, useState } from "react";
-import { Icon, IconButton, StatusBadge } from "@nodedc/ui-react";
-
-import { LaboratoryEvidenceViewer } from "../../components/laboratory/LaboratoryEvidenceViewer";
-import { LaboratoryRecordedClipPlayer } from "../../components/laboratory/LaboratoryRecordedClipPlayer";
 import {
   LaboratoryEvidence,
   LaboratoryResultSummary,
@@ -10,436 +5,48 @@ import {
   LaboratoryWorkTemplate,
 } from "../../components/laboratory/LaboratoryPresentation";
 import {
-  RecordedEvidenceSemanticMaskOverlay,
-  type RecordedEvidenceSemanticClass,
-  type RecordedEvidenceSemanticPaletteEntry,
-} from "../../components/laboratory/RecordedEvidenceSemanticMaskOverlay";
-import {
-  vegetationFullRouteMaskUrl,
   vegetationVideoMaskUrl,
-  type VegetationFullRouteLayer,
-  type VegetationFullRouteReview,
-  type VegetationMixedRouteReview,
   type VegetationShadowResult,
 } from "../../core/laboratory/vegetationShadow";
-import { recordedObservationSources } from "../../core/observation/recordedObservationSources";
-import { resolveObservationSessionReplay } from "../../core/observation/useObservationSessions";
-import type { ObservationSourceDescriptor } from "../../core/runtime/contracts";
 import {
-  fetchM49TgsFullShadowResult,
-  type M49TgsFullShadowResult,
-} from "../../core/laboratory/m49TgsFullShadow";
+  M48MaskComparisonVisual,
+  type M48MaskComparisonCase,
+} from "./M48FailureAtlasVisual";
 import { M4ReplayThreatVisual } from "./M4ReplayThreatVisual";
-import { M49TgsFullShadowEvidence } from "./M49TgsFullShadowEvidence";
 
 function decimal(value: number, digits = 1): string {
   return value.toLocaleString("ru-RU", { maximumFractionDigits: digits });
 }
 
-const MIXED_ROUTE_MODES = [
-  { value: "source", label: "SOURCE" },
-  { value: "city", label: "ГОРОД · EoMT" },
-  { value: "vegetation", label: "ПРИРОДА · DDRNet" },
-  { value: "tgs", label: "TGS" },
-] as const;
+const VEGETATION_LABELS: Readonly<Record<string, string>> = {
+  high_grass: "Высокая трава",
+  low_grass: "Низкая трава",
+  bush: "Куст",
+  tree_trunk: "Ствол дерева",
+  tree_crown: "Крона дерева",
+  hedge: "Живая изгородь",
+  forest: "Лесная растительность",
+  crops: "Посевы",
+};
 
-const FULL_ROUTE_MODES = [
-  { value: "source", label: "SOURCE" },
-  { value: "city", label: "ГОРОД · EoMT" },
-  { value: "vegetation", label: "ПРИРОДА · DDRNet" },
-] as const;
-
-function semanticPresentation(layer: VegetationFullRouteLayer): {
-  classes: readonly RecordedEvidenceSemanticClass[];
-  palette: readonly RecordedEvidenceSemanticPaletteEntry[];
-} {
-  return {
-    classes: layer.taxonomy.map((item) => ({ id: item.classId, label: item.label })),
-    palette: layer.taxonomy.map((item) => ({
-      classId: item.classId,
-      color: item.classId === 0
-        ? { kind: "transparent" as const }
-        : { kind: "diagnostic" as const, rgb: item.colorRgb },
-    })),
-  };
-}
-
-function FullRouteReviewEvidence({
-  resultId,
-  review,
-}: {
-  resultId: string;
-  review: VegetationFullRouteReview;
-}) {
-  const [sequence, setSequence] = useState(1);
-  const [playing, setPlaying] = useState(false);
-  const [playbackRate, setPlaybackRate] = useState(1);
-  const [mode, setMode] = useState<typeof FULL_ROUTE_MODES[number]["value"]>("vegetation");
-  const [expanded, setExpanded] = useState(false);
-  const [videoSource, setVideoSource] = useState<ObservationSourceDescriptor | null>(null);
-  const [videoError, setVideoError] = useState<string | null>(null);
-  const frames = useMemo(
-    () => review.frameSourceTimesNs.map((sourceTimeNs, index) => ({
-      sequence: index + 1,
-      sourceTimeNs,
-    })),
-    [review.frameSourceTimesNs],
-  );
-  const layer = mode === "source" ? null : review[mode];
-  const semantic = useMemo(() => layer ? semanticPresentation(layer) : null, [layer]);
-  const maskSequence = sequence - 1;
-  const prefetchSrcs = useMemo(() => layer
-    ? Array.from({ length: 8 }, (_, offset) => maskSequence + offset + 1)
-      .filter((candidate) => candidate < review.frameCount)
-      .map((candidate) => vegetationFullRouteMaskUrl(resultId, mode as "city" | "vegetation", candidate))
-    : [], [layer, maskSequence, mode, resultId, review.frameCount]);
-
-  useEffect(() => {
-    const controller = new AbortController();
-    setVideoSource(null);
-    setVideoError(null);
-    void resolveObservationSessionReplay(review.sessionId, { signal: controller.signal })
-      .then((launch) => {
-        const source = recordedObservationSources(launch).find((candidate) => (
-          candidate.id === review.recordedMediaSourceId
-          && candidate.modality === "video"
-          && candidate.semanticChannelId === "camera.video.recorded"
-          && candidate.delivery?.kind === "recorded-fmp4-manifest"
-          && candidate.delivery.manifestGenerationSha256 === review.recordedMediaGenerationSha256
-          && candidate.delivery.timelineStartSeconds === review.timelineStartSeconds
-          && candidate.delivery.timelineEndSeconds >= review.timelineEndSeconds
-        ));
-        if (!source) {
-          throw new Error("RIGHT-видео не совпало с sealed RAVNOVES004TREE timeline.");
-        }
-        if (!controller.signal.aborted) setVideoSource(source);
-      })
-      .catch((caught: unknown) => {
-        if (!controller.signal.aborted) {
-          setVideoError(caught instanceof Error ? caught.message : "Записанное видео недоступно.");
-        }
-      });
-    return () => controller.abort();
-  }, [
-    review.recordedMediaGenerationSha256,
-    review.recordedMediaSourceId,
-    review.sessionId,
-    review.timelineEndSeconds,
-    review.timelineStartSeconds,
-  ]);
-
-  return (
-    <LaboratoryEvidenceViewer
-      label="RAVNOVES004TREE full recorded review"
-      className="m48-atlas-visual"
-      mode={mode}
-      modes={FULL_ROUTE_MODES}
-      expanded={expanded}
-      onModeChange={setMode}
-      onExpandedChange={setExpanded}
-      chromeLayout="stacked"
-    >
-      {videoSource ? (
-        <LaboratoryRecordedClipPlayer
-          source={videoSource}
-          segmentCount={review.frameCount}
-          frames={frames}
-          sequence={sequence}
-          playing={playing}
-          playbackRate={playbackRate}
-          cameraPresentation="primary"
-          continuousPlayback
-          sourceCount={1}
-          onSequenceChange={setSequence}
-          onPlayingChange={setPlaying}
-          onPlaybackRateChange={setPlaybackRate}
-          cameraOverlay={(
-            <>
-              <div className="m48-clip-player__pane-label" data-pane="camera">
-                {mode === "source" ? "SOURCE" : `${mode === "city" ? "EoMT CITY" : "DDRNet NATURE"} · КАДР ${sequence}/${review.frameCount}`}
-              </div>
-              {layer && semantic ? (
-                <div className="m48-clip-player__overlay">
-                  <RecordedEvidenceSemanticMaskOverlay
-                    src={vegetationFullRouteMaskUrl(resultId, mode as "city" | "vegetation", maskSequence)}
-                    prefetchSrcs={prefetchSrcs}
-                    imageWidth={review.width}
-                    imageHeight={review.height}
-                    classes={semantic.classes}
-                    palette={semantic.palette}
-                    opacity={0.76}
-                    ariaLabel={`${layer.name} semantic prediction`}
-                  />
-                </div>
-              ) : null}
-            </>
-          )}
-        />
-      ) : (
-        <div className="m4-replay-threat-visual__pane-status" role={videoError ? "alert" : "status"}>
-          {videoError ?? "Открываем автономный recorded source…"}
-        </div>
-      )}
-    </LaboratoryEvidenceViewer>
-  );
-}
-
-function FullRouteReviewResult({
-  rigLabel,
-  resultId,
-  review,
-}: {
-  rigLabel: string;
-  resultId: string;
-  review: VegetationFullRouteReview;
-}) {
-  return (
-    <LaboratoryWorkTemplate
-      summary={(
-        <LaboratorySummary
-          title="LAB V1 · RAVNOVES004TREE · полный маршрут"
-          description="Существующий M4.7-шаблон воспроизводит всю запись и переключает два независимых sealed semantic-слоя: городской EoMT и природный DDRNet. Worker для открытия результата не нужен."
-          status="FULL RECORDED REVIEW · truth отсутствует · commands OFF"
-          statusTone="warning"
-          facts={[
-            { label: "Источник", value: `${review.sourceId} · ${review.frameCount}/${review.frameCount} frames` },
-            { label: "Город", value: `${review.city.name} · ${decimal(review.city.inferenceFps, 2)} fps` },
-            { label: "Природа", value: `${review.vegetation.name} · ${decimal(review.vegetation.inferenceFps, 2)} fps` },
-            { label: "Authority", value: `${rigLabel} · VISUAL REVIEW ONLY · commands OFF` },
-          ]}
-          brief={{
-            question: "Как оба semantic-кандидата ведут себя на полном переходе от сельской среды к городской?",
-            approach: "Все 6830 позиции одной recorded timeline последовательно прогнаны на Worker 006 и сохранены двумя независимыми архивами масок. В M4.7 переключается только видимый слой.",
-            principalResult: "Полная временная шкала доступна локально в SOURCE / EoMT CITY / DDRNet NATURE без обращения к Worker.",
-            limitation: "Ручной truth отсутствует. Один повреждённый H.264-пакет на позиции 6092 представлен предыдущим декодированным кадром и явно зафиксирован в proof. Полный TGS и кюветы этим прогоном не проверялись.",
-          }}
-          method={{
-            completeness: "complete",
-            executionClass: "ai-inference",
-            pipelineId: "ravnoves004tree-full-eomt-ddrnet-recorded-review/v1",
-            components: [
-              { kind: "model", name: review.city.name, version: "sealed Worker 006 run", role: "urban semantic review", identitySha256: null },
-              { kind: "model", name: review.vegetation.name, version: "GOOSE DDRNet-39", role: "vegetation semantic review", identitySha256: null },
-            ],
-          }}
-        />
-      )}
-      evidence={(
-        <LaboratoryEvidence
-          eyebrow="M4.7 TEMPLATE · RAVNOVES004TREE FULL VIDEO"
-          title="SOURCE / EoMT CITY / DDRNet NATURE · 6830/6830 · TRUTH отсутствует"
-          kind="diagnostic-model"
-          resizable
-        >
-          <FullRouteReviewEvidence resultId={resultId} review={review} />
-        </LaboratoryEvidence>
-      )}
-      result={(
-        <LaboratoryResultSummary
-          title="Полный двухслойный visual review собран; управление не авторизовано"
-          status="Recorded evidence ready · navigation/actuation OFF"
-          statusTone="warning"
-          metrics={[
-            { label: "Route masks", value: "6830/6830 × 2", hint: "sealed local archives · Worker не требуется" },
-            { label: "EoMT p95", value: `${decimal(review.city.latencyP95Ms, 2)} ms`, hint: "последовательный изолированный прогон" },
-            { label: "DDRNet p95", value: `${decimal(review.vegetation.latencyP95Ms, 2)} ms`, hint: "последовательный изолированный прогон" },
-            { label: "Decode repair", value: "1/6830", hint: "sequence 6092 · previous frame · sealed proof" },
-          ]}
-          conclusion={{
-            proved: "Городской EoMT и природный DDRNet воспроизводимо обработали полную запись и доступны в одном существующем M4.7 viewer.",
-            notProved: "Не доказаны truth accuracy, одновременный realtime-load, полный TGS, отрицательные препятствия и безопасное управление ровером.",
-            decision: "Использовать результат только как визуальную диагностику. Navigation/actuation оставить OFF; следующий gate — оценка временной стабильности и независимый person/vehicle STOP.",
-          }}
-        />
-      )}
-    />
-  );
-}
-
-function MixedRouteReviewEvidence({ review }: { review: VegetationMixedRouteReview }) {
-  const [index, setIndex] = useState(0);
-  const [mode, setMode] = useState<typeof MIXED_ROUTE_MODES[number]["value"]>("vegetation");
-  const [expanded, setExpanded] = useState(false);
-  const item = review.cases[index]!;
-  return (
-    <LaboratoryEvidenceViewer
-      label="RAVNOVES004TREE mixed route review"
-      className="m48-atlas-visual"
-      mode={mode}
-      modes={MIXED_ROUTE_MODES}
-      expanded={expanded}
-      onModeChange={setMode}
-      onExpandedChange={setExpanded}
-      chromeLayout="stacked"
-      actions={(
-        <>
-          <IconButton label="Предыдущая сцена" onClick={() => setIndex((index - 1 + review.cases.length) % review.cases.length)}>
-            <Icon name="chevron-left" size={16} />
-          </IconButton>
-          <IconButton label="Следующая сцена" onClick={() => setIndex((index + 1) % review.cases.length)}>
-            <Icon name="chevron-right" size={16} />
-          </IconButton>
-        </>
-      )}
-      overlay={(
-        <div className="m48-atlas-visual__case">
-          <StatusBadge tone={item.phase === "urban" ? "accent" : item.phase === "transition" ? "warning" : "neutral"}>
-            {item.phase.toUpperCase()} · {index + 1}/{review.cases.length}
-          </StatusBadge>
-          <strong>sequence {item.sourceSequence} · +{decimal(item.sessionSeconds, 2)} s</strong>
-          <small>
-            TGS: {item.tgs.groundCells} ground · {item.tgs.occupiedCells} occupied · {item.tgs.unobservedCells} unobserved
-          </small>
-        </div>
-      )}
-    >
-      <div className="recorded-evidence-image-scene">
-        <img src={item.assets[mode]} alt="" draggable={false} />
-      </div>
-    </LaboratoryEvidenceViewer>
-  );
-}
-
-function MixedRouteReviewResult({
-  rigLabel,
-  review,
-}: {
-  rigLabel: string;
-  review: VegetationMixedRouteReview;
-}) {
-  return (
-    <LaboratoryWorkTemplate
-      summary={(
-        <LaboratorySummary
-          title="LAB V1 · RAVNOVES004TREE · село → город"
-          description="Существующий LAB-шаблон показывает 10 синхронных camera/LiDAR сцен одной записи. EoMT и DDRNet остаются независимыми слоями; TGS показывает отдельную геометрию и не может быть очищен семантической маской."
-          status="BOUNDED RECORDED REVIEW · truth отсутствует · commands OFF"
-          statusTone="warning"
-          facts={[
-            { label: "Источник", value: `${review.sourceId} · ${review.frameCount} camera/LiDAR islands` },
-            { label: "Переход", value: "5 rural · 1 transition · 4 urban" },
-            { label: "Слои", value: "SOURCE · EoMT CITY · DDRNet VEGETATION · causal TGS" },
-            { label: "Authority", value: `${rigLabel} · VISUAL REVIEW ONLY · commands OFF` },
-          ]}
-          brief={{
-            question: "Сохраняются ли городская семантика, растительность и геометрия при переходе из сельской среды в город?",
-            approach: "Выбраны десять соседних с исходными сцен camera-кадров, каждый синхронизирован с LiDAR в пределах 100 мс. Все три вычислительных слоя прогнаны на Worker 006 и запечатаны локально.",
-            principalResult: "Все 10 сцен обработаны EoMT, DDRNet и causal TGS. Слои можно переключать без наложения цветов и без зависимости LAB от воркера.",
-            limitation: "Это bounded islands без ручной truth. DDRNet шумит по подтипам растительности; TGS не доказывает обнаружение кювета или отрицательного препятствия.",
-          }}
-          method={{
-            completeness: "complete",
-            executionClass: "ai-inference",
-            pipelineId: "ravnoves004tree-eomt-ddrnet-causal-tgs-review/v1",
-            components: [
-              { kind: "model", name: review.models.city.name, version: "sealed Worker run", role: "urban semantic review", identitySha256: null },
-              { kind: "model", name: review.models.vegetation.name, version: "GOOSE DDRNet-39", role: "vegetation semantic review", identitySha256: null },
-              { kind: "algorithm", name: review.models.tgs.name, version: "TRAVEL compatibility runner", role: "independent local geometry", identitySha256: null },
-            ],
-          }}
-        />
-      )}
-      evidence={(
-        <LaboratoryEvidence
-          eyebrow="M4.7 TEMPLATE · RAVNOVES004TREE"
-          title="SOURCE / ГОРОД / ПРИРОДА / TGS · 10/10 · TRUTH отсутствует"
-          kind="diagnostic-model"
-          resizable
-        >
-          <MixedRouteReviewEvidence review={review} />
-        </LaboratoryEvidence>
-      )}
-      result={(
-        <LaboratoryResultSummary
-          title="Переход село → город воспроизведён; safety gate не закрыт"
-          status="Review ready · navigation/actuation OFF"
-          statusTone="warning"
-          metrics={[
-            { label: "Aligned scenes", value: "10/10", hint: "camera + LiDAR + pose · автономный archive" },
-            { label: "EoMT end-to-end p95", value: `${decimal(review.models.city.endToEndP95Ms, 2)} ms`, hint: `${decimal(review.models.city.inferenceFps, 2)} fps в изолированном прогоне` },
-            { label: "DDRNet inference p95", value: `${decimal(review.models.vegetation.latencyP95Ms, 2)} ms`, hint: "candidate review · не совместный realtime stack" },
-            { label: "TGS p95", value: `${decimal(review.models.tgs.latencyP95Ms, 2)} ms`, hint: `${review.models.tgs.cellSizeM} m cells · ${review.models.tgs.radiusM} m radius` },
-          ]}
-          conclusion={{
-            proved: "Оба semantic слоя и causal TGS воспроизводимо работают на сельской, переходной и городской части новой записи.",
-            notProved: "Не доказаны accuracy без truth, временная стабильность по всему видео, детект кюветов и безопасное совместное realtime-управление ровером.",
-            decision: "Оставить navigation/actuation OFF. Следующий короткий gate — непрерывный realtime-load двух моделей плюс независимый person/vehicle STOP; кюветы проверять отдельной записью.",
-          }}
-        />
-      )}
-    />
-  );
-}
-
-function VegetationRouteEvidence({ result }: { result: VegetationShadowResult }) {
-  const route = result.routeVideo!;
-  const [tgs, setTgs] = useState<M49TgsFullShadowResult | null>(null);
-  const [tgsError, setTgsError] = useState<string | null>(null);
-
-  useEffect(() => {
-    const controller = new AbortController();
-    setTgs(null);
-    setTgsError(null);
-    if (!route.linkedTgsResultId) return () => controller.abort();
-    void fetchM49TgsFullShadowResult(route.linkedTgsResultId, {
-      signal: controller.signal,
-    }).then((next) => {
-      if (controller.signal.aborted) return;
-      if (next.source.linkedVisualResultId !== route.baseM4ResultId) {
-        throw new Error("TGS и camera timeline имеют разные source identities.");
-      }
-      setTgs(next);
-    }).catch((caught: unknown) => {
-      if (!controller.signal.aborted) {
-        setTgsError(caught instanceof Error ? caught.message : "Sealed TGS недоступен.");
-      }
-    });
-    return () => controller.abort();
-  }, [route.baseM4ResultId, route.linkedTgsResultId]);
-
-  const semantic = {
-    id: "vegetation",
-    controlLabel: "ПРИРОДА · DDRNet",
-    resultId: route.workerResultId,
-    spatialResultId: null,
-    taxonomy: route.taxonomy,
-    maskUrl: (sequence: number) => vegetationVideoMaskUrl(result.resultId, sequence),
-    label: "DDRNet coarse vegetation material · recorded video",
-    maskAriaLabel: "DDRNet vegetation material prediction",
-  } as const;
-
-  if (route.linkedTgsResultId && tgs) {
-    return (
-      <M49TgsFullShadowEvidence
-        result={tgs}
-        semanticOverride={semantic}
-        evidenceLabel="LAB V1 · EoMT + DDRNet + YOLOX + TGS"
-      />
-    );
-  }
-  if (route.linkedTgsResultId && !tgsError) {
-    return (
-      <div className="m4-replay-threat-visual__pane-status" role="status">
-        Открываем sealed EoMT, TGS и coarse vegetation timeline…
-      </div>
-    );
-  }
-  return (
-    <>
-      <M4ReplayThreatVisual
-        resultId={route.baseM4ResultId}
-        evidenceLabel="LAB V1 · DDRNet"
-        showReferenceMediaLayers
-        showSpatialOverlaySummary={false}
-        semantic={semantic}
-      />
-      {tgsError ? (
-        <div className="m4-replay-threat-visual__pane-status" role="alert">
-          TGS слой недоступен: {tgsError}
-        </div>
-      ) : null}
-    </>
-  );
+function comparisonCases(result: VegetationShadowResult): readonly M48MaskComparisonCase[] {
+  return result.validationCases.map((item) => {
+    const focus = item.focus!;
+    return {
+      caseId: item.caseId,
+      title: `${VEGETATION_LABELS[focus.className] ?? focus.className} · truth ${decimal(focus.truthFraction * 100, 1)}% кадра`,
+      sourceUrl: item.assets.source,
+      truthUrl: item.assets.truth,
+      predictions: {
+        ddrnet: item.assets.ddrnet,
+        ppliteseg: item.assets.ppliteseg,
+      },
+      errors: {
+        ddrnet: item.assets.ddrnet_error,
+        ppliteseg: item.assets.ppliteseg_error,
+      },
+    };
+  });
 }
 
 export function VegetationShadowResultView({
@@ -449,129 +56,143 @@ export function VegetationShadowResultView({
   rigLabel: string;
   result: VegetationShadowResult;
 }) {
-  if (result.routeFullReview) {
-    return (
-      <FullRouteReviewResult
-        rigLabel={rigLabel}
-        resultId={result.resultId}
-        review={result.routeFullReview}
-      />
-    );
-  }
-  if (result.routeReview) {
-    return <MixedRouteReviewResult rigLabel={rigLabel} review={result.routeReview} />;
-  }
-  const route = result.routeVideo;
   const selected = result.candidates.find(
     (candidate) => candidate.candidate === result.selectedCandidate,
   )!;
-
+  const alternative = result.candidates.find(
+    (candidate) => candidate.candidate !== result.selectedCandidate,
+  )!;
   return (
     <LaboratoryWorkTemplate
       summary={(
         <LaboratorySummary
-          title="LAB V1 · карта ровера · город + растительность"
-          description="Один recorded-контур RAVNOVES00 синхронно показывает городской EoMT, природный DDRNet, frozen YOLOX detections и causal TGS. Семантические маски переключаются, чтобы их цвета не скрывали друг друга; геометрическое veto остаётся независимым."
-          status={route
-            ? "MULTILAYER RECORDED REVIEW · commands OFF · route truth отсутствует"
-            : "ROUTE EVIDENCE MISSING · commands OFF"}
+          title="LAB V1 · готовые модели растительности"
+          description={result.routeVideo
+            ? "M4.8 сохраняет truth-backed сравнение моделей, а штатный M4.7 viewer показывает фактический DDRNet prediction на всей записи RAVNOVES00. Все 4489 масок запечатаны локально и открываются без Worker 006."
+            : "Штатный M4.8-инструмент сравнивает две готовые fine-64 модели на полном GOOSE validation split и на 12 truth-backed hard cases, выбранных только по наличию нужной растительности. Sealed evidence открывается локально без Worker 006."}
+          status={result.routeVideo
+            ? "DDRNet full-video prediction ready · route truth отсутствует"
+            : "Truth-backed model comparison · route transfer не принят"}
           statusTone="warning"
           facts={[
-            { label: "Источник", value: "RAVNOVES00 · sensor.camera.right · 4489 recorded frames" },
-            { label: "Город", value: "EoMT Cityscapes · sealed E47 semantic archive" },
-            { label: "Растительность", value: "DDRNet-39 fine-64 → coarse mission-neutral materials" },
-            { label: "Safety", value: "YOLOX object boxes + causal TGS · semantic masks не снимают veto" },
-            { label: "Authority", value: `${rigLabel} · VISUAL REVIEW ONLY · commands OFF` },
+            { label: "Источник", value: "GOOSE validation · 962 размеченных кадра · 12 vegetation hard cases" },
+            { label: "Сравнение", value: "DDRNet-39 vs PPLiteSeg · official fine-64 weights" },
+            { label: "Кейсы", value: "трава · куст · ствол · крона · изгородь · лес · посевы" },
+            ...(result.routeVideo ? [{
+              label: "Видео",
+              value: "RAVNOVES00 · 4489/4489 DDRNet masks · exact recorded sequence",
+            }] : []),
+            { label: "Authority", value: `${rigLabel} · MODEL QUALIFICATION ONLY · commands OFF` },
           ]}
           brief={{
-            question: "Можно ли одновременно видеть городской и природный semantic stack, не теряя независимую геометрическую защиту?",
-            approach: "EoMT и DDRNet сохранены как два независимых sealed слоя на одной M4 timeline. В штатном M4.7 viewer пользователь переключает только отображаемую маску; YOLOX и TGS остаются активными слоями evidence.",
-            principalResult: route
-              ? "Оба semantic archive доступны в одном viewer. Это не пиксельный fusion и не единая новая модель: городской и природный ответы остаются раздельными."
-              : "Route archive для этой immutable identity отсутствует.",
-            limitation: "RAVNOVES00 не имеет ручной truth. DDRNet заметно прыгает между HIGH GRASS, WOODY и UNKNOWN; поэтому subtype нельзя подавать напрямую в planner. Отсутствие класса никогда не означает свободный путь.",
+            question: "Какие готовые веса лучше различают проезжаемую траву, кусты и стволы на размеченных off-road кадрах?",
+            approach: "Обе модели последовательно прогнаны в одном изолированном CUDA-runtime на 962 кадрах. 12 визуальных кейсов выбраны детерминированно по truth-поддержке восьми растительных классов; один M4.8 viewer показывает source, truth, prediction и material-error для выбранной модели.",
+            principalResult: `${selected.loadedModelName} лидирует по vegetation IoU: ${decimal(selected.vegetationMeanIouPercent, 2)}% против ${decimal(alternative.vegetationMeanIouPercent, 2)}%. ${result.routeVideo ? "Его фактическая temporal stability теперь видна на всех 4489 кадрах штатного recorded viewer." : "Ошибки по каждому типу проверяются в одном штатном инструменте."}`,
+            limitation: "GOOSE — внешний размеченный домен; RAVNOVES00 — наш fisheye, но без ручной truth-разметки. Full-video слой показывает prediction, а не доказывает правильность. Папоротник отдельным классом отсутствует.",
           }}
           method={{
-            completeness: route ? "complete" : "legacy-partial",
+            completeness: "complete",
             executionClass: "ai-inference",
-            pipelineId: "ravnoves-eomt-ddrnet-yolox-causal-tgs-recorded-review/v1",
-            components: [
-              {
-                kind: "model",
-                name: "EoMT Cityscapes semantic",
-                version: "sealed E47 archive",
-                role: "urban semantic review",
-                identitySha256: null,
-              },
-              {
-                kind: "model",
-                name: selected.loadedModelName,
-                version: selected.candidate,
-                role: "vegetation material candidate",
-                identitySha256: selected.checkpointSha256,
-              },
-              {
-                kind: "algorithm",
-                name: "Frozen YOLOX + causal TGS",
-                version: "linked M4/M4.9 archives",
-                role: "independent object and geometry veto",
-                identitySha256: null,
-              },
-            ],
+            pipelineId: "goose-fine64-ready-weights-to-ravnoves-policy-shadow/v1",
+            components: result.candidates.map((candidate) => ({
+              kind: "model" as const,
+              name: candidate.loadedModelName,
+              version: candidate.candidate,
+              role: candidate.candidate === result.selectedCandidate ? "selected policy provider" : "comparison candidate",
+              identitySha256: candidate.checkpointSha256,
+            })),
           }}
         />
       )}
-      evidence={route ? (
-        <LaboratoryEvidence
-          eyebrow="M4.7 · RAVNOVES00 FULL VIDEO"
-          title="EoMT CITY / DDRNet VEGETATION + YOLOX + CAUSAL TGS · 4489/4489 · TRUTH отсутствует"
-          kind="diagnostic-model"
-          resizable
-        >
-          <VegetationRouteEvidence result={result} />
-        </LaboratoryEvidence>
-      ) : (
-        <LaboratoryEvidence
-          eyebrow="M4.7 · RAVNOVES00 FULL VIDEO"
-          title="ROUTE ARCHIVE отсутствует"
-          kind="diagnostic-model"
-        >
-          <div className="m4-replay-threat-visual__pane-status" role="alert">
-            Для этой immutable identity нет полного route video evidence.
-          </div>
-        </LaboratoryEvidence>
+      evidence={(
+        <>
+          <LaboratoryEvidence
+            eyebrow="M4.8 · GOOSE VEGETATION HARD CASES"
+            title="ERROR: красный — пропуск · жёлтый — лишнее · фиолетовый — перепутан тип · зелёный — совпадение"
+            kind="diagnostic-model"
+            resizable
+          >
+            <M48MaskComparisonVisual
+              cases={comparisonCases(result)}
+              initialCandidate={result.selectedCandidate}
+            />
+          </LaboratoryEvidence>
+          {result.routeVideo ? (
+            <LaboratoryEvidence
+              eyebrow="M4.7 · RAVNOVES00 FULL VIDEO"
+              title="DDRNet PREDICTION · 4489/4489 кадров · TRUTH для этой записи отсутствует"
+              kind="diagnostic-model"
+              resizable
+            >
+              <M4ReplayThreatVisual
+                resultId={result.routeVideo.baseM4ResultId}
+                evidenceLabel="LAB V1 · DDRNet"
+                showReferenceMediaLayers={false}
+                showSpatialOverlaySummary={false}
+                semantic={{
+                  resultId: result.routeVideo.workerResultId,
+                  spatialResultId: null,
+                  taxonomy: result.routeVideo.taxonomy,
+                  maskUrl: (sequence) => vegetationVideoMaskUrl(result.resultId, sequence),
+                  label: "DDRNet vegetation prediction · recorded video",
+                  maskAriaLabel: "DDRNet vegetation prediction",
+                }}
+              />
+            </LaboratoryEvidence>
+          ) : null}
+        </>
       )}
       result={(
         <LaboratoryResultSummary
-          title="Многослойный visual review собран; управление не авторизовано"
-          status="Semantics advisory · YOLOX/TGS veto cannot be cleared"
+          title="DDRNet — стартовые веса; перенос на ровер ещё не доказан"
+          status={`${selected.loadedModelName} выбран только как vegetation candidate`}
           statusTone="warning"
           metrics={[
             {
-              label: "Route masks",
-              value: route ? `${route.frameCount}/${route.frameCount}` : "0/4489",
-              hint: "sealed local playback · Worker для открытия не нужен",
+              label: "GOOSE mIoU",
+              value: `${decimal(selected.meanIouPercent, 2)}% / ${decimal(alternative.meanIouPercent, 2)}%`,
+              hint: `${selected.candidate} / ${alternative.candidate} · полный validation split`,
             },
             {
-              label: "Semantic sources",
-              value: route ? "2 independent layers" : "0",
-              hint: "EoMT CITY / DDRNet VEGETATION · display switches, evidence does not fuse",
+              label: "Vegetation IoU",
+              value: `${decimal(selected.vegetationMeanIouPercent, 2)}% / ${decimal(alternative.vegetationMeanIouPercent, 2)}%`,
+              hint: "агрегация классов grass/vegetation/bush/tree и родственных fine-64 labels",
             },
             {
-              label: "Vegetation worker p95",
-              value: `${decimal(selected.shadowLatencyP95Ms, 2)} ms`,
-              hint: "изолированный DDRNet inference; не совместный realtime stack",
+              label: "Worker shadow p95",
+              value: `${decimal(selected.shadowLatencyP95Ms, 2)} / ${decimal(alternative.shadowLatencyP95Ms, 2)} ms`,
+              hint: "чистый inference · одна тяжёлая модель за раз",
             },
             {
-              label: "Vegetation peak VRAM",
-              value: `${decimal(selected.peakReservedVramBytes / 1024 ** 3, 2)} GiB`,
-              hint: "DDRNet candidate на Worker 006",
+              label: "Cold prewarm",
+              value: `${decimal(selected.shadowPrewarmLatencyMs, 1)} / ${decimal(alternative.shadowPrewarmLatencyMs, 1)} ms`,
+              hint: "один явный inference до допуска кадров; исключён из steady-state p95",
             },
+            {
+              label: "Worker throughput",
+              value: `${decimal(selected.shadowThroughputFps, 1)} / ${decimal(alternative.shadowThroughputFps, 1)} FPS`,
+              hint: "изолированный Worker 006 · не realtime graph целиком",
+            },
+            {
+              label: "Peak VRAM",
+              value: `${decimal(selected.peakReservedVramBytes / 1024 ** 3, 2)} / ${decimal(alternative.peakReservedVramBytes / 1024 ** 3, 2)} GiB`,
+              hint: `${selected.candidate} / ${alternative.candidate} · RTX 4090`,
+            },
+            {
+              label: "Hard-case evidence",
+              value: "12 truth-backed cases",
+              hint: "8 vegetation strata · Worker для открытия не требуется",
+            },
+            ...(result.routeVideo ? [{
+              label: "Route video",
+              value: "4489/4489 masks",
+              hint: "DDRNet prediction · exact sequence · Worker-independent playback",
+            }] : []),
           ]}
           conclusion={{
-            proved: "На одной recorded timeline доступны городской EoMT, природный DDRNet, YOLOX detections и causal TGS; LAB автономна от Worker.",
-            notProved: "Не доказаны совместный live-runtime EoMT+DDRNet, truth accuracy на fisheye, стабильные vegetation subtypes и безопасное управление ровером.",
-            decision: "Использовать маски только для диагностики. Следующий qualification gate — motion-aware temporal vegetation fusion и отдельный совместный realtime load test; до него planner/actuation остаются OFF.",
+            proved: "Обе официальные fine-64 модели воспроизводимо запускаются на Worker 006; DDRNet лучше по aggregate vegetation IoU. Truth-backed hard cases прямо показывают траву, кусты и стволы, а не случайные автомобили и здания.",
+            notProved: "Не доказаны accuracy на нашем fisheye-домене, папоротник как отдельный материал, collision safety и physical-live поведение ровера. Видео позволяет увидеть temporal stability, но без truth не превращает её в метрику качества.",
+            decision: "Смотреть полный prediction на видео и собирать конкретные temporal/domain failure cases. DDRNet остаётся diagnostic candidate; LiDAR/TGS fail-closed геометрию не ослаблять.",
           }}
         />
       )}

apps/control-station/src/workspaces/laboratory/annotation/useL34AnnotationCapability.tsx

@@ -1,4 +1,4 @@
-import { useCallback, useEffect, useMemo, useState, type ReactNode } from "react";
+import { useCallback, useEffect, useState, type ReactNode } from "react";
 
 import type { L34RightYoloxTruthIslandResult } from "../../../core/laboratory/l34RightYoloxTruthIsland";
 import type { L34DResult } from "../../../core/laboratory/l34dCumulativePostprocessing";
@@ -28,19 +28,19 @@ export function useL34AnnotationCapability({
 }): ReactNode {
   const [open, setOpen] = useState(false);
   const openWorkspace = useCallback(() => setOpen(true), []);
-  const available = useMemo(() => (
-    selectedWorkId === "l34-right-yolox-truth-island-freeze" && l34Result
-      ? { resultId: l34Result.resultId, workflow: "assisted-candidate" as const }
-      : selectedWorkId === "e46-detector-truth-island" && e46Result
-        ? { resultId: e46Result.resultId, workflow: "independent-blind" as const }
-        : selectedWorkId === "e46a-ai-engineering-preannotation" && e46aResult
-          ? { resultId: e46aResult.resultId, workflow: "engineering-preannotation" as const }
-          : selectedWorkId === "l34d-cumulative-postprocessing-candidate" && l34dResult
-            ? { resultId: l34dResult.resultId, workflow: "prediction-hidden" as const }
-            : selectedWorkId === "l34e-self-review-diagnostic" && l34eResult
-              ? { resultId: l34eResult.resultId, workflow: "adjudication" as const }
-              : null
-  ), [e46Result, e46aResult, l34Result, l34dResult, l34eResult, selectedWorkId]);
+  const available = selectedWorkId === "l34-right-yolox-truth-island-freeze"
+    && l34Result
+    ? { resultId: l34Result.resultId, workflow: "assisted-candidate" as const }
+    : selectedWorkId === "e46-detector-truth-island" && e46Result
+      ? { resultId: e46Result.resultId, workflow: "independent-blind" as const }
+      : selectedWorkId === "e46a-ai-engineering-preannotation" && e46aResult
+        ? { resultId: e46aResult.resultId, workflow: "engineering-preannotation" as const }
+      : selectedWorkId === "l34d-cumulative-postprocessing-candidate"
+      && l34dResult
+      ? { resultId: l34dResult.resultId, workflow: "prediction-hidden" as const }
+      : selectedWorkId === "l34e-self-review-diagnostic" && l34eResult
+        ? { resultId: l34eResult.resultId, workflow: "adjudication" as const }
+        : null;
 
   useEffect(() => {
     if (!available) {

apps/control-station/src/workspaces/laboratory/laboratoryArchiveProfiles.ts

@@ -10,7 +10,6 @@ export type LaboratoryProfileId =
   | "rig-camera-local-surface-v1"
   | "rig-track-geometry-temporal-v1"
   | "rig-ravnoves-perception-gate-v1"
-  | "rig-goose-vegetation-benchmark-v1"
   | "rig-pointpillars-transfer-v1"
   | "rig-right-yolox-lidar-range-v1"
   | "rig-nvidia-ready-stack-v1"
@@ -64,19 +63,12 @@ interface KnownWorkDefinition {
 const rig = (rigLabel: string): string => rigLabel.trim() || "Сенсорный риг";
 
 const KNOWN_WORKS: Readonly<Record<Exclude<LaboratoryWorkId, `session:${string}`>, KnownWorkDefinition>> = {
-  "lab-v1-vegetation-benchmark": {
-    profileId: "rig-goose-vegetation-benchmark-v1",
-    profileName: (rigLabel) => `${rig(rigLabel)} · GOOSE vegetation archive`,
-    experimentId: "lab-v1-vegetation-benchmark-archive",
-    experimentName: "DDRNet vs PPLiteSeg · truth-backed archival comparison",
-    variantName: "M4.8 · GOOSE truth · архивный анализ моделей",
-  },
   "lab-v1-vegetation-shadow": {
     profileId: "rig-ravnoves-perception-gate-v1",
-    profileName: (rigLabel) => `${rig(rigLabel)} · RAVNOVES00 rover perception gate`,
+    profileName: (rigLabel) => `${rig(rigLabel)} · GOOSE vegetation qualification`,
     experimentId: "lab-v1-vegetation-mission-policy",
-    experimentName: "RAVNOVES00 · city + vegetation + TGS review",
-    variantName: "LAB V1 · EoMT + DDRNet + YOLOX + TGS · commands OFF",
+    experimentName: "DDRNet vs PPLiteSeg · truth-backed vegetation hard cases",
+    variantName: "LAB V1 · готовые vegetation weights · GOOSE truth",
   },
   "m48-object-centric-quality": {
     profileId: "rig-dual-evidence-virtual-corridor-v1",

apps/control-station/src/workspaces/laboratory/useAdvancedLaboratoryCatalog.ts

@@ -18,7 +18,6 @@ function mergeResults(
   next: AdvancedLaboratoryResults,
 ): AdvancedLaboratoryResults {
   return {
-    vegetationBenchmark: next.vegetationBenchmark ?? current.vegetationBenchmark,
     vegetationShadow: next.vegetationShadow ?? current.vegetationShadow,
     m47Graph: next.m47Graph ?? current.m47Graph,
     m48: next.m48 ?? current.m48,
@@ -123,7 +122,6 @@ export function useAdvancedLaboratoryCatalog({
     const indexedResultId = index.find((item) => item.workId === selectedWorkId)?.resultId;
     if (
       [
-        "lab-v1-vegetation-benchmark",
         "lab-v1-vegetation-shadow",
         "m47-reference-graph-shadow",
         "m48-object-centric-quality",

apps/control-station/test/m49TgsFullShadow.test.mjs

@@ -108,14 +108,7 @@ test("M4.9T5 viewer prefers autonomous chunks and keeps a sealed legacy fallback
   assert.doesNotMatch(source, /centersXyM\.map\(/);
   assert.match(source, /fetchE47SemanticSlamResult/);
   assert.match(source, /next\.baseM4ResultId !== result\.source\.linkedVisualResultId/);
-  assert.match(source, /semanticLayers=\{semanticLayers\}/);
-  assert.match(source, /ГОРОД · EoMT/);
-  assert.match(source, /ПРИРОДА · DDRNet/);
-  assert.match(source, /semanticOverride/);
-  assert.doesNotMatch(source, /if \(semanticOverride\) return/);
-  assert.match(visual, /label="Источник семантики"/);
-  assert.match(visual, /availableSemanticLayers\.length > 1/);
-  assert.doesNotMatch(visual, /classifiedSpatialLayer \|\| !showReferenceMediaLayers \? \[\]/);
+  assert.match(source, /semantic=\{semantic \? \{/);
   assert.match(
     visual,
     /classifiedSpatialFrame\s*&&\s*classifiedSpatialFrame\.sampleAvailable !== false/,

apps/control-station/test/vegetationShadow.test.mjs

@@ -5,9 +5,7 @@ import { after, before, test } from "node:test";
 import { createServer } from "vite";
 
 let server;
-let fetchVegetationBenchmarkResult;
 let fetchVegetationShadowResult;
-let vegetationFullRouteMaskUrl;
 
 before(async () => {
   server = await createServer({
@@ -15,11 +13,7 @@ before(async () => {
     logLevel: "silent",
     server: { middlewareMode: true },
   });
-  ({
-    fetchVegetationBenchmarkResult,
-    fetchVegetationShadowResult,
-    vegetationFullRouteMaskUrl,
-  } = await server.ssrLoadModule(
+  ({ fetchVegetationShadowResult } = await server.ssrLoadModule(
     "/src/core/laboratory/vegetationShadow.ts",
   ));
 });
@@ -29,7 +23,6 @@ after(async () => {
 });
 
 const resultId = `lab-v1-vegetation-shadow-${"a".repeat(64)}`;
-const benchmarkResultId = `lab-v1-vegetation-benchmark-${"d".repeat(64)}`;
 
 function candidate(candidateKey, vegetationIou) {
   return {
@@ -111,157 +104,45 @@ function routeVideo() {
   };
 }
 
-function coarseRouteVideo() {
-  return {
-    ...routeVideo(),
-    view_kind: "coarse-material-policy-review",
-    linked_tgs_result_id: `m49-tgs-full-shadow-${"2".repeat(64)}`,
-    taxonomy: {
-      schema_version: "missioncore.lab-v1-terrain-policy-taxonomy/v1",
-      classes: Array.from({ length: 10 }, (_, classId) => ({
-        class_id: classId,
-        label: `policy-${classId}`,
-        color_rgb: [classId, classId, classId],
-        disposition: classId === 0 ? "ambiguous" : classId === 9 ? "undefined" : "prediction",
-        material_class: classId === 0 || classId === 9 ? null : "grass",
-        evidence_state: classId === 0 || classId === 9 ? "UNOBSERVED" : "SUPPORTED_GROUND",
-      })),
-    },
-    aggregate_prediction_pixels: Array(10).fill(0),
-    mask_archive: {
-      path: "video/coarse-material-policy-masks.zip",
-      sha256: "8".repeat(64),
-      byte_length: 2048,
-    },
-    valid_fov: {
-      mask_path: "video/valid-fov-mask.png",
-      mask_sha256: "7".repeat(64),
-      outside_valid_fov_class_id: 9,
-    },
-    policy: {
-      presets: {
-        urban: { grass: "NO_GO" },
-        rural: { grass: "HIGH_COST" },
-        offroad: { grass: "HIGH_COST" },
-      },
-    },
-    fusion: {
-      mode: "synchronised-multilayer-review",
-      pixel_raster_fusion: false,
-      camera_semantic_temporal_filter: "none",
-    },
-  };
-}
-
-function fullRouteReview() {
-  const layer = (kind) => ({
-    name: kind === "city" ? "EoMT Cityscapes" : "ddrnet_39",
-    result_id: kind === "city"
-      ? `result-${"2".repeat(64)}`
-      : `lab-v1-ravnoves-video-ddrnet-${"3".repeat(64)}`,
-    frame_count: 6830,
-    taxonomy: {
-      schema_version: kind === "city"
-        ? "missioncore.recorded-eomt-taxonomy/v1"
-        : "missioncore.lab-v1-vegetation-taxonomy/v1",
-      classes: Array.from({ length: kind === "city" ? 16 : 64 }, (_, classId) => ({
-        class_id: classId,
-        label: classId === 0 ? "undefined" : `${kind}-${classId}`,
-        color_rgb: [classId, classId, classId],
-        disposition: classId === 0 ? "undefined" : "prediction",
-      })),
-    },
-    mask_archive: {
-      path: kind === "city"
-        ? "video/eomt-semantic-masks.zip"
-        : "video/ddrnet-semantic-masks.zip",
-      sha256: "4".repeat(64),
-      byte_length: 4096,
-    },
-    inference_fps: 9.5,
-    latency_p95_ms: 101.2,
-    peak_reserved_vram_bytes: 3_000_000_000,
-  });
-  return {
-    source_id: "RAVNOVES004TREE",
-    session_id: "20260828T130511Z_viewer_live",
-    source_job_id: "recorded-camera-eb2783c5480d56bda07c8af0",
-    source_job_input_sha256: "eb2783c5480d56bda07c8af008dff5344d19dc550ef70fe2075d6f098f7cc715",
-    source_stream_sha256: "e5eb017e2cc0f546736eda5235ca157b501913093cb64af5e548e335417e1bac",
-    recorded_media_source_id: "recorded.camera.6a3945242828a038",
-    recorded_media_generation_sha256: "b073ea1e7babf1c77a664e1a5b95e3702d0e05b0e34c1e85a7c67a6f8b392ded",
-    frame_count: 6830,
-    width: 800,
-    height: 600,
-    timeline_start_seconds: 39.215263458,
-    timeline_end_seconds: 757.260263458,
-    timeline: {
-      path: "video/frame-source-times-ns.bin",
-      sha256: "5".repeat(64),
-      byte_length: 6830 * 8,
-      encoding: "uint64-le-nanoseconds",
-      frame_count: 6830,
-    },
-    ground_truth: false,
-    decode_repair: {
-      repaired_frame_count: 1,
-      sequence: 6092,
-      method: "duplicate-previous-decoded-frame",
-      proofs: {
-        eomt: { path: "proofs/decode_repair.json", sha256: "7".repeat(64) },
-        ddrnet: { path: "proofs/ddrnet_decode_repair.json", sha256: "8".repeat(64) },
-      },
-    },
-    layers: { city: layer("city"), vegetation: layer("vegetation") },
-  };
-}
-
-function labPayload(route = routeVideo()) {
-  return {
-    schema_version: "missioncore.lab-v1-vegetation-shadow/v1",
-    result_id: resultId,
-    created_at_utc: "2026-08-27T20:00:00Z",
-    status: "visual-shadow-ready-policy-not-authorized",
-    ground_truth: false,
-    identity: { selected_candidate: "ddrnet" },
-    metrics: {
-      candidates: {
-        ddrnet: candidate("ddrnet", 0.64),
-        ppliteseg: candidate("ppliteseg", 0.61),
-      },
-    },
-    decision: {
-      selected_candidate: "ddrnet",
-      visual_shadow_ready: true,
-      mission_policy_ready_for_configuration: true,
-      navigation_accepted: false,
-      production_accepted: false,
-    },
-    limitations: ["shadow only"],
-    authority: {
-      commands_enabled: false,
-      navigation_or_safety_accepted: false,
-      actuation_accepted: false,
-      camera_semantics_can_clear_rigid_geometry: false,
-    },
-    catalogs: {
-      goose: Array.from({ length: 12 }, (_, index) => visualCase("goose", index)),
-      ravnoves: [],
-    },
-    route_video: route,
-    access: "read-only",
-  };
-}
-
 test("vegetation LAB keeps autonomous assets and fail-closed authority", async () => {
   let requestedUrl = "";
   const result = await fetchVegetationShadowResult(resultId, {
     fetcher: async (url) => {
       requestedUrl = String(url);
-      return new Response(JSON.stringify(labPayload()), {
-        status: 200,
-        headers: { "Content-Type": "application/json" },
-      });
+      return new Response(JSON.stringify({
+        schema_version: "missioncore.lab-v1-vegetation-shadow/v1",
+        result_id: resultId,
+        created_at_utc: "2026-08-27T20:00:00Z",
+        status: "visual-shadow-ready-policy-not-authorized",
+        ground_truth: false,
+        identity: { selected_candidate: "ddrnet" },
+        metrics: {
+          candidates: {
+            ddrnet: candidate("ddrnet", 0.64),
+            ppliteseg: candidate("ppliteseg", 0.61),
+          },
+        },
+        decision: {
+          selected_candidate: "ddrnet",
+          visual_shadow_ready: true,
+          mission_policy_ready_for_configuration: true,
+          navigation_accepted: false,
+          production_accepted: false,
+        },
+        limitations: ["shadow only"],
+        authority: {
+          commands_enabled: false,
+          navigation_or_safety_accepted: false,
+          actuation_accepted: false,
+          camera_semantics_can_clear_rigid_geometry: false,
+        },
+        catalogs: {
+          goose: Array.from({ length: 12 }, (_, index) => visualCase("goose", index)),
+          ravnoves: [],
+        },
+        route_video: routeVideo(),
+        access: "read-only",
+      }), { status: 200, headers: { "Content-Type": "application/json" } });
     },
   });
   assert.equal(
@@ -273,8 +154,6 @@ test("vegetation LAB keeps autonomous assets and fail-closed authority", async (
   assert.equal(result.routeCases.length, 0);
   assert.equal(result.validationCases.length, 12);
   assert.equal(result.routeVideo.frameCount, 4489);
-  assert.equal(result.routeVideo.viewKind, "fine-semantic-prediction");
-  assert.equal(result.routeVideo.linkedTgsResultId, null);
   assert.equal(result.routeVideo.taxonomy[0].disposition, "undefined");
   assert.equal(result.validationCases[0].focus.className, "high_grass");
   assert.match(result.validationCases[0].assets.ddrnet_error, /\/assets\/visual\/goose\//);
@@ -286,109 +165,15 @@ test("vegetation LAB keeps autonomous assets and fail-closed authority", async (
   });
 });
 
-test("vegetation LAB parses coarse material policy and sealed TGS binding", async () => {
-  const result = await fetchVegetationShadowResult(resultId, {
-    fetcher: async () => new Response(JSON.stringify(labPayload(coarseRouteVideo())), {
-      status: 200,
-      headers: { "Content-Type": "application/json" },
-    }),
-  });
-  assert.equal(result.routeVideo.viewKind, "coarse-material-policy-review");
-  assert.match(result.routeVideo.linkedTgsResultId, /^m49-tgs-full-shadow-/);
-  assert.equal(result.routeVideo.taxonomy.length, 10);
-  assert.equal(result.routeVideo.taxonomy[0].evidenceState, "UNOBSERVED");
-  assert.equal(result.routeVideo.policyPresets.urban.grass, "NO_GO");
-  assert.equal(result.routeVideo.fusionMode, "synchronised-multilayer-review");
-});
-
-test("vegetation LAB parses the full 004 pass inside the existing result contract", async () => {
-  const payload = {
-    ...labPayload(null),
-    catalogs: { goose: [], ravnoves: [] },
-    route_full_review: fullRouteReview(),
-  };
-  const timeline = new ArrayBuffer(6830 * 8);
-  const timelineView = new DataView(timeline);
-  for (let index = 0; index < 6830; index += 1) {
-    timelineView.setBigUint64(
-      index * 8,
-      BigInt(39_215_263_458 + index * 100_000_000),
-      true,
-    );
-  }
-  const result = await fetchVegetationShadowResult(resultId, {
-    fetcher: async (url) => String(url).endsWith("/route-timeline")
-      ? new Response(timeline, {
-        status: 200,
-        headers: {
-          "Content-Type": "application/octet-stream",
-          ETag: `"${"5".repeat(64)}"`,
-        },
-      })
-      : new Response(JSON.stringify(payload), {
-        status: 200,
-        headers: { "Content-Type": "application/json" },
-      }),
-  });
-  assert.equal(result.routeVideo, null);
-  assert.equal(result.routeFullReview.frameCount, 6830);
-  assert.equal(result.routeFullReview.city.taxonomy.length, 16);
-  assert.equal(result.routeFullReview.vegetation.taxonomy.length, 64);
-  assert.equal(result.routeFullReview.decodeRepair.sequence, 6092);
-  assert.equal(result.routeFullReview.frameSourceTimesNs.length, 6830);
-  assert.equal(
-    vegetationFullRouteMaskUrl(resultId, "vegetation", 6829),
-    `/api/v1/laboratory/vegetation-shadow/${resultId}/route-masks/vegetation/6829`,
+test("vegetation LAB reuses the admitted M4.8 and M4.7 instruments", async () => {
+  const resultSource = await readFile(
+    new URL("../src/workspaces/laboratory/VegetationShadowResult.tsx", import.meta.url),
+    "utf8",
   );
-});
-
-test("vegetation GOOSE benchmark opens through its separate archival endpoint", async () => {
-  let requestedUrl = "";
-  const result = await fetchVegetationBenchmarkResult(benchmarkResultId, {
-    fetcher: async (url) => {
-      requestedUrl = String(url);
-      return new Response(JSON.stringify({
-        ...labPayload(null),
-        result_id: benchmarkResultId,
-      }), {
-        status: 200,
-        headers: { "Content-Type": "application/json" },
-      });
-    },
-  });
-  assert.equal(
-    requestedUrl,
-    `/api/v1/laboratory/vegetation-benchmark/${benchmarkResultId}`,
-  );
-  assert.equal(result.routeVideo, null);
-  assert.equal(result.validationCases.length, 12);
-});
-
-test("vegetation realtime LAB and archival benchmark use separate admitted instruments", async () => {
-  const [resultSource, benchmarkSource] = await Promise.all([
-    readFile(
-      new URL("../src/workspaces/laboratory/VegetationShadowResult.tsx", import.meta.url),
-      "utf8",
-    ),
-    readFile(
-      new URL("../src/workspaces/laboratory/VegetationBenchmarkResult.tsx", import.meta.url),
-      "utf8",
-    ),
-  ]);
-  assert.doesNotMatch(resultSource, /M48MaskComparisonVisual/);
+  assert.match(resultSource, /M48MaskComparisonVisual/);
   assert.match(resultSource, /M4ReplayThreatVisual/);
-  assert.match(resultSource, /M49TgsFullShadowEvidence/);
-  assert.match(resultSource, /semanticOverride/);
-  assert.match(resultSource, /EoMT CITY \/ DDRNet VEGETATION/);
-  assert.equal(resultSource.match(/<LaboratoryEvidence\b/g)?.length, 4);
-  assert.match(resultSource, /RAVNOVES004TREE mixed route review/);
-  assert.match(resultSource, /RAVNOVES004TREE full recorded review/);
-  assert.match(resultSource, /LaboratoryRecordedClipPlayer/);
-  assert.match(resultSource, /className="m48-clip-player__overlay"/);
-  assert.match(resultSource, /linkedTgsResultId/);
-  assert.match(benchmarkSource, /M48MaskComparisonVisual/);
-  assert.doesNotMatch(benchmarkSource, /M49TgsFullShadowEvidence/);
-  assert.equal(benchmarkSource.match(/<LaboratoryEvidence\b/g)?.length, 1);
+  assert.equal(resultSource.match(/<LaboratoryEvidence\b/g)?.length, 2);
+  assert.match(resultSource, /showReferenceMediaLayers=\{false\}/);
   assert.doesNotMatch(resultSource, /VegetationRouteVisual|urban\/rural\/off-road presets/);
   await assert.rejects(
     access(new URL("../src/workspaces/laboratory/VegetationShadowVisual.tsx", import.meta.url)),

config/laboratories/lab-v1-vegetation-benchmark.json

@@ -1,10 +0,0 @@
-{
-  "schema_version": "missioncore.laboratory-evidence-definition/v1",
-  "work_id": "lab-v1-vegetation-benchmark",
-  "evidence": {
-    "runtime_relative_root": "lab-v1-vegetation-benchmark/results",
-    "result_id_prefix": "lab-v1-vegetation-benchmark",
-    "document_name": "result.json",
-    "schema_version": "missioncore.lab-v1-vegetation-shadow/v1"
-  }
-}

config/laboratory-execution.json

@@ -212,7 +212,6 @@
     }
   ],
   "legacy_work_ids": [
-    "lab-v1-vegetation-benchmark",
     "m48r3-static-occupancy-shadow",
     "m47-reference-graph-shadow",
     "e31-source-binding",

config/laboratory-value-review.json

@@ -282,17 +282,10 @@
       "lifecycle": "current",
       "visual_evidence": "available"
     },
-    {
-      "catalog_id": "lab-v1-vegetation-benchmark",
-      "evidence_id": "lab-v1-vegetation-benchmark-a8944d6c2d1102d81da78bcb4963760c9288db0421d9f2686afcbdd14b610d3d",
-      "signal": "progress",
-      "lifecycle": "current",
-      "visual_evidence": "available"
-    },
     {
       "catalog_id": "lab-v1-vegetation-shadow",
-      "evidence_id": "lab-v1-vegetation-shadow-d179462134967ace1c5ebd6fbdbdd8659905d390484b9c01ea7930f083bb74d1",
-      "signal": "progress",
+      "evidence_id": "lab-v1-vegetation-shadow-ad4d9fbbb21ff8a270b77f559b4e78dcdaf0455afd61afb5033009623984e554",
+      "signal": "failed",
       "lifecycle": "current",
       "visual_evidence": "available"
     }

config/perception/lab-v1-ravnoves004tree-full-video-source-v1.json

@@ -1,19 +0,0 @@
-{
-  "schema_version": "missioncore.lab-v1-ravnoves-source/v1",
-  "profile_id": "ravnoves004tree-full-video-source/v1",
-  "source": {
-    "source_id": "RAVNOVES004TREE/right-e5eb017e2cc0f546736eda5235ca157b501913093cb64af5e548e335417e1bac",
-    "source_sha256": "e5eb017e2cc0f546736eda5235ca157b501913093cb64af5e548e335417e1bac",
-    "source_job_id": "recorded-camera-eb2783c5480d56bda07c8af0",
-    "source_job_input_sha256": "eb2783c5480d56bda07c8af008dff5344d19dc550ef70fe2075d6f098f7cc715",
-    "session_id": "20260828T130511Z_viewer_live",
-    "base_m4_result_id": null,
-    "expected_width": 800,
-    "expected_height": 600,
-    "expected_frame_count": 6830,
-    "timeline_start_seconds": 39.215263458,
-    "timeline_end_seconds": 757.260263458,
-    "frame_indices": [],
-    "crop_contract": "center-600-square-to-512; outside-crop-is-undefined"
-  }
-}

config/perception/lab-v1-vegetation-integrated-multirate-phased-shadow-v3.json

@@ -1,82 +0,0 @@
-{
-  "schema_version": "missioncore.lab-v1-vegetation-integrated-shadow-profile/v3",
-  "profile_id": "lab-v1-ravnoves00-ddrnet-m49-integrated-multirate-phased-shadow/v3",
-  "source": {
-    "source_id": "RAVNOVES00",
-    "expected_timeline_frames": 4489,
-    "requested_source_rate_hz": 12.0,
-    "shared_start_barrier": true,
-    "ground_truth_available": false
-  },
-  "stages": {
-    "m49_graph_tgs": {
-      "profile": "m49-tgs-integrated-graph-shadow-v1.json",
-      "profile_sha256": "b61e018b2d04eec58802e2d4186ce7a3dd3a15b254db106b57b609e903eeef80",
-      "candidate": "frozen-native-rf-detr-plus-cpu-tgs",
-      "parameters_unchanged": true,
-      "timeline_rate_hz": 12.0
-    },
-    "vegetation": {
-      "candidate_id": "ddrnet_39-goose-fine-64",
-      "candidate_key": "ddrnet",
-      "checkpoint_sha256": "b99c2838051bcd7b092fd3970aa62a77d5c0bbb809c9b9afb2ff4b0ebdaa4ee6",
-      "config_sha256": "96a427a8baae387b827ec9c0bf7ca42e3fb9114b8fa9a8671bbc9d10877670b9",
-      "policy_sha256": "b75c4ac841d7b4bcc57f7a9c8417ca2317d8ecfa499e72a9af8a8591a2ec0d35",
-      "provider_map_sha256": "f2b69046b6a740fd9532d2d88e7fabae7c20fb662f783c9502adc9026406f352",
-      "container_image": "ndc/mission-core-lab-v1-goose:sg3.2.0-cu117-v1",
-      "container_image_id": "sha256:591cb382c099eeb05e7ec16e2371e0b2da54d2bb5c49ec0f4ac88dbf72b0f0cd",
-      "timeline_rate_hz": 12.0,
-      "inference_rate_hz": 6.0,
-      "inference_stride": 2,
-      "inference_phase_offset_ms": 40.0,
-      "held_evidence_fail_closed": true,
-      "semantic_output_persisted": false,
-      "one_heavy_vegetation_candidate_at_a_time": true
-    }
-  },
-  "acceptance": {
-    "minimum_graph_world_state_fps": 11.209069,
-    "minimum_vegetation_timeline_fps": 11.209069,
-    "minimum_vegetation_inference_fps": 5.604534,
-    "maximum_vegetation_inference_completion_p95_ms": 125.0,
-    "maximum_semantic_evidence_source_age_ms": 125.0,
-    "maximum_combined_output_age_p99_ms": 125.0,
-    "capacity_drop_count_max": 0,
-    "unaccounted_frame_count_max": 0
-  },
-  "telemetry": {
-    "sample_interval_seconds": 1.0,
-    "required_roles": [
-      "graph",
-      "triton",
-      "tgs",
-      "vegetation"
-    ]
-  },
-  "invariants": {
-    "raw_fisheye_immutable": true,
-    "reference_graph_parameters_unchanged": true,
-    "tgs_parameters_unchanged": true,
-    "ddrnet_parameters_unchanged": true,
-    "safety_layers_remain_12hz": true,
-    "vegetation_gpu_phase_follows_safety_detector": true,
-    "held_semantic_evidence_is_advisory_only": true,
-    "vegetation_source_buffer_bounded": true,
-    "vegetation_full_route_rgb_prefetch_allowed": false,
-    "ppliteseg_concurrent_run_allowed": false,
-    "camera_semantics_can_clear_rigid_geometry": false,
-    "canonical_triton_mutation_allowed": false,
-    "runtime_shared_source_frame_target": true,
-    "gauss_or_playcanvas_in_scope": false
-  },
-  "authority": {
-    "visual_quality_accepted": false,
-    "route_truth_available": false,
-    "traversability_accepted": false,
-    "physical_free_space_accepted": false,
-    "commands_enabled": false,
-    "actuation_allowed": false,
-    "navigation_or_safety_accepted": false,
-    "production_accepted": false
-  }
-}

config/perception/lab-v1-vegetation-integrated-multirate-shadow-v2.json

@@ -1,80 +0,0 @@
-{
-  "schema_version": "missioncore.lab-v1-vegetation-integrated-shadow-profile/v2",
-  "profile_id": "lab-v1-ravnoves00-ddrnet-m49-integrated-multirate-shadow/v2",
-  "source": {
-    "source_id": "RAVNOVES00",
-    "expected_timeline_frames": 4489,
-    "requested_source_rate_hz": 12.0,
-    "shared_start_barrier": true,
-    "ground_truth_available": false
-  },
-  "stages": {
-    "m49_graph_tgs": {
-      "profile": "m49-tgs-integrated-graph-shadow-v1.json",
-      "profile_sha256": "b61e018b2d04eec58802e2d4186ce7a3dd3a15b254db106b57b609e903eeef80",
-      "candidate": "frozen-native-rf-detr-plus-cpu-tgs",
-      "parameters_unchanged": true,
-      "timeline_rate_hz": 12.0
-    },
-    "vegetation": {
-      "candidate_id": "ddrnet_39-goose-fine-64",
-      "candidate_key": "ddrnet",
-      "checkpoint_sha256": "b99c2838051bcd7b092fd3970aa62a77d5c0bbb809c9b9afb2ff4b0ebdaa4ee6",
-      "config_sha256": "96a427a8baae387b827ec9c0bf7ca42e3fb9114b8fa9a8671bbc9d10877670b9",
-      "policy_sha256": "b75c4ac841d7b4bcc57f7a9c8417ca2317d8ecfa499e72a9af8a8591a2ec0d35",
-      "provider_map_sha256": "f2b69046b6a740fd9532d2d88e7fabae7c20fb662f783c9502adc9026406f352",
-      "container_image": "ndc/mission-core-lab-v1-goose:sg3.2.0-cu117-v1",
-      "container_image_id": "sha256:591cb382c099eeb05e7ec16e2371e0b2da54d2bb5c49ec0f4ac88dbf72b0f0cd",
-      "timeline_rate_hz": 12.0,
-      "inference_rate_hz": 6.0,
-      "inference_stride": 2,
-      "held_evidence_fail_closed": true,
-      "semantic_output_persisted": false,
-      "one_heavy_vegetation_candidate_at_a_time": true
-    }
-  },
-  "acceptance": {
-    "minimum_graph_world_state_fps": 11.209069,
-    "minimum_vegetation_timeline_fps": 11.209069,
-    "minimum_vegetation_inference_fps": 5.604534,
-    "maximum_vegetation_inference_completion_p95_ms": 125.0,
-    "maximum_semantic_evidence_source_age_ms": 125.0,
-    "maximum_combined_output_age_p99_ms": 125.0,
-    "capacity_drop_count_max": 0,
-    "unaccounted_frame_count_max": 0
-  },
-  "telemetry": {
-    "sample_interval_seconds": 1.0,
-    "required_roles": [
-      "graph",
-      "triton",
-      "tgs",
-      "vegetation"
-    ]
-  },
-  "invariants": {
-    "raw_fisheye_immutable": true,
-    "reference_graph_parameters_unchanged": true,
-    "tgs_parameters_unchanged": true,
-    "ddrnet_parameters_unchanged": true,
-    "safety_layers_remain_12hz": true,
-    "held_semantic_evidence_is_advisory_only": true,
-    "vegetation_source_buffer_bounded": true,
-    "vegetation_full_route_rgb_prefetch_allowed": false,
-    "ppliteseg_concurrent_run_allowed": false,
-    "camera_semantics_can_clear_rigid_geometry": false,
-    "canonical_triton_mutation_allowed": false,
-    "runtime_shared_source_frame_target": true,
-    "gauss_or_playcanvas_in_scope": false
-  },
-  "authority": {
-    "visual_quality_accepted": false,
-    "route_truth_available": false,
-    "traversability_accepted": false,
-    "physical_free_space_accepted": false,
-    "commands_enabled": false,
-    "actuation_allowed": false,
-    "navigation_or_safety_accepted": false,
-    "production_accepted": false
-  }
-}

config/perception/lab-v1-vegetation-integrated-shadow-v1.json

@@ -1,71 +0,0 @@
-{
-  "schema_version": "missioncore.lab-v1-vegetation-integrated-shadow-profile/v1",
-  "profile_id": "lab-v1-ravnoves00-ddrnet-m49-integrated-shadow/v1",
-  "source": {
-    "source_id": "RAVNOVES00",
-    "expected_timeline_frames": 4489,
-    "requested_source_rate_hz": 12.0,
-    "shared_start_barrier": true,
-    "ground_truth_available": false
-  },
-  "stages": {
-    "m49_graph_tgs": {
-      "profile": "m49-tgs-integrated-graph-shadow-v1.json",
-      "profile_sha256": "b61e018b2d04eec58802e2d4186ce7a3dd3a15b254db106b57b609e903eeef80",
-      "candidate": "frozen-native-rf-detr-plus-cpu-tgs",
-      "parameters_unchanged": true
-    },
-    "vegetation": {
-      "candidate_id": "ddrnet_39-goose-fine-64",
-      "candidate_key": "ddrnet",
-      "checkpoint_sha256": "b99c2838051bcd7b092fd3970aa62a77d5c0bbb809c9b9afb2ff4b0ebdaa4ee6",
-      "config_sha256": "96a427a8baae387b827ec9c0bf7ca42e3fb9114b8fa9a8671bbc9d10877670b9",
-      "policy_sha256": "b75c4ac841d7b4bcc57f7a9c8417ca2317d8ecfa499e72a9af8a8591a2ec0d35",
-      "provider_map_sha256": "f2b69046b6a740fd9532d2d88e7fabae7c20fb662f783c9502adc9026406f352",
-      "container_image": "ndc/mission-core-lab-v1-goose:sg3.2.0-cu117-v1",
-      "container_image_id": "sha256:591cb382c099eeb05e7ec16e2371e0b2da54d2bb5c49ec0f4ac88dbf72b0f0cd",
-      "semantic_output_persisted": false,
-      "one_heavy_vegetation_candidate_at_a_time": true
-    }
-  },
-  "acceptance": {
-    "minimum_graph_world_state_fps": 11.209069,
-    "minimum_vegetation_fps": 11.209069,
-    "maximum_vegetation_completion_p95_ms": 125.0,
-    "maximum_combined_output_age_p99_ms": 125.0,
-    "capacity_drop_count_max": 0,
-    "unaccounted_frame_count_max": 0
-  },
-  "telemetry": {
-    "sample_interval_seconds": 1.0,
-    "required_roles": [
-      "graph",
-      "triton",
-      "tgs",
-      "vegetation"
-    ]
-  },
-  "invariants": {
-    "raw_fisheye_immutable": true,
-    "reference_graph_parameters_unchanged": true,
-    "tgs_parameters_unchanged": true,
-    "ddrnet_parameters_unchanged": true,
-    "vegetation_source_buffer_bounded": true,
-    "vegetation_full_route_rgb_prefetch_allowed": false,
-    "ppliteseg_concurrent_run_allowed": false,
-    "camera_semantics_can_clear_rigid_geometry": false,
-    "canonical_triton_mutation_allowed": false,
-    "runtime_shared_source_frame_target": true,
-    "gauss_or_playcanvas_in_scope": false
-  },
-  "authority": {
-    "visual_quality_accepted": false,
-    "route_truth_available": false,
-    "traversability_accepted": false,
-    "physical_free_space_accepted": false,
-    "commands_enabled": false,
-    "actuation_allowed": false,
-    "navigation_or_safety_accepted": false,
-    "production_accepted": false
-  }
-}

config/perception/mixed-route-tgs-review-v1.json

@@ -1,68 +0,0 @@
-{
-  "schema_version": "missioncore.mixed-route-tgs-review-profile/v1",
-  "profile_id": "ravnoves004tree-mixed-route-tgs-review/v1",
-  "source": {
-    "source_id": "RAVNOVES004TREE",
-    "session_id": "20260828T130511Z_viewer_live",
-    "review_pack_id": "mixed-route-review-pack-a8d245eb08a9581a994c4ae5ad242fec20f02c7c512c5ca5d3a6dd9464012753",
-    "source_pack_id": "mixed-route-lidar-pack-e3fe195588cc4a2ec17e15af6f46582ed71c9bed643943779c4ed5e565a3c839",
-    "source_pack_sha256": "10c759463da7711fbbe67e70df931597d85ab21325f7f8026e2c945b677e1bc6",
-    "input_coordinate_frame": "map-gravity-local-translation-only"
-  },
-  "tgs": {
-    "max_range_m": 80.0,
-    "min_range_m": 1.0,
-    "resolution_m": 8.0,
-    "num_iterations": 3,
-    "num_lowest_representative_points": 5,
-    "minimum_points": 10,
-    "seed_threshold_m": 0.5,
-    "distance_threshold_m": 0.125,
-    "outlier_threshold_m": 0.3,
-    "normal_threshold": 0.94,
-    "weight_threshold": 200.0,
-    "lcc_normal_similarity": 0.03,
-    "lcc_planar_distance_m": 0.1,
-    "obstacle_height_m": 1.0,
-    "refine_mode": true
-  },
-  "profiles": {
-    "current_increment": {
-      "role": "diagnostic-current-evidence"
-    },
-    "causal_rolling_1s": {
-      "role": "primary-local-evidence",
-      "history_seconds": 1.0,
-      "local_radius_m": 12.0
-    }
-  },
-  "costmap": {
-    "coordinate_frame": "map-gravity-local",
-    "cell_size_m": 0.45,
-    "radius_m": 12.0,
-    "state_priority": [
-      "NONGROUND_OCCUPIED",
-      "UNKNOWN_REJECTED",
-      "GROUND_SUPPORT",
-      "UNOBSERVED"
-    ]
-  },
-  "state_codes": {
-    "UNOBSERVED": 0,
-    "GROUND_SUPPORT": 1,
-    "NONGROUND_OCCUPIED": 2,
-    "UNKNOWN_REJECTED": 3
-  },
-  "invariants": {
-    "all_eligible_input_points_accounted": true,
-    "aos_allowed": false,
-    "lidar_orientation_applied_to_tgs_input": false,
-    "map_gravity_axis_preserved": true,
-    "missing_support_means_free": false,
-    "unobserved_cells_are_emitted": true,
-    "camera_projection_is_authoritative": false,
-    "future_frames_used": false,
-    "gpu_allowed": false,
-    "navigation_or_actuation_allowed": false
-  }
-}

experiments/perception/prepare_mixed_route_lidar_pack.py

@@ -1,418 +0,0 @@
-#!/usr/bin/env python3
-"""Publish LiDAR/pose evidence aligned to an immutable mixed-route review pack."""
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import os
-import shutil
-import tempfile
-from datetime import UTC, datetime
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-from fuse_e6_tracking_lidar import CameraAnchor, _lidar_samples
-
-from k1link.compute.jobs import validate_camera_compute_job
-from k1link.device_plugins.xgrids_k1.analyze.calibrated_overlay import (
-    _load_calibration_snapshot,
-)
-from k1link.device_plugins.xgrids_k1.analyze.calibrated_projection import (
-    Kb4ProjectionProfile,
-)
-from k1link.device_plugins.xgrids_k1.mqtt.capture import read_capture_clock_origin
-from k1link.device_plugins.xgrids_k1.protocol.streams import decode_lio_pcl
-from k1link.device_plugins.xgrids_k1.viewer.replay import iter_replay_messages
-
-SCHEMA = "missioncore.mixed-route-lidar-pack/v1"
-REVIEW_SCHEMA = "missioncore.mixed-route-review-pack/v1"
-MAXIMUM_LIDAR_CAMERA_DELTA_MS = 100.0
-MAXIMUM_POSE_POINT_DELTA_MS = 100.0
-CAUSAL_HISTORY_SECONDS = 1.0
-
-
-class MixedRouteLidarPackError(RuntimeError):
-    """The recorded route cannot satisfy the selected LiDAR evidence contract."""
-
-
-def _canonical_json(value: object) -> bytes:
-    return json.dumps(
-        value,
-        ensure_ascii=False,
-        sort_keys=True,
-        separators=(",", ":"),
-        allow_nan=False,
-    ).encode("utf-8")
-
-
-def _sha256(path: Path) -> str:
-    digest = hashlib.sha256()
-    with path.open("rb") as stream:
-        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
-            digest.update(chunk)
-    return digest.hexdigest()
-
-
-def _arguments() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--job", type=Path, required=True)
-    parser.add_argument("--session", type=Path, required=True)
-    parser.add_argument("--review-pack", type=Path, required=True)
-    parser.add_argument("--calibration", type=Path, required=True)
-    parser.add_argument("--output-root", type=Path, required=True)
-    return parser.parse_args()
-
-
-def _read_review_pack(root: Path) -> tuple[dict[str, Any], list[dict[str, Any]]]:
-    resolved = root.resolve(strict=True)
-    manifest_path = resolved / "manifest.json"
-    try:
-        manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
-    except (OSError, json.JSONDecodeError) as exc:
-        raise MixedRouteLidarPackError("mixed-route review manifest is invalid") from exc
-    identity = manifest.get("identity") if isinstance(manifest, dict) else None
-    timeline = manifest.get("timeline") if isinstance(manifest, dict) else None
-    frames = manifest.get("frames") if isinstance(manifest, dict) else None
-    if (
-        manifest.get("schema_version") != REVIEW_SCHEMA
-        or not isinstance(identity, dict)
-        or identity.get("schema_version") != REVIEW_SCHEMA
-        or identity.get("ground_truth") is not False
-        or not isinstance(timeline, dict)
-        or not isinstance(frames, list)
-        or manifest.get("frame_count") != len(frames)
-        or not frames
-    ):
-        raise MixedRouteLidarPackError("mixed-route review contract changed")
-    timeline_path = resolved / str(timeline.get("path"))
-    if (
-        not timeline_path.is_file()
-        or timeline.get("sha256") != _sha256(timeline_path)
-        or timeline.get("byte_length") != timeline_path.stat().st_size
-    ):
-        raise MixedRouteLidarPackError("mixed-route review timeline changed")
-    rows: list[dict[str, Any]] = []
-    previous_seconds = -1.0
-    with timeline_path.open(encoding="utf-8") as stream:
-        for expected, line in enumerate(stream):
-            try:
-                row = json.loads(line)
-            except json.JSONDecodeError as exc:
-                raise MixedRouteLidarPackError("mixed-route timeline JSON is invalid") from exc
-            seconds = row.get("session_seconds") if isinstance(row, dict) else None
-            if (
-                not isinstance(row, dict)
-                or row.get("frame_index") != expected
-                or row.get("sequence") != expected + 1
-                or row.get("source_sequence") != row.get("source_frame_index") + 1
-                or not isinstance(seconds, (int, float))
-                or isinstance(seconds, bool)
-                or float(seconds) <= previous_seconds
-            ):
-                raise MixedRouteLidarPackError("mixed-route timeline row changed")
-            rows.append(row)
-            previous_seconds = float(seconds)
-    if len(rows) != len(frames):
-        raise MixedRouteLidarPackError("mixed-route timeline is incomplete")
-    for frame in frames:
-        path = resolved / str(frame.get("path"))
-        if (
-            not path.is_file()
-            or frame.get("byte_length") != path.stat().st_size
-            or frame.get("sha256") != _sha256(path)
-        ):
-            raise MixedRouteLidarPackError("mixed-route source frame changed")
-    return manifest, rows
-
-
-def _causal_history_clouds(
-    raw_path: Path,
-    *,
-    origin_monotonic_ns: int,
-    sample_seconds: list[float],
-) -> list[np.ndarray]:
-    grouped: list[list[np.ndarray]] = [[] for _ in sample_seconds]
-    last = sample_seconds[-1]
-    for message in iter_replay_messages(raw_path):
-        monotonic_ns = message.received_monotonic_ns
-        if not isinstance(monotonic_ns, int) or monotonic_ns < origin_monotonic_ns:
-            raise MixedRouteLidarPackError("MQTT replay message has no compatible clock")
-        seconds = (monotonic_ns - origin_monotonic_ns) / 1e9
-        if seconds > last:
-            break
-        if not message.topic.endswith("/lio_pcl"):
-            continue
-        matching = [
-            index
-            for index, sample_time in enumerate(sample_seconds)
-            if sample_time - CAUSAL_HISTORY_SECONDS <= seconds <= sample_time
-        ]
-        if not matching:
-            continue
-        frame = decode_lio_pcl(message.payload)
-        cloud = np.asarray(
-            [point.scaled_xyz(frame.header.scaler) for point in frame.points],
-            dtype=np.float32,
-        ).reshape((-1, 3))
-        if cloud.shape[0] == 0 or not np.isfinite(cloud).all():
-            raise MixedRouteLidarPackError("causal LiDAR history is empty or non-finite")
-        for index in matching:
-            grouped[index].append(cloud)
-    result: list[np.ndarray] = []
-    for clouds in grouped:
-        if not clouds:
-            raise MixedRouteLidarPackError("selected frame has no causal LiDAR history")
-        result.append(np.concatenate(clouds))
-    return result
-
-
-def prepare(
-    *,
-    job_root: Path,
-    session_root: Path,
-    review_pack_root: Path,
-    calibration_root: Path,
-    output_root: Path,
-) -> Path:
-    job = validate_camera_compute_job(job_root)
-    session = session_root.resolve(strict=True)
-    if not session.is_dir() or session.name != job.session_id:
-        raise MixedRouteLidarPackError("camera job and observation session differ")
-    review, timeline = _read_review_pack(review_pack_root)
-    review_identity = review["identity"]
-    if (
-        review_identity.get("job_id") != job.job_id
-        or review_identity.get("input_sha256") != job.input_sha256
-        or review_identity.get("session_id") != job.session_id
-        or review_identity.get("source_id") != job.source_id
-        or review_identity.get("codec_epoch") != job.codec_epoch
-    ):
-        raise MixedRouteLidarPackError("review pack and camera job differ")
-
-    calibration, calibration_sha256 = _load_calibration_snapshot(
-        calibration_root.resolve(strict=True)
-    )
-    projection = Kb4ProjectionProfile.from_factory_calibration(calibration, job.source_id)
-    capture_root = session / "captures" / "mqtt_live"
-    origin_path = capture_root / "mqtt.timeline.origin.json"
-    origin = read_capture_clock_origin(origin_path)
-    anchors = [
-        CameraAnchor(
-            frame_index=int(row["frame_index"]),
-            source_frame_index=int(row["source_frame_index"]),
-            host_session_seconds=(
-                int(row["host_monotonic_ns"]) - origin.started_monotonic_ns
-            )
-            / 1e9,
-            video_session_seconds=float(row["session_seconds"]),
-        )
-        for row in timeline
-    ]
-    if any(
-        anchor.host_session_seconds != anchor.video_session_seconds
-        for anchor in anchors
-    ):
-        raise MixedRouteLidarPackError("review timeline does not use host arrival time")
-    samples = list(
-        _lidar_samples(
-            capture_root / "mqtt.raw.k1mqtt",
-            anchors,
-            origin_monotonic_ns=origin.started_monotonic_ns,
-            maximum_lidar_camera_delta_s=MAXIMUM_LIDAR_CAMERA_DELTA_MS / 1000.0,
-            maximum_pose_point_delta_s=MAXIMUM_POSE_POINT_DELTA_MS / 1000.0,
-        )
-    )
-    if len(samples) != len(anchors):
-        raise MixedRouteLidarPackError("LiDAR sampler did not account for every anchor")
-
-    count = len(anchors)
-    available = np.zeros((count,), dtype=np.bool_)
-    offsets = [0]
-    clouds: list[np.ndarray] = []
-    positions = np.full((count, 3), np.nan, dtype=np.float64)
-    quaternions = np.full((count, 4), np.nan, dtype=np.float64)
-    lidar_delta = np.full((count,), np.nan, dtype=np.float64)
-    pose_delta = np.full((count,), np.nan, dtype=np.float64)
-    sample_seconds: list[float] = []
-    for index, (anchor, sample) in enumerate(zip(anchors, samples, strict=True)):
-        if sample is None:
-            offsets.append(offsets[-1])
-            sample_seconds.append(float("nan"))
-            continue
-        cloud = np.asarray(
-            [
-                point.scaled_xyz(sample.point_frame.header.scaler)
-                for point in sample.point_frame.points
-            ],
-            dtype=np.float32,
-        ).reshape((-1, 3))
-        if cloud.shape[0] == 0 or not np.isfinite(cloud).all():
-            raise MixedRouteLidarPackError("selected LiDAR sample is empty or non-finite")
-        available[index] = True
-        clouds.append(cloud)
-        offsets.append(offsets[-1] + cloud.shape[0])
-        positions[index] = sample.pose_frame.position_xyz
-        quaternions[index] = sample.pose_frame.orientation_xyzw
-        lidar_delta[index] = (
-            sample.point_session_seconds - anchor.host_session_seconds
-        ) * 1000.0
-        pose_delta[index] = (
-            sample.pose_session_seconds - sample.point_session_seconds
-        ) * 1000.0
-        sample_seconds.append(sample.point_session_seconds)
-
-    if not available.all() or not np.isfinite(np.asarray(sample_seconds)).all():
-        raise MixedRouteLidarPackError(
-            "every mixed-route review island must have a temporally admissible LiDAR sample"
-        )
-    history_clouds = _causal_history_clouds(
-        capture_root / "mqtt.raw.k1mqtt",
-        origin_monotonic_ns=origin.started_monotonic_ns,
-        sample_seconds=sample_seconds,
-    )
-    history_offsets = [0]
-    for cloud in history_clouds:
-        history_offsets.append(history_offsets[-1] + cloud.shape[0])
-
-    identity = {
-        "schema_version": SCHEMA,
-        "job_id": job.job_id,
-        "input_sha256": job.input_sha256,
-        "session_id": job.session_id,
-        "source_id": job.source_id,
-        "camera_slot": "camera_1",
-        "calibration_sha256": calibration_sha256,
-        "review_pack_id": review["pack_id"],
-        "review_pack_identity_sha256": review["identity_sha256"],
-        "selected_source_frame_indices": [
-            int(row["source_frame_index"]) for row in timeline
-        ],
-        "frame_count": count,
-        "available_lidar_frames": int(available.sum()),
-        "point_count": int(offsets[-1]),
-        "causal_history_seconds": CAUSAL_HISTORY_SECONDS,
-        "causal_history_point_count": int(history_offsets[-1]),
-        "temporal_policy": {
-            "binding": "nearest-host-arrival-best-effort",
-            "maximum_lidar_camera_delta_ms": MAXIMUM_LIDAR_CAMERA_DELTA_MS,
-            "maximum_pose_point_delta_ms": MAXIMUM_POSE_POINT_DELTA_MS,
-            "clock_source": "recorded-host-monotonic-arrival",
-        },
-        "projection": {
-            "model": "kb4",
-            "width": projection.width,
-            "height": projection.height,
-            "source_coordinates": "k1-map",
-            "target_camera": job.source_id,
-        },
-        "ground_truth": False,
-        "authority": {
-            "navigation_or_safety_accepted": False,
-            "actuation_allowed": False,
-        },
-        "producer_sha256": _sha256(Path(__file__).resolve(strict=True)),
-    }
-    identity_sha256 = hashlib.sha256(_canonical_json(identity)).hexdigest()
-    pack_id = f"mixed-route-lidar-pack-{identity_sha256}"
-    parent = output_root.resolve()
-    parent.mkdir(mode=0o700, parents=True, exist_ok=True)
-    final = parent / pack_id
-    if final.exists():
-        return final
-    staging = Path(tempfile.mkdtemp(prefix=f".{pack_id}.", dir=parent))
-    published = False
-    try:
-        arrays_path = staging / "lidar-pack.npz"
-        np.savez_compressed(
-            arrays_path,
-            frame_indices=np.arange(count, dtype=np.int64),
-            source_frame_indices=np.asarray(
-                [row["source_frame_index"] for row in timeline], dtype=np.int64
-            ),
-            session_seconds=np.asarray(
-                [anchor.video_session_seconds for anchor in anchors], dtype=np.float64
-            ),
-            host_session_seconds=np.asarray(
-                [anchor.host_session_seconds for anchor in anchors], dtype=np.float64
-            ),
-            lidar_session_seconds=np.asarray(sample_seconds, dtype=np.float64),
-            sample_available=available,
-            cloud_offsets=np.asarray(offsets, dtype=np.int64),
-            cloud_points_map=(
-                np.concatenate(clouds) if clouds else np.empty((0, 3), dtype=np.float32)
-            ),
-            pose_positions_map=positions,
-            pose_quaternions_map_from_lidar=quaternions,
-            lidar_camera_delta_ms=lidar_delta,
-            pose_point_delta_ms=pose_delta,
-            causal_history_seconds=np.asarray(
-                [CAUSAL_HISTORY_SECONDS], dtype=np.float64
-            ),
-            causal_history_offsets=np.asarray(history_offsets, dtype=np.int64),
-            causal_history_points_map=np.concatenate(history_clouds),
-            intrinsic_fx_fy_cx_cy=np.asarray(
-                projection.intrinsic_fx_fy_cx_cy, dtype=np.float64
-            ),
-            distortion_kb4=np.asarray(projection.distortion_kb4, dtype=np.float64),
-            t_camera_from_lidar=np.asarray(projection.t_camera_from_lidar, dtype=np.float64),
-        )
-        manifest = {
-            "schema_version": SCHEMA,
-            "pack_id": pack_id,
-            "identity_sha256": identity_sha256,
-            "identity": identity,
-            "created_at_utc": datetime.now(UTC)
-            .isoformat(timespec="milliseconds")
-            .replace("+00:00", "Z"),
-            "classification": "private-recorded-sensor-review-input",
-            "ground_truth": False,
-            "artifact": {
-                "path": arrays_path.name,
-                "media_type": "application/x-npz",
-                "byte_length": arrays_path.stat().st_size,
-                "sha256": _sha256(arrays_path),
-            },
-        }
-        (staging / "manifest.json").write_text(
-            json.dumps(manifest, ensure_ascii=False, sort_keys=True, indent=2) + "\n",
-            encoding="utf-8",
-        )
-        os.replace(staging, final)
-        published = True
-    finally:
-        if not published:
-            shutil.rmtree(staging, ignore_errors=True)
-    return final
-
-
-def main() -> int:
-    args = _arguments()
-    output = prepare(
-        job_root=args.job,
-        session_root=args.session,
-        review_pack_root=args.review_pack,
-        calibration_root=args.calibration,
-        output_root=args.output_root,
-    )
-    manifest = json.loads((output / "manifest.json").read_text(encoding="utf-8"))
-    print(
-        json.dumps(
-            {
-                "pack_id": manifest["pack_id"],
-                "output": str(output),
-                "frames": manifest["identity"]["frame_count"],
-                "lidar_frames": manifest["identity"]["available_lidar_frames"],
-                "points": manifest["identity"]["point_count"],
-                "artifact_sha256": manifest["artifact"]["sha256"],
-            },
-            sort_keys=True,
-        )
-    )
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

experiments/perception/worker/Invoke-E4FullSessionSegmentation.ps1

@@ -187,15 +187,12 @@ Write-Output "PHASE=e4-preflight-complete"
 $runToken = [Guid]::NewGuid().ToString("N")
 $workRoot = Join-Path $tmpRoot ("{0}-e4-{1}" -f $job.job_id, $runToken)
 $framesRoot = Join-Path $workRoot "frames"
-$decodedFramesRoot = Join-Path $workRoot "decoded-by-pts"
 $streamPath = Join-Path $workRoot "camera.mp4"
-$packetsPath = Join-Path $workRoot "packets.csv"
-$decodeRepairPath = Join-Path $workRoot "decode-repair.json"
+$ptsPath = Join-Path $workRoot "pts.json"
 $timelinePath = Join-Path $workRoot "timeline.jsonl"
 $publishRoot = Join-Path $derivedRoot (".{0}-e4-{1}.publish" -f $job.job_id, $runToken)
 $stagingRoot = Join-Path $publishRoot "output"
 $null = New-Item -ItemType Directory -Path $framesRoot
-$null = New-Item -ItemType Directory -Path $decodedFramesRoot
 $null = New-Item -ItemType Directory -Path $publishRoot
 $totalWatch = [Diagnostics.Stopwatch]::StartNew()
 $completed = $false
@@ -233,69 +230,29 @@ try {
 
     $extractWatch = [Diagnostics.Stopwatch]::StartNew()
     Write-Output "PHASE=e4-frame-extraction-start"
-    & ffprobe -v error -select_streams v:0 -show_packets -show_entries packet=pts,flags -of csv=p=0 -o $packetsPath $streamPath
-    Assert-LastExitCode "LAB E4 packet timestamp probe"
-    $packetRows = @(Get-Content -LiteralPath $packetsPath | Select-Object -First $activeFrameCount)
-    if ($packetRows.Count -ne $activeFrameCount) {
-        throw "LAB E4 packet count differs from the requested camera epoch"
-    }
-
-    & ffmpeg -hide_banner -loglevel error `
-        -hwaccel cuda -hwaccel_output_format cuda -c:v h264_cuvid `
-        -err_detect ignore_err -flags +output_corrupt -copyts `
-        -i $streamPath -map 0:v:0 -vf "hwdownload,format=nv12" `
-        -fps_mode passthrough -enc_time_base demux -frames:v $activeFrameCount `
-        -frame_pts 1 (Join-Path $decodedFramesRoot "frame-%d.png")
+    & ffmpeg -hide_banner -loglevel fatal -i $streamPath -map 0:v:0 -fps_mode passthrough -frames:v $activeFrameCount (Join-Path $framesRoot "frame-%06d.png")
     Assert-LastExitCode "LAB E4 camera extraction"
-
-    $decodedCount = @(Get-ChildItem -LiteralPath $decodedFramesRoot -File -Filter "frame-*.png").Count
-    $repairs = @()
-    $packetPts = @()
-    for ($index = 0; $index -lt $activeFrameCount; $index++) {
-        $columns = ([string]$packetRows[$index]).Split(",")
-        if ($columns.Count -lt 2) {
-            throw "LAB E4 packet timestamp row is malformed"
-        }
-        $pts = [int64]::Parse($columns[0].Trim(), [Globalization.CultureInfo]::InvariantCulture)
-        $packetPts += $pts
-        $decodedPath = Join-Path $decodedFramesRoot ("frame-{0}.png" -f $pts)
-        $canonicalPath = Join-Path $framesRoot ("frame-{0:D6}.png" -f ($index + 1))
-        if (Test-Path -LiteralPath $decodedPath -PathType Leaf) {
-            Move-Item -LiteralPath $decodedPath -Destination $canonicalPath
-            continue
-        }
-        if ($index -eq 0 -or $repairs.Count -ge 1) {
-            throw "LAB E4 source contains more than one recoverable decoder gap"
-        }
-        $previousPath = Join-Path $framesRoot ("frame-{0:D6}.png" -f $index)
-        Copy-Item -LiteralPath $previousPath -Destination $canonicalPath
-        $repairs += [ordered]@{
-            sequence = $index + 1
-            packet_pts = $pts
-            method = "duplicate-previous-decoded-frame"
-        }
-    }
+    & ffprobe -v error -select_streams v:0 -show_entries frame=best_effort_timestamp_time -of json $streamPath | Set-Content -LiteralPath $ptsPath -Encoding utf8
+    Assert-LastExitCode "LAB E4 camera timestamp probe"
 
     $decodedFrames = @(Get-ChildItem -LiteralPath $framesRoot -File -Filter "frame-*.png")
-    if ($decodedFrames.Count -ne $activeFrameCount) {
+    $ptsDocument = Get-Content -LiteralPath $ptsPath -Raw | ConvertFrom-Json
+    $pts = @($ptsDocument.frames)
+    if ($decodedFrames.Count -ne $activeFrameCount -or $pts.Count -lt $activeFrameCount) {
         throw "Decoded LAB E4 frame count differs from the requested camera epoch"
     }
-    $decodeRepair = [ordered]@{
-        schema_version = "missioncore.recorded-video-decode-repair/v1"
-        decoder = "ffmpeg-h264_cuvid-output-corrupt"
-        packets_requested = $activeFrameCount
-        frames_decoded = $decodedCount
-        repaired_frame_count = $repairs.Count
-        repairs = $repairs
-    }
-    $decodeRepair | ConvertTo-Json -Depth 8 | Set-Content -LiteralPath $decodeRepairPath -Encoding utf8
-
-    $firstPacketPts = [int64]$packetPts[0]
+    $firstEpochSeconds = [double]::Parse(
+        ([string]$pts[0].best_effort_timestamp_time).Trim(),
+        [Globalization.CultureInfo]::InvariantCulture
+    )
     $previousEpochSeconds = -1.0
     $timelineWriter = [IO.StreamWriter]::new($timelinePath, $false, [Text.UTF8Encoding]::new($false))
     try {
         for ($index = 0; $index -lt $activeFrameCount; $index++) {
-            $epochSeconds = ([int64]$packetPts[$index] - $firstPacketPts) / 90000.0
+            $epochSeconds = [double]::Parse(
+                ([string]$pts[$index].best_effort_timestamp_time).Trim(),
+                [Globalization.CultureInfo]::InvariantCulture
+            ) - $firstEpochSeconds
             if ($epochSeconds -le $previousEpochSeconds -or $epochSeconds -gt ($timelineDuration + 0.001)) {
                 throw "Decoded LAB E4 timestamps are not strictly monotonic inside the camera timeline"
             }
@@ -356,7 +313,6 @@ try {
     Write-Output ("PHASE=e4-inference-start FRAMES={0}" -f $activeFrameCount)
     & docker @runArgs
     Assert-LastExitCode "LAB E4 semantic inference"
-    Copy-Item -LiteralPath $decodeRepairPath -Destination (Join-Path $stagingRoot "decode-repair.json")
     $freeBytesPostInference = Assert-FreeSpace "post-inference"
     Write-Output "PHASE=e4-inference-complete"
 

experiments/perception/worker/Invoke-LabV1VegetationGooseBenchmark.ps1

@@ -12,18 +12,7 @@ param(
 
     [string]$OutputRoot = "D:\NDC_MISSIONCORE\runtime\experiments\lab-v1-vegetation",
 
-    [string]$RavnovesVideo = "D:\NDC_MISSIONCORE\runtime\experiments\e46e\inputs\right-cadd1696ff000904eb78633a0a8418104b8024f178b91f3421789021ccb160e8.mp4",
-
-    [string]$RavnovesSourceId = "RAVNOVES00/right-cadd1696ff000904eb78633a0a8418104b8024f178b91f3421789021ccb160e8",
-
-    [string]$RavnovesSha256 = "cadd1696ff000904eb78633a0a8418104b8024f178b91f3421789021ccb160e8",
-
-    [ValidateRange(1, 1000000)]
-    [int]$RavnovesExpectedFrameCount = 4489,
-
-    [string]$RavnovesBaseM4ResultId = "m4-threat-replay-2a953c5f27f2a5b1dddc5c658c1de2c323d7796084a099c024987a1da03aa324",
-
-    [string]$RavnovesSourceProfile = ""
+    [string]$RavnovesVideo = "D:\NDC_MISSIONCORE\runtime\experiments\e46e\inputs\right-cadd1696ff000904eb78633a0a8418104b8024f178b91f3421789021ccb160e8.mp4"
 )
 
 Set-StrictMode -Version Latest
@@ -49,31 +38,6 @@ $configRoot = Join-Path $ToolRoot "config"
 $benchmarkConfig = Join-Path $configRoot "lab-v1-goose-vegetation-benchmark-v1.json"
 $policyConfig = Join-Path $configRoot "lab-v1-vegetation-mission-policy-v1.json"
 $providerMapConfig = Join-Path $configRoot "lab-v1-vegetation-provider-label-map-v1.json"
-$ravnovesProfileDocument = $null
-if (-not [string]::IsNullOrWhiteSpace($RavnovesSourceProfile)) {
-    $resolvedProfile = (Resolve-Path -LiteralPath $RavnovesSourceProfile).Path
-    if (-not $resolvedProfile.StartsWith($ToolRoot, [StringComparison]::OrdinalIgnoreCase)) {
-        throw "RAVNOVES source profile must stay under ToolRoot"
-    }
-    $ravnovesProfileDocument = Get-Content -LiteralPath $resolvedProfile -Raw | ConvertFrom-Json
-    $source = $ravnovesProfileDocument.source
-    if (
-        $ravnovesProfileDocument.schema_version -ne "missioncore.lab-v1-ravnoves-source/v1" -or
-        $null -eq $source -or
-        [string]::IsNullOrWhiteSpace([string]$source.source_id) -or
-        [string]$source.source_sha256 -notmatch "^[a-f0-9]{64}$" -or
-        [int]$source.expected_width -ne 800 -or
-        [int]$source.expected_height -ne 600 -or
-        [int]$source.expected_frame_count -lt 1 -or
-        [string]$source.crop_contract -ne "center-600-square-to-512; outside-crop-is-undefined"
-    ) {
-        throw "RAVNOVES source profile is incompatible"
-    }
-    $RavnovesSourceId = [string]$source.source_id
-    $RavnovesSha256 = [string]$source.source_sha256
-    $RavnovesExpectedFrameCount = [int]$source.expected_frame_count
-    $RavnovesBaseM4ResultId = [string]$source.base_m4_result_id
-}
 $datasetRoot = Join-Path $AssetRoot "goose-2d\validation"
 $checkpointRelative = if ($candidateKey -eq "ddrnet") {
     "models\goose\ddrnet_class_512.pth"
@@ -87,6 +51,7 @@ $expectedCheckpointSha256 = if ($candidateKey -eq "ddrnet") {
     "6dd412c0c99115e359896c4cab43a8e6bce9e09b843e7fa885fe597b0a6121cd"
 }
 $expectedCheckpointBytes = if ($candidateKey -eq "ddrnet") { 259419077 } else { 98208249 }
+$ravnovesSha256 = "cadd1696ff000904eb78633a0a8418104b8024f178b91f3421789021ccb160e8"
 $frameIndices = @(0, 253, 512, 768, 1024, 1536, 2048, 2560, 3072, 3584, 4096, 4488)
 $dockerConfig = "D:\NDC_MISSIONCORE\datasets\state\lab-v1-vegetation\docker-config"
 
@@ -145,18 +110,6 @@ function Invoke-IsolatedRun {
         [string]$FramesRoot = ""
     )
     $containerName = "ndc-lab-v1-goose-$candidateKey-$([Guid]::NewGuid().ToString('N').Substring(0, 10))"
-    $activeConfigRoot = $configRoot
-    if ($RunMode -eq "ravnoves-video" -and $null -ne $ravnovesProfileDocument) {
-        $activeConfigRoot = Join-Path $RunRoot "effective-config"
-        New-Item -ItemType Directory -Path $activeConfigRoot | Out-Null
-        Copy-Item -LiteralPath $policyConfig -Destination $activeConfigRoot
-        Copy-Item -LiteralPath $providerMapConfig -Destination $activeConfigRoot
-        $benchmark = Get-Content -LiteralPath $benchmarkConfig -Raw | ConvertFrom-Json
-        $benchmark.ravnoves = $ravnovesProfileDocument.source
-        $benchmark | ConvertTo-Json -Depth 32 | Set-Content -LiteralPath (
-            Join-Path $activeConfigRoot "lab-v1-goose-vegetation-benchmark-v1.json"
-        ) -Encoding utf8
-    }
     $visualCount = if ($RunMode -eq "ravnoves-video") { 0 } else { 12 }
     $arguments = @(
         "run", "--rm", "--name", $containerName,
@@ -172,7 +125,7 @@ function Invoke-IsolatedRun {
         "--env", "HOME=/tmp",
         "--mount", "type=bind,src=$datasetRoot,dst=/data/goose,readonly",
         "--mount", "type=bind,src=$checkpoint,dst=/models/candidate.pth,readonly",
-        "--mount", "type=bind,src=$activeConfigRoot,dst=/config,readonly",
+        "--mount", "type=bind,src=$configRoot,dst=/config,readonly",
         "--mount", "type=bind,src=$RunRoot,dst=/output",
         $image,
         "--mode", $RunMode,
@@ -194,27 +147,15 @@ function Invoke-IsolatedRun {
         $tail = @($arguments[$mountIndex..($arguments.Count - 1)])
         $arguments = $head + @("--mount", "type=bind,src=$FramesRoot,dst=/input,readonly") + $tail
     }
-    $dockerExitCode = -1
-    $previousErrorActionPreference = $ErrorActionPreference
-    try {
-        # Windows PowerShell exposes native stderr as ErrorRecord objects. Model
-        # libraries legitimately emit warnings there, so merge the stream and
-        # fail only on the native process exit code.
-        $ErrorActionPreference = "Continue"
-        & docker @arguments 2>&1 | ForEach-Object { Write-Output $_ }
-        $dockerExitCode = $LASTEXITCODE
-    }
-    finally {
-        $ErrorActionPreference = $previousErrorActionPreference
-    }
-    if ($dockerExitCode -ne 0) {
-        throw "LAB V1 container failed with exit code $dockerExitCode"
+    & docker @arguments
+    if ($LASTEXITCODE -ne 0) {
+        throw "LAB V1 container failed with exit code $LASTEXITCODE"
     }
 }
 
 function Export-RavnovesFrames {
     param([string]$Destination)
-    Assert-FileIdentity -Path $RavnovesVideo -ExpectedBytes (Get-Item -LiteralPath $RavnovesVideo).Length -ExpectedSha256 $RavnovesSha256
+    Assert-FileIdentity -Path $RavnovesVideo -ExpectedBytes (Get-Item -LiteralPath $RavnovesVideo).Length -ExpectedSha256 $ravnovesSha256
     New-Item -ItemType Directory -Path $Destination | Out-Null
     $expression = ($frameIndices | ForEach-Object { "eq(n\,$_ )" }) -join "+"
     $temporaryPattern = Join-Path $Destination "selected-%03d.png"
@@ -234,68 +175,14 @@ function Export-RavnovesFrames {
 
 function Export-RavnovesVideoFrames {
     param([string]$Destination)
-    Assert-FileIdentity -Path $RavnovesVideo -ExpectedBytes (Get-Item -LiteralPath $RavnovesVideo).Length -ExpectedSha256 $RavnovesSha256
+    Assert-FileIdentity -Path $RavnovesVideo -ExpectedBytes (Get-Item -LiteralPath $RavnovesVideo).Length -ExpectedSha256 $ravnovesSha256
     New-Item -ItemType Directory -Path $Destination | Out-Null
-    $decodedRoot = "{0}-decoded-by-pts" -f $Destination
-    $packetsPath = "{0}-packets.csv" -f $Destination
-    New-Item -ItemType Directory -Path $decodedRoot | Out-Null
-    & ffprobe -v error -select_streams v:0 -show_packets -show_entries packet=pts,flags -of csv=p=0 -o $packetsPath $RavnovesVideo
-    if ($LASTEXITCODE -ne 0) {
-        throw "RAVNOVES full-video packet probe failed"
-    }
-    $packetRows = @(Get-Content -LiteralPath $packetsPath | Select-Object -First $RavnovesExpectedFrameCount)
-    if ($packetRows.Count -ne $RavnovesExpectedFrameCount) {
-        throw "RAVNOVES full-video packet sequence changed"
-    }
-    & ffmpeg -hide_banner -loglevel error `
-        -hwaccel cuda -hwaccel_output_format cuda -c:v h264_cuvid `
-        -err_detect ignore_err -flags +output_corrupt -copyts `
-        -i $RavnovesVideo -map 0:v:0 -vf "hwdownload,format=nv12" `
-        -fps_mode passthrough -enc_time_base demux -frames:v $RavnovesExpectedFrameCount `
-        -frame_pts 1 (Join-Path $decodedRoot "frame-%d.png")
+    & ffmpeg -hide_banner -loglevel error -i $RavnovesVideo -map 0:v:0 -fps_mode passthrough (Join-Path $Destination "frame-%06d.png")
     if ($LASTEXITCODE -ne 0) {
         throw "RAVNOVES full-video frame extraction failed"
     }
-    $decodedCount = @(Get-ChildItem -LiteralPath $decodedRoot -File -Filter "frame-*.png").Count
-    $repairs = @()
-    for ($index = 0; $index -lt $RavnovesExpectedFrameCount; $index++) {
-        $columns = ([string]$packetRows[$index]).Split(",")
-        if ($columns.Count -lt 2) {
-            throw "RAVNOVES full-video packet row is malformed"
-        }
-        $pts = [int64]::Parse($columns[0].Trim(), [Globalization.CultureInfo]::InvariantCulture)
-        $decodedPath = Join-Path $decodedRoot ("frame-{0}.png" -f $pts)
-        $canonicalPath = Join-Path $Destination ("frame-{0:D6}.png" -f ($index + 1))
-        if (Test-Path -LiteralPath $decodedPath -PathType Leaf) {
-            Move-Item -LiteralPath $decodedPath -Destination $canonicalPath
-            continue
-        }
-        if ($index -eq 0 -or $repairs.Count -ge 1) {
-            throw "RAVNOVES source contains more than one recoverable decoder gap"
-        }
-        $previousPath = Join-Path $Destination ("frame-{0:D6}.png" -f $index)
-        Copy-Item -LiteralPath $previousPath -Destination $canonicalPath
-        $repairs += [ordered]@{
-            sequence = $index + 1
-            packet_pts = $pts
-            method = "duplicate-previous-decoded-frame"
-        }
-    }
-    Remove-Item -LiteralPath $decodedRoot -Recurse -Force
-    Remove-Item -LiteralPath $packetsPath -Force
-    [ordered]@{
-        schema_version = "missioncore.recorded-video-decode-repair/v1"
-        decoder = "ffmpeg-h264_cuvid-output-corrupt"
-        packets_requested = $RavnovesExpectedFrameCount
-        frames_decoded = $decodedCount
-        repaired_frame_count = $repairs.Count
-        repairs = $repairs
-    } | ConvertTo-Json -Depth 8 | Set-Content -LiteralPath (
-        Join-Path (Split-Path $Destination -Parent) "decode-repair.json"
-    ) -Encoding utf8
     $frames = @(Get-ChildItem -LiteralPath $Destination -File -Filter "frame-*.png" | Sort-Object Name)
-    $lastFrameName = "frame-{0:D6}.png" -f $RavnovesExpectedFrameCount
-    if ($frames.Count -ne $RavnovesExpectedFrameCount -or $frames[0].Name -ne "frame-000001.png" -or $frames[-1].Name -ne $lastFrameName) {
+    if ($frames.Count -ne 4489 -or $frames[0].Name -ne "frame-000001.png" -or $frames[-1].Name -ne "frame-004489.png") {
         throw "RAVNOVES full-video frame sequence changed"
     }
 }
@@ -357,9 +244,6 @@ try {
         $framesRoot = Join-Path $runRoot "input-frames"
         Export-RavnovesVideoFrames -Destination $framesRoot
         Invoke-IsolatedRun -RunMode "ravnoves-video" -RunRoot $runRoot -Limit 0 -FramesRoot $framesRoot
-        Copy-Item -LiteralPath (Join-Path $runRoot "decode-repair.json") -Destination (
-            Join-Path $runRoot "result\decode-repair.json"
-        )
         Remove-Item -LiteralPath $framesRoot -Recurse -Force
     }
 }

experiments/perception/worker/Invoke-M49TgsIntegratedGraphShadow.ps1

@@ -12,10 +12,6 @@ param(
     [string]$RunId,
     [ValidateRange(1.0, 120.0)]
     [double]$SourceRateHz = 12.0,
-    [switch]$VegetationLoadGate,
-    [string]$VegetationAssetRoot = (
-        "D:\NDC_MISSIONCORE\datasets\vegetation-v1\observed-2026-08-27"
-    ),
     [string]$OutputRoot = (
         "D:\NDC_MISSIONCORE\runtime\results\m49-tgs-integrated-graph-shadow"
     )
@@ -27,8 +23,6 @@ $TravelImageTag = "ndc/mission-core-m49-t3-travel:20260826"
 $TravelImageId = "sha256:7b412020f4d8392d1d1ed1b33beadc44140f0ea8f781e62dd69796042334300f"
 $ParityImageTag = "ndc-mission-core-m48t-upstream-parity:1.9.4-cu130"
 $ParityImageId = "sha256:ceb13548617e4bd3f619766bfdff00af3fa5160946b367828da6d2233dcdcba0"
-$VegetationImageTag = "ndc/mission-core-lab-v1-goose:sg3.2.0-cu117-v1"
-$VegetationImageId = "sha256:591cb382c099eeb05e7ec16e2371e0b2da54d2bb5c49ec0f4ac88dbf72b0f0cd"
 $RuntimeImage = (
     "nvcr.io/nvidia/tritonserver:26.06-py3@" +
     "sha256:58df7489c3f2276f9591d500a012dee03e23d35543ce3c390b4c001e6bf90794"
@@ -104,20 +98,12 @@ function Wait-Healthy([string]$Name) {
 function Wait-SharedReady(
     [string]$GraphReady,
     [string]$TgsReady,
-    [string]$VegetationReady,
     [string]$GraphName,
-    [string]$TgsName,
-    [string]$VegetationName
+    [string]$TgsName
 ) {
     $deadline = [DateTimeOffset]::UtcNow.AddMinutes(10)
-    $requiredFiles = @($GraphReady, $TgsReady)
-    $requiredContainers = @($GraphName, $TgsName)
-    if (-not [string]::IsNullOrWhiteSpace($VegetationReady)) {
-        $requiredFiles += $VegetationReady
-        $requiredContainers += $VegetationName
-    }
-    while ($requiredFiles.Where({ -not (Test-Path -LiteralPath $_) }).Count -gt 0) {
-        foreach ($name in $requiredContainers) {
+    while (-not ((Test-Path -LiteralPath $GraphReady) -and (Test-Path -LiteralPath $TgsReady))) {
+        foreach ($name in @($GraphName, $TgsName)) {
             $container = Get-Container $name
             if (-not $container.State.Running) {
                 & docker logs $name
@@ -142,25 +128,15 @@ $runCandidate = Join-Path $output $RunId
 if (Test-Path -LiteralPath $runCandidate) { throw "M49 integrated output already exists" }
 $null = New-Item -ItemType Directory -Path $runCandidate
 $runOutput = Resolve-DDirectory $runCandidate "M49 integrated run output" $false
-foreach ($directory in @("bin", "control", "graph", "tgs", "vegetation")) {
+foreach ($directory in @("bin", "control", "graph", "tgs")) {
     $null = New-Item -ItemType Directory -Path (Join-Path $runOutput $directory)
 }
 
 $releaseDocument = Get-Content -LiteralPath (Join-Path $payload "release.json") -Raw | ConvertFrom-Json
-$expectedReleaseSchema = if ($VegetationLoadGate) {
-    "missioncore.lab-v1-vegetation-integrated-worker-release/v3"
-} else {
-    "missioncore.m49-tgs-integrated-graph-worker-release/v1"
-}
-$expectedTransition = if ($VegetationLoadGate) {
-    "lab-v1-vegetation-m49-integrated-multirate-phased-shadow/v3"
-} else {
-    "m49-tgs-native-risk-integrated-shadow/v1"
-}
 if (
-    $releaseDocument.schema_version -cne $expectedReleaseSchema -or
+    $releaseDocument.schema_version -cne "missioncore.m49-tgs-integrated-graph-worker-release/v1" -or
     $releaseDocument.worker_id -cne "worker-006" -or
-    $releaseDocument.transition -cne $expectedTransition
+    $releaseDocument.transition -cne "m49-tgs-native-risk-integrated-shadow/v1"
 ) { throw "M49 integrated release contract changed" }
 foreach ($property in $releaseDocument.files.PSObject.Properties) {
     $path = Join-Path $payload $property.Name
@@ -170,9 +146,6 @@ foreach ($property in $releaseDocument.files.PSObject.Properties) {
 }
 $wheelSha256 = [string]$releaseDocument.files."nodedc_mission_core-0.1.0-py3-none-any.whl".sha256
 $runnerSha256 = [string]$releaseDocument.files."run_m48s_reference_graph_shadow_worker.py".sha256
-$vegetationRunnerSha256 = if ($VegetationLoadGate) {
-    [string]$releaseDocument.files."run_vegetation_integrated_load.py".sha256
-} else { "" }
 
 $source = [ordered]@{
     CameraIndex = (
@@ -205,27 +178,6 @@ foreach ($entry in $source.GetEnumerator()) {
 if ((Get-Sha256 $source.SourcePack) -cne [string]$releaseDocument.source_pack_sha256) {
     throw "RAVNOVES00 source pack digest changed"
 }
-$videoSha256 = [string]$releaseDocument.video_sha256
-if ((Get-Sha256 $source.Video) -cne $videoSha256) {
-    throw "RAVNOVES00 video digest changed"
-}
-
-$vegetation = $null
-if ($VegetationLoadGate) {
-    $vegetationRoot = Resolve-DDirectory $VegetationAssetRoot "vegetation asset root" $false
-    $vegetation = [ordered]@{
-        Dataset = Resolve-DDirectory (
-            (Join-Path $vegetationRoot "goose-2d\validation")
-        ) "GOOSE validation root" $false
-        Checkpoint = Resolve-DFile (
-            (Join-Path $vegetationRoot "models\goose\ddrnet_class_512.pth")
-        ) "DDRNet checkpoint"
-    }
-    if (
-        (Get-Sha256 $vegetation.Checkpoint) -cne
-        "b99c2838051bcd7b092fd3970aa62a77d5c0bbb809c9b9afb2ff4b0ebdaa4ee6"
-    ) { throw "DDRNet checkpoint SHA-256 changed" }
-}
 
 $nativeConfig = Resolve-DFile (
     (Join-Path $payload "rf_detr_large_native_kb4_config.pbtxt")
@@ -255,17 +207,12 @@ $pillow = Resolve-DDirectory (
 
 Assert-Image $TravelImageTag $TravelImageId
 Assert-Image $ParityImageTag $ParityImageId
-if ($VegetationLoadGate) { Assert-Image $VegetationImageTag $VegetationImageId }
 & docker image inspect $RuntimeImage *> $null
 Assert-LastExitCode "pinned runtime image inspection"
 $os = Get-CimInstance Win32_OperatingSystem
 $freeMemoryGiB = [double]$os.FreePhysicalMemory / 1MB
-$requiredMemoryGiB = if ($VegetationLoadGate) { 32.0 } else { 24.0 }
-if ($freeMemoryGiB -lt $requiredMemoryGiB) {
-    throw (
-        "M49 integrated shadow requires {0:N0} GiB free memory; observed {1:N2} GiB" -f
-        $requiredMemoryGiB, $freeMemoryGiB
-    )
+if ($freeMemoryGiB -lt 24.0) {
+    throw ("M49 integrated shadow requires 24 GiB free memory; observed {0:N2} GiB" -f $freeMemoryGiB)
 }
 $canonicalBefore = Get-Container "ndc-mission-core-triton"
 if (-not $canonicalBefore.State.Running -or $canonicalBefore.State.Health.Status -cne "healthy") {
@@ -278,12 +225,9 @@ $compileName = "ndc-mission-core-m49-integrated-compile-$RunId"
 $tritonName = "ndc-mission-core-m49-integrated-triton-$RunId"
 $graphName = "ndc-mission-core-m49-integrated-graph-$RunId"
 $tgsName = "ndc-mission-core-m49-integrated-tgs-$RunId"
-$vegetationName = "ndc-mission-core-m49-integrated-vegetation-$RunId"
 $analyzeName = "ndc-mission-core-m49-integrated-analyze-$RunId"
 $evidenceName = "ndc-mission-core-m49-integrated-evidence-$RunId"
-$vegetationEvidenceName = "ndc-mission-core-m49-integrated-vegetation-evidence-$RunId"
 $containers = @($prepareName, $compileName, $tritonName, $graphName, $tgsName, $analyzeName, $evidenceName)
-if ($VegetationLoadGate) { $containers += @($vegetationName, $vegetationEvidenceName) }
 foreach ($name in $containers) {
     if (& docker ps -a --format "{{.Names}}" --filter "name=^/$name$") {
         throw "M49 integrated container name already exists: $name"
@@ -380,7 +324,7 @@ try {
 
     & docker create --name $tgsName --network none --cpus 16 --memory 24g `
         --read-only --security-opt "no-new-privileges:true" --cap-drop ALL `
-        --pids-limit 256 --tmpfs "/tmp:rw,noexec,nosuid,size=2g" `
+        --pids-limit 256 --tmpfs "/tmp:rw,noexec,nosuid,size=1g" `
         -e ("M49_SOURCE_RATE_HZ={0}" -f $rate) `
         --entrypoint /bin/bash `
         --volume ($dockerRelease + ":/release:ro") `
@@ -388,78 +332,24 @@ try {
         $TravelImageTag /release/run_tgs_integrated_shadow.sh *> $null
     Assert-LastExitCode "M49 integrated TGS creation"
 
-    if ($VegetationLoadGate) {
-        $dockerVegetationDataset = Convert-ToDockerPath $vegetation.Dataset
-        $dockerVegetationCheckpoint = Convert-ToDockerPath $vegetation.Checkpoint
-        & docker create --name $vegetationName --network none --cpus 8 --memory 10g `
-            --gpus all --read-only --security-opt "no-new-privileges:true" --cap-drop ALL `
-            --pids-limit 512 --tmpfs "/tmp:rw,noexec,nosuid,size=2g" `
-            -e "HOME=/tmp" `
-            --entrypoint conda `
-            --volume ($dockerRelease + ":/release:ro") `
-            --volume ($dockerRun + ":/shared:rw") `
-            --volume ($dockerVegetationDataset + ":/data/goose:ro") `
-            --volume ($dockerVegetationCheckpoint + ":/models/candidate.pth:ro") `
-            --volume ((Convert-ToDockerPath $source.Video) + ":/source/right.mp4:ro") `
-            $VegetationImageTag run --no-capture-output --name goose python `
-            /release/run_vegetation_integrated_load.py `
-            --config /release/lab-v1-goose-vegetation-benchmark-v1.json `
-            --policy /release/lab-v1-vegetation-mission-policy-v1.json `
-            --provider-map /release/lab-v1-vegetation-provider-label-map-v1.json `
-            --checkpoint /models/candidate.pth `
-            --dataset-root /data/goose `
-            --video /source/right.mp4 `
-            --video-sha256 $videoSha256 `
-            --runtime-video-cache /tmp/vegetation-right.mp4 `
-            --source-rate-hz $rate `
-            --inference-stride 2 `
-            --inference-phase-offset-ms 40.0 `
-            --minimum-effective-timeline-fps 11.209069 `
-            --minimum-effective-inference-fps 5.604534 `
-            --maximum-inference-completion-p95-ms 125.0 `
-            --maximum-evidence-source-age-ms 125.0 `
-            --shared-start-ready-file /shared/control/vegetation.ready `
-            --shared-start-file /shared/control/start.signal `
-            --frame-ledger /shared/vegetation/frames.jsonl `
-            --output /shared/vegetation/result.json `
-            --release-sha256 $ExpectedArtifactSha256 *> $null
-        Assert-LastExitCode "M49 integrated vegetation creation"
-    }
-
     & docker start $graphName *> $null
     Assert-LastExitCode "M49 integrated graph start"
     & docker start $tgsName *> $null
     Assert-LastExitCode "M49 integrated TGS start"
-    if ($VegetationLoadGate) {
-        & docker start $vegetationName *> $null
-        Assert-LastExitCode "M49 integrated vegetation start"
-    }
     $graphReady = Join-Path $runOutput "control\graph.ready"
     $tgsReady = Join-Path $runOutput "control\tgs.ready"
-    $vegetationReady = if ($VegetationLoadGate) {
-        Join-Path $runOutput "control\vegetation.ready"
-    } else { "" }
-    Wait-SharedReady $graphReady $tgsReady $vegetationReady $graphName $tgsName $vegetationName
+    Wait-SharedReady $graphReady $tgsReady $graphName $tgsName
     [DateTimeOffset]::UtcNow.ToString("o") | Set-Content -LiteralPath (
         Join-Path $runOutput "control\start.signal"
     ) -Encoding utf8
 
     $telemetryPath = Join-Path $runOutput "container-telemetry.jsonl"
-    $m49TelemetryPath = if ($VegetationLoadGate) {
-        Join-Path $runOutput "m49-container-telemetry.jsonl"
-    } else { $telemetryPath }
     while ($true) {
         $graphState = Get-Container $graphName
         $tgsState = Get-Container $tgsName
-        $vegetationState = if ($VegetationLoadGate) {
-            Get-Container $vegetationName
-        } else { $null }
         $running = @()
         if ($graphState.State.Running) { $running += $graphName }
         if ($tgsState.State.Running) { $running += $tgsName }
-        if ($VegetationLoadGate -and $vegetationState.State.Running) {
-            $running += $vegetationName
-        }
         if ((Get-Container $tritonName).State.Running) { $running += $tritonName }
         if ($running.Count -gt 0) {
             $stats = @((& docker stats --no-stream --format "{{json .}}" @running))
@@ -472,12 +362,10 @@ try {
                     "tgs"
                 } elseif ($value.Name -ceq $tritonName) {
                     "triton"
-                } elseif ($VegetationLoadGate -and $value.Name -ceq $vegetationName) {
-                    "vegetation"
                 } else {
                     throw "Unknown M49 telemetry container"
                 }
-                $telemetryRow = [ordered]@{
+                [ordered]@{
                     observed_utc = [DateTimeOffset]::UtcNow.ToString("o")
                     role = $role
                     name = [string]$value.Name
@@ -485,46 +373,23 @@ try {
                     memory_usage = [string]$value.MemUsage
                     memory_percent = [string]$value.MemPerc
                     pids = [string]$value.PIDs
-                } | ConvertTo-Json -Compress
-                $telemetryRow | Out-File -LiteralPath $telemetryPath -Encoding utf8 -Append
-                if ($VegetationLoadGate -and $role -cne "vegetation") {
-                    $telemetryRow | Out-File -LiteralPath $m49TelemetryPath -Encoding utf8 -Append
-                }
+                } | ConvertTo-Json -Compress | Out-File -LiteralPath $telemetryPath -Encoding utf8 -Append
             }
         }
-        $vegetationStopped = -not $VegetationLoadGate -or -not $vegetationState.State.Running
-        if (
-            -not $graphState.State.Running -and
-            -not $tgsState.State.Running -and
-            $vegetationStopped
-        ) { break }
+        if (-not $graphState.State.Running -and -not $tgsState.State.Running) { break }
         Start-Sleep -Seconds 1
     }
     $graphExit = [int](Get-Container $graphName).State.ExitCode
     $tgsExit = [int](Get-Container $tgsName).State.ExitCode
-    $vegetationExit = if ($VegetationLoadGate) {
-        [int](Get-Container $vegetationName).State.ExitCode
-    } else { 0 }
     $previousErrorAction = $ErrorActionPreference
     $ErrorActionPreference = "Continue"
     $graphLogs = & docker logs $graphName 2>&1
     $tgsLogs = & docker logs $tgsName 2>&1
-    $vegetationLogs = if ($VegetationLoadGate) {
-        & docker logs $vegetationName 2>&1
-    } else { @() }
     $ErrorActionPreference = $previousErrorAction
     $graphLogs | Set-Content -LiteralPath (Join-Path $runOutput "graph.log") -Encoding utf8
     $tgsLogs | Set-Content -LiteralPath (Join-Path $runOutput "tgs.log") -Encoding utf8
-    if ($VegetationLoadGate) {
-        $vegetationLogs | Set-Content -LiteralPath (
-            Join-Path $runOutput "vegetation.log"
-        ) -Encoding utf8
-    }
     if ($graphExit -ne 0) { throw "M49 integrated graph failed with exit code $graphExit" }
     if ($tgsExit -ne 0) { throw "M49 integrated TGS failed with exit code $tgsExit" }
-    if ($vegetationExit -ne 0) {
-        throw "M49 integrated vegetation failed with exit code $vegetationExit"
-    }
 
     & docker run --rm --name $analyzeName --network none --cpus 8 --memory 16g `
         --entrypoint python3 `
@@ -536,12 +401,6 @@ try {
         --output-root /shared/tgs/evidence
     Assert-LastExitCode "M49 integrated TGS evidence analysis"
 
-    $m49ResultPath = if ($VegetationLoadGate) {
-        "/shared/m49-result.json"
-    } else { "/shared/result.json" }
-    $dockerM49TelemetryPath = if ($VegetationLoadGate) {
-        "/shared/m49-container-telemetry.jsonl"
-    } else { "/shared/container-telemetry.jsonl" }
     & docker run --rm --name $evidenceName --network none --cpus 4 --memory 8g `
         --entrypoint python3 `
         --volume ($dockerRelease + ":/release:ro") `
@@ -552,28 +411,10 @@ try {
         --graph-frames /shared/graph/frames.jsonl `
         --tgs-result /shared/tgs/evidence/result.json `
         --tgs-timing /shared/tgs/tgs-full-timing.tsv `
-        --telemetry $dockerM49TelemetryPath `
-        --output $m49ResultPath `
+        --telemetry /shared/container-telemetry.jsonl `
+        --output /shared/result.json `
         --release-sha256 $ExpectedArtifactSha256
     Assert-LastExitCode "M49 integrated evidence gate"
-
-    if ($VegetationLoadGate) {
-        & docker run --rm --name $vegetationEvidenceName --network none --cpus 4 --memory 8g `
-            --entrypoint python3 `
-            --volume ($dockerRelease + ":/release:ro") `
-            --volume ($dockerRun + ":/shared:rw") `
-            $ParityImageTag /release/build_vegetation_integrated_graph_evidence.py `
-            --profile /release/lab-v1-vegetation-integrated-multirate-phased-shadow-v3.json `
-            --m49-result /shared/m49-result.json `
-            --graph-frames /shared/graph/frames.jsonl `
-            --tgs-timing /shared/tgs/tgs-full-timing.tsv `
-            --vegetation-result /shared/vegetation/result.json `
-            --vegetation-frames /shared/vegetation/frames.jsonl `
-            --telemetry /shared/container-telemetry.jsonl `
-            --output /shared/result.json `
-            --release-sha256 $ExpectedArtifactSha256
-        Assert-LastExitCode "M49 integrated vegetation evidence gate"
-    }
 } finally {
     foreach ($name in $containers) { Remove-ExactContainer $name }
     $canonicalAfter = Get-Container "ndc-mission-core-triton"
@@ -591,11 +432,7 @@ if (-not (Test-Path -LiteralPath $resultPath -PathType Leaf)) {
 }
 $result = Get-Content -LiteralPath $resultPath -Raw | ConvertFrom-Json
 $summary = [ordered]@{
-    schema_version = if ($VegetationLoadGate) {
-        "missioncore.lab-v1-vegetation-integrated-worker-summary/v3"
-    } else {
-        "missioncore.m49-tgs-integrated-graph-worker-summary/v1"
-    }
+    schema_version = "missioncore.m49-tgs-integrated-graph-worker-summary/v1"
     worker_id = "worker-006"
     run_id = $RunId
     code_revision = [string]$releaseDocument.code_revision
@@ -606,7 +443,6 @@ $summary = [ordered]@{
     free_memory_gib_before = [math]::Round($freeMemoryGiB, 6)
     result_id = [string]$result.result_id
     result_status = [string]$result.status
-    vegetation_load_gate = [bool]$VegetationLoadGate
     canonical_triton_id = $canonicalId
     canonical_triton_health = "healthy"
     gauss_or_playcanvas_action = "none"

experiments/perception/worker/lab_v1_vegetation_goose/run_mixed_route_ddrnet_islands.py

@@ -1,340 +0,0 @@
-#!/usr/bin/env python3
-"""Run DDRNet on an immutable mixed-route camera review pack."""
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import math
-import platform
-import statistics
-import time
-from pathlib import Path, PurePosixPath
-from typing import Any
-
-import numpy as np
-import torch
-from PIL import Image
-from run_goose_vegetation_benchmark import (
-    CLASS_COUNT,
-    expand_mask,
-    infer,
-    load_mapping,
-    load_model,
-    percentile,
-    preprocess,
-    read_json,
-    save_image,
-    sha256,
-    stable_digest,
-    validate_contracts,
-)
-
-SCHEMA = "missioncore.mixed-route-ddrnet-islands/v1"
-PACK_SCHEMA = "missioncore.mixed-route-review-pack/v1"
-AUTHORITY = {
-    "ground_truth": False,
-    "candidate_accepted": False,
-    "navigation_or_safety_accepted": False,
-    "camera_semantics_can_clear_rigid_geometry": False,
-    "actuation_allowed": False,
-}
-
-
-class MixedRouteDdrnetError(RuntimeError):
-    """The route pack or DDRNet evidence changed or is incomplete."""
-
-
-def arguments() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--pack", type=Path, required=True)
-    parser.add_argument("--config", type=Path, required=True)
-    parser.add_argument("--policy", type=Path, required=True)
-    parser.add_argument("--provider-map", type=Path, required=True)
-    parser.add_argument("--checkpoint", type=Path, required=True)
-    parser.add_argument("--dataset-root", type=Path, required=True)
-    parser.add_argument("--output", type=Path, required=True)
-    return parser.parse_args()
-
-
-def canonical_json(value: object) -> bytes:
-    return json.dumps(
-        value,
-        ensure_ascii=False,
-        sort_keys=True,
-        separators=(",", ":"),
-        allow_nan=False,
-    ).encode("utf-8")
-
-
-def object_value(value: object, label: str) -> dict[str, Any]:
-    if not isinstance(value, dict) or not all(isinstance(key, str) for key in value):
-        raise MixedRouteDdrnetError(f"{label} must be an object")
-    return value
-
-
-def load_pack(root: Path) -> tuple[dict[str, Any], list[dict[str, Any]]]:
-    pack = root.resolve(strict=True)
-    if not pack.is_dir() or pack.is_symlink():
-        raise MixedRouteDdrnetError("mixed-route review pack is unavailable")
-    manifest_path = pack / "manifest.json"
-    manifest = object_value(
-        json.loads(manifest_path.read_text(encoding="utf-8")),
-        "mixed-route manifest",
-    )
-    identity = object_value(manifest.get("identity"), "mixed-route identity")
-    identity_sha256 = manifest.get("identity_sha256")
-    frames = manifest.get("frames")
-    frame_count = manifest.get("frame_count")
-    if (
-        manifest.get("schema_version") != PACK_SCHEMA
-        or identity.get("schema_version") != PACK_SCHEMA
-        or not isinstance(identity_sha256, str)
-        or hashlib.sha256(canonical_json(identity)).hexdigest() != identity_sha256
-        or manifest.get("pack_id") != f"mixed-route-review-pack-{identity_sha256}"
-        or identity.get("ground_truth") is not False
-        or object_value(identity.get("authority"), "mixed-route authority").get(
-            "navigation_or_safety_accepted"
-        )
-        is not False
-        or not isinstance(frame_count, int)
-        or isinstance(frame_count, bool)
-        or not 1 <= frame_count <= 64
-        or not isinstance(frames, list)
-        or len(frames) != frame_count
-    ):
-        raise MixedRouteDdrnetError("mixed-route review pack identity changed")
-    timeline_descriptor = object_value(manifest.get("timeline"), "mixed-route timeline")
-    timeline_path = pack / "timeline.jsonl"
-    if (
-        timeline_descriptor.get("path") != timeline_path.name
-        or timeline_path.stat().st_size != timeline_descriptor.get("byte_length")
-        or sha256(timeline_path) != timeline_descriptor.get("sha256")
-    ):
-        raise MixedRouteDdrnetError("mixed-route timeline proof changed")
-    rows: list[dict[str, Any]] = []
-    with timeline_path.open(encoding="utf-8") as stream:
-        for expected, line in enumerate(stream):
-            row = object_value(json.loads(line), "mixed-route timeline row")
-            seconds = row.get("session_seconds")
-            if (
-                row.get("frame_index") != expected
-                or row.get("sequence") != expected + 1
-                or not isinstance(row.get("source_sequence"), int)
-                or row.get("source_frame_index") != row["source_sequence"] - 1
-                or not isinstance(seconds, (int, float))
-                or isinstance(seconds, bool)
-                or (rows and float(seconds) <= float(rows[-1]["session_seconds"]))
-            ):
-                raise MixedRouteDdrnetError("mixed-route timeline order changed")
-            rows.append(row)
-    if len(rows) != frame_count:
-        raise MixedRouteDdrnetError("mixed-route timeline is incomplete")
-    for expected, (descriptor_raw, row) in enumerate(zip(frames, rows)):  # noqa: B905
-        descriptor = object_value(descriptor_raw, "mixed-route frame descriptor")
-        relative = descriptor.get("path")
-        if relative != f"frames/frame-{expected + 1:06d}.png":
-            raise MixedRouteDdrnetError("mixed-route frame path changed")
-        pure = PurePosixPath(relative)
-        path = pack.joinpath(*pure.parts)
-        if (
-            path.is_symlink()
-            or not path.is_file()
-            or not path.resolve().is_relative_to(pack)
-            or path.stat().st_size != descriptor.get("byte_length")
-            or sha256(path) != descriptor.get("sha256")
-            or not isinstance(descriptor.get("source_segment_sha256"), str)
-            or row.get("source_sequence")
-            != identity["selected_sequences"][expected]
-        ):
-            raise MixedRouteDdrnetError("mixed-route frame proof changed")
-    return manifest, rows
-
-
-def overlay(source: Image.Image, semantic: np.ndarray, palette: np.ndarray) -> Image.Image:
-    if semantic.shape != (600, 800):
-        raise MixedRouteDdrnetError("expanded semantic mask shape changed")
-    base = source.convert("RGBA")
-    colors = Image.fromarray(palette[semantic], mode="RGBA")
-    return Image.alpha_composite(base, colors)
-
-
-def run() -> int:
-    args = arguments()
-    if not torch.cuda.is_available():
-        raise MixedRouteDdrnetError("CUDA is required for DDRNet islands")
-    if args.output.exists():
-        raise MixedRouteDdrnetError("DDRNet islands output already exists")
-    manifest, timeline = load_pack(args.pack)
-    config = read_json(args.config, "benchmark config")
-    policy = read_json(args.policy, "mission policy")
-    provider_map = read_json(args.provider_map, "provider map")
-    candidate = validate_contracts(config, policy, provider_map, "ddrnet")
-    checkpoint = args.checkpoint.resolve(strict=True)
-    if (
-        checkpoint.is_symlink()
-        or checkpoint.stat().st_size != candidate["checkpoint_size_bytes"]
-        or sha256(checkpoint) != candidate["checkpoint_sha256"]
-    ):
-        raise MixedRouteDdrnetError("DDRNet checkpoint identity changed")
-    dataset_root = args.dataset_root.resolve(strict=True)
-    mapping_path = dataset_root / config["dataset"]["mapping_relative_path"]
-    names, palette = load_mapping(mapping_path, config["dataset"]["mapping_sha256"])
-
-    args.output.mkdir(mode=0o700, parents=True, exist_ok=False)
-    mask_root = args.output / "semantic-masks"
-    overlay_root = args.output / "overlay-frames"
-    mask_root.mkdir(mode=0o700)
-    overlay_root.mkdir(mode=0o700)
-    torch.cuda.empty_cache()
-    model, model_name, architecture_failures = load_model("ddrnet", checkpoint)
-    first_path = args.pack / manifest["frames"][0]["path"]
-    with Image.open(first_path) as opened:
-        warm_source = opened.convert("RGB")
-    warm_tensor, _ = preprocess(warm_source)
-    warmup_ms = [infer(model, warm_tensor)[1] for _ in range(3)]
-    torch.cuda.reset_peak_memory_stats()
-
-    latencies_ms: list[float] = []
-    aggregate = np.zeros(CLASS_COUNT, dtype=np.int64)
-    frame_results: list[dict[str, Any]] = []
-    started = time.perf_counter()
-    for index, (descriptor, timeline_row) in enumerate(
-        zip(manifest["frames"], timeline)  # noqa: B905 - Worker image uses Python 3.9.
-    ):
-        source_path = args.pack / descriptor["path"]
-        with Image.open(source_path) as opened:
-            source = opened.convert("RGB")
-        if source.size != (800, 600):
-            raise MixedRouteDdrnetError("mixed-route source resolution changed")
-        tensor, crop_box = preprocess(source)
-        prediction, latency_ms = infer(model, tensor)
-        expanded = expand_mask(prediction, source.size, crop_box)
-        latencies_ms.append(latency_ms)
-        aggregate += np.bincount(expanded.reshape(-1), minlength=CLASS_COUNT)
-        mask_path = mask_root / f"frame-{index + 1:06d}.png"
-        overlay_path = overlay_root / f"frame-{index + 1:06d}.png"
-        mask_sha256 = save_image(mask_path, expanded, "L")
-        overlay_sha256 = save_image(overlay_path, overlay(source, expanded, palette))
-        present = np.flatnonzero(np.bincount(expanded.reshape(-1), minlength=CLASS_COUNT))
-        frame_results.append(
-            {
-                "frame_index": index,
-                "source_sequence": timeline_row["source_sequence"],
-                "source_frame_index": timeline_row["source_frame_index"],
-                "session_seconds": timeline_row["session_seconds"],
-                "latency_ms": round(latency_ms, 6),
-                "present_classes": [
-                    {"class_id": int(class_id), "label": names[int(class_id)]}
-                    for class_id in present
-                ],
-                "mask": {
-                    "path": mask_path.relative_to(args.output).as_posix(),
-                    "byte_length": mask_path.stat().st_size,
-                    "sha256": mask_sha256,
-                },
-                "overlay": {
-                    "path": overlay_path.relative_to(args.output).as_posix(),
-                    "byte_length": overlay_path.stat().st_size,
-                    "sha256": overlay_sha256,
-                },
-            }
-        )
-    wall_seconds = time.perf_counter() - started
-    if len(frame_results) != manifest["frame_count"]:
-        raise MixedRouteDdrnetError("DDRNet island accounting changed")
-    timing = {
-        "prewarm_inference_count": len(warmup_ms),
-        "prewarm_latency_ms_first": round(warmup_ms[0], 6),
-        "prewarm_latency_ms_last": round(warmup_ms[-1], 6),
-        "inference_wall_seconds": round(wall_seconds, 6),
-        "latency_ms_mean": round(statistics.fmean(latencies_ms), 6),
-        "latency_ms_p50": round(percentile(latencies_ms, 0.5), 6),
-        "latency_ms_p95": round(percentile(latencies_ms, 0.95), 6),
-        "throughput_fps_from_mean_inference": round(
-            1000.0 / statistics.fmean(latencies_ms), 6
-        ),
-    }
-    if any(not math.isfinite(float(value)) for value in timing.values()):
-        raise MixedRouteDdrnetError("DDRNet timing is non-finite")
-    result: dict[str, Any] = {
-        "schema_version": SCHEMA,
-        "status": "review-islands-ready-not-accepted",
-        "worker_id": "worker-006",
-        "source": {
-            "pack_id": manifest["pack_id"],
-            "pack_identity_sha256": manifest["identity_sha256"],
-            "job_id": manifest["identity"]["job_id"],
-            "input_sha256": manifest["identity"]["input_sha256"],
-            "session_id": manifest["identity"]["session_id"],
-            "source_id": manifest["identity"]["source_id"],
-            "frame_count": manifest["frame_count"],
-            "ground_truth_available": False,
-        },
-        "candidate": {
-            "candidate_key": "ddrnet",
-            "candidate_id": candidate["candidate_id"],
-            "loaded_model_name": model_name,
-            "architecture_probe_failures": architecture_failures,
-            "checkpoint_size_bytes": checkpoint.stat().st_size,
-            "checkpoint_sha256": sha256(checkpoint),
-        },
-        "taxonomy": {
-            "schema_version": "missioncore.lab-v1-vegetation-taxonomy/v1",
-            "classes": [
-                {
-                    "class_id": class_id,
-                    "label": names[class_id],
-                    "color_rgb": palette[class_id, :3].astype(int).tolist(),
-                    "disposition": "undefined" if class_id == 0 else "prediction",
-                }
-                for class_id in range(CLASS_COUNT)
-            ],
-        },
-        "aggregate_prediction_pixels": aggregate.tolist(),
-        "frames": frame_results,
-        "timing": timing,
-        "resource": {
-            "hostname": platform.node(),
-            "gpu_name": torch.cuda.get_device_name(0),
-            "peak_allocated_vram_bytes": int(torch.cuda.max_memory_allocated()),
-            "peak_reserved_vram_bytes": int(torch.cuda.max_memory_reserved()),
-            "torch_version": torch.__version__,
-            "cuda_runtime_version": torch.version.cuda,
-            "python_version": platform.python_version(),
-        },
-        "provenance": {
-            "pack_manifest_sha256": sha256(args.pack / "manifest.json"),
-            "config_sha256": sha256(args.config),
-            "policy_sha256": sha256(args.policy),
-            "provider_map_sha256": sha256(args.provider_map),
-            "runner_sha256": sha256(Path(__file__)),
-        },
-        "limitations": [
-            "Selected independently decodable islands are not a complete route timeline.",
-            "RAVNOVES004TREE has no route truth; class colors are model predictions.",
-            "DDRNet evidence cannot clear rigid geometry, person or vehicle vetoes.",
-        ],
-        "authority": AUTHORITY,
-    }
-    result["result_id"] = f"mixed-route-ddrnet-islands-{stable_digest(result)}"
-    (args.output / "result.json").write_text(
-        json.dumps(result, ensure_ascii=False, sort_keys=True, indent=2) + "\n",
-        encoding="utf-8",
-    )
-    print(
-        json.dumps(
-            {
-                "result_id": result["result_id"],
-                "frames": len(frame_results),
-                "latency_p95_ms": timing["latency_ms_p95"],
-            },
-            sort_keys=True,
-        )
-    )
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(run())

experiments/perception/worker/lab_v1_vegetation_goose/run_vegetation_integrated_load.py

@@ -1,398 +0,0 @@
-#!/usr/bin/env python3
-"""Run source-paced DDRNet beside the frozen M4 graph and TGS shadow."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import math
-import platform
-import shutil
-import statistics
-import time
-from pathlib import Path
-from typing import Any
-
-import cv2
-import torch
-from PIL import Image
-from run_goose_vegetation_benchmark import (
-    infer,
-    load_mapping,
-    load_model,
-    percentile,
-    preprocess,
-    read_json,
-    sha256,
-    stable_digest,
-    validate_contracts,
-)
-
-SCHEMA = "missioncore.lab-v1-vegetation-integrated-load/v3"
-FRAME_SCHEMA = "missioncore.lab-v1-vegetation-integrated-frame/v2"
-FRAME_COUNT = 4_489
-AUTHORITY = {
-    "ground_truth": False,
-    "candidate_accepted": False,
-    "camera_semantics_can_clear_rigid_geometry": False,
-    "commands_enabled": False,
-    "actuation_allowed": False,
-    "navigation_or_safety_accepted": False,
-    "production_accepted": False,
-}
-
-
-class IntegratedLoadError(RuntimeError):
-    """The bounded integrated-load contract is incomplete or changed."""
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--config", type=Path, required=True)
-    parser.add_argument("--policy", type=Path, required=True)
-    parser.add_argument("--provider-map", type=Path, required=True)
-    parser.add_argument("--checkpoint", type=Path, required=True)
-    parser.add_argument("--dataset-root", type=Path, required=True)
-    parser.add_argument("--video", type=Path, required=True)
-    parser.add_argument("--video-sha256", required=True)
-    parser.add_argument("--runtime-video-cache", type=Path, required=True)
-    parser.add_argument("--source-rate-hz", type=float, required=True)
-    parser.add_argument("--inference-stride", type=int, required=True)
-    parser.add_argument("--inference-phase-offset-ms", type=float, required=True)
-    parser.add_argument("--minimum-effective-timeline-fps", type=float, required=True)
-    parser.add_argument("--minimum-effective-inference-fps", type=float, required=True)
-    parser.add_argument("--maximum-inference-completion-p95-ms", type=float, required=True)
-    parser.add_argument("--maximum-evidence-source-age-ms", type=float, required=True)
-    parser.add_argument("--shared-start-ready-file", type=Path, required=True)
-    parser.add_argument("--shared-start-file", type=Path, required=True)
-    parser.add_argument("--shared-start-timeout-seconds", type=float, default=600.0)
-    parser.add_argument("--frame-ledger", type=Path, required=True)
-    parser.add_argument("--output", type=Path, required=True)
-    parser.add_argument("--release-sha256", required=True)
-    return parser.parse_args()
-
-
-def wait_for_shared_start(ready_file: Path, start_file: Path, timeout_seconds: float) -> None:
-    if ready_file.exists():
-        raise IntegratedLoadError("shared-start ready file already exists")
-    ready_file.parent.mkdir(mode=0o700, parents=True, exist_ok=True)
-    ready_file.write_text("ready\n", encoding="utf-8")
-    deadline = time.monotonic() + timeout_seconds
-    while not start_file.is_file():
-        if time.monotonic() >= deadline:
-            raise IntegratedLoadError("shared-start barrier timed out")
-        time.sleep(0.01)
-
-
-def distribution(values: list[float]) -> dict[str, float]:
-    return {
-        "mean": round(statistics.fmean(values), 6),
-        "p50": round(percentile(values, 0.50), 6),
-        "p95": round(percentile(values, 0.95), 6),
-        "p99": round(percentile(values, 0.99), 6),
-        "maximum": round(max(values), 6),
-    }
-
-
-def open_video(path: Path) -> cv2.VideoCapture:
-    if path.is_symlink() or not path.is_file():
-        raise IntegratedLoadError("RAVNOVES video is unavailable")
-    capture = cv2.VideoCapture(str(path))
-    if not capture.isOpened():
-        raise IntegratedLoadError("RAVNOVES video decoder did not open")
-    return capture
-
-
-def decode_source(capture: cv2.VideoCapture, expected_size: tuple[int, int]) -> Image.Image:
-    available, bgr = capture.read()
-    if not available or bgr is None:
-        raise IntegratedLoadError("RAVNOVES video ended before the frozen frame count")
-    if (bgr.shape[1], bgr.shape[0]) != expected_size:
-        raise IntegratedLoadError("RAVNOVES decoded frame dimensions changed")
-    return Image.fromarray(cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB), mode="RGB")
-
-
-def validate_sha256(value: str, label: str) -> None:
-    if len(value) != 64 or any(character not in "0123456789abcdef" for character in value):
-        raise IntegratedLoadError(f"{label} SHA-256 is invalid")
-
-
-def buffer_compressed_video(source: Path, target: Path, expected_sha256: str) -> dict[str, Any]:
-    if source.is_symlink() or not source.is_file():
-        raise IntegratedLoadError("RAVNOVES video is unavailable")
-    if target.exists() or target.is_symlink():
-        raise IntegratedLoadError("RAVNOVES runtime video cache already exists")
-    if not target.parent.is_dir():
-        raise IntegratedLoadError("RAVNOVES runtime video cache parent is unavailable")
-    started = time.monotonic_ns()
-    shutil.copyfile(source, target)
-    copied_bytes = target.stat().st_size
-    if copied_bytes != source.stat().st_size:
-        raise IntegratedLoadError("RAVNOVES runtime video cache size changed")
-    copied_sha256 = sha256(target)
-    if copied_sha256 != expected_sha256:
-        raise IntegratedLoadError("RAVNOVES runtime video cache digest changed")
-    return {
-        "bytes": copied_bytes,
-        "sha256": copied_sha256,
-        "seconds": round((time.monotonic_ns() - started) / 1_000_000_000.0, 6),
-    }
-
-
-def run() -> int:
-    args = parse_args()
-    if not torch.cuda.is_available():
-        raise IntegratedLoadError("CUDA is required for Worker 006 qualification")
-    positive_finite_values = (
-        args.source_rate_hz,
-        args.minimum_effective_timeline_fps,
-        args.minimum_effective_inference_fps,
-        args.maximum_inference_completion_p95_ms,
-        args.maximum_evidence_source_age_ms,
-        args.shared_start_timeout_seconds,
-    )
-    if args.inference_stride <= 0 or any(
-        not math.isfinite(value) or value <= 0 for value in positive_finite_values
-    ):
-        raise IntegratedLoadError("integrated-load thresholds must be positive and finite")
-    if (
-        not math.isfinite(args.inference_phase_offset_ms)
-        or args.inference_phase_offset_ms < 0
-        or args.inference_phase_offset_ms >= 1000.0 / args.source_rate_hz
-    ):
-        raise IntegratedLoadError("inference phase offset must fit inside one source interval")
-    validate_sha256(args.release_sha256, "release")
-    validate_sha256(args.video_sha256, "video")
-    if args.output.exists() or args.frame_ledger.exists():
-        raise IntegratedLoadError("integrated-load output already exists")
-
-    config = read_json(args.config, "benchmark config")
-    policy = read_json(args.policy, "mission policy")
-    provider_map = read_json(args.provider_map, "provider map")
-    candidate = validate_contracts(config, policy, provider_map, "ddrnet")
-    if args.checkpoint.is_symlink() or not args.checkpoint.is_file():
-        raise IntegratedLoadError("DDRNet checkpoint is unavailable")
-    if args.checkpoint.stat().st_size != candidate["checkpoint_size_bytes"]:
-        raise IntegratedLoadError("DDRNet checkpoint size changed")
-    checkpoint_sha256 = sha256(args.checkpoint)
-    if checkpoint_sha256 != candidate["checkpoint_sha256"]:
-        raise IntegratedLoadError("DDRNet checkpoint digest changed")
-    mapping_path = args.dataset_root / config["dataset"]["mapping_relative_path"]
-    load_mapping(mapping_path, config["dataset"]["mapping_sha256"])
-    expected_size = (
-        config["ravnoves"]["expected_width"],
-        config["ravnoves"]["expected_height"],
-    )
-    compressed_video_buffer = buffer_compressed_video(
-        args.video, args.runtime_video_cache, args.video_sha256
-    )
-    warmup_capture = open_video(args.runtime_video_cache)
-    warmup_source = decode_source(warmup_capture, expected_size)
-    warmup_capture.release()
-
-    torch.cuda.empty_cache()
-    model, model_name, architecture_failures = load_model("ddrnet", args.checkpoint)
-    warmup_tensor, _ = preprocess(warmup_source)
-    warmup_latencies_ms = [infer(model, warmup_tensor)[1] for _ in range(3)]
-    torch.cuda.reset_peak_memory_stats()
-    source_capture = open_video(args.runtime_video_cache)
-    wait_for_shared_start(
-        args.shared_start_ready_file,
-        args.shared_start_file,
-        args.shared_start_timeout_seconds,
-    )
-
-    interval_ns = 1_000_000_000.0 / args.source_rate_hz
-    start_ns = time.monotonic_ns()
-    started_utc_ns = time.time_ns()
-    completion_ages_ms: list[float] = []
-    inference_completion_ages_ms: list[float] = []
-    evidence_source_ages_ms: list[float] = []
-    stage_latencies_ms: list[float] = []
-    inference_latencies_ms: list[float] = []
-    late_deadline_count = 0
-    inference_frame_count = 0
-    last_inference_sequence = -1
-    args.frame_ledger.parent.mkdir(parents=True, exist_ok=True)
-    with args.frame_ledger.open("x", encoding="utf-8") as ledger:
-        for sequence in range(FRAME_COUNT):
-            scheduled_ns = start_ns + round(sequence * interval_ns)
-            inference_executed = sequence % args.inference_stride == 0
-            execution_target_ns = scheduled_ns
-            if inference_executed:
-                execution_target_ns += round(args.inference_phase_offset_ms * 1_000_000.0)
-            remaining_ns = execution_target_ns - time.monotonic_ns()
-            if remaining_ns > 0:
-                time.sleep(remaining_ns / 1_000_000_000.0)
-            admitted_ns = time.monotonic_ns()
-            source = decode_source(source_capture, expected_size)
-            inference_ms: float | None = None
-            if inference_executed:
-                tensor, _ = preprocess(source)
-                _, inference_ms = infer(model, tensor)
-                last_inference_sequence = sequence
-                inference_frame_count += 1
-            if last_inference_sequence < 0:
-                raise IntegratedLoadError("semantic evidence is unavailable for the timeline")
-            completed_ns = time.monotonic_ns()
-            completion_age_ms = (completed_ns - scheduled_ns) / 1_000_000.0
-            stage_ms = (completed_ns - admitted_ns) / 1_000_000.0
-            semantic_source_scheduled_ns = start_ns + round(
-                last_inference_sequence * interval_ns
-            )
-            evidence_source_age_ms = (
-                completed_ns - semantic_source_scheduled_ns
-            ) / 1_000_000.0
-            completion_ages_ms.append(completion_age_ms)
-            evidence_source_ages_ms.append(evidence_source_age_ms)
-            stage_latencies_ms.append(stage_ms)
-            if inference_ms is not None:
-                inference_latencies_ms.append(inference_ms)
-                inference_completion_ages_ms.append(completion_age_ms)
-            if sequence + 1 < FRAME_COUNT and completed_ns > start_ns + round(
-                (sequence + 1) * interval_ns
-            ):
-                late_deadline_count += 1
-            row = {
-                "schema_version": FRAME_SCHEMA,
-                "sequence": sequence,
-                "frame_name": f"frame-{sequence + 1:06d}",
-                "scheduled_monotonic_ns": scheduled_ns,
-                "admitted_monotonic_ns": admitted_ns,
-                "completed_monotonic_ns": completed_ns,
-                "completion_age_ms": round(completion_age_ms, 6),
-                "stage_ms": round(stage_ms, 6),
-                "inference_executed": inference_executed,
-                "inference_phase_offset_ms": args.inference_phase_offset_ms
-                if inference_executed
-                else 0.0,
-                "inference_ms": round(inference_ms, 6) if inference_ms is not None else None,
-                "semantic_source_sequence": last_inference_sequence,
-                "semantic_evidence_source_age_ms": round(evidence_source_age_ms, 6),
-            }
-            ledger.write(json.dumps(row, sort_keys=True, separators=(",", ":")) + "\n")
-            if sequence % 64 == 0:
-                ledger.flush()
-    extra_available, _ = source_capture.read()
-    source_capture.release()
-    if extra_available:
-        raise IntegratedLoadError("RAVNOVES video contains frames beyond the frozen timeline")
-
-    completed_ns = time.monotonic_ns()
-    wall_seconds = (completed_ns - start_ns) / 1_000_000_000.0
-    effective_timeline_fps = FRAME_COUNT / wall_seconds
-    effective_inference_fps = inference_frame_count / wall_seconds
-    completion = distribution(completion_ages_ms)
-    inference_completion = distribution(inference_completion_ages_ms)
-    evidence_source_age = distribution(evidence_source_ages_ms)
-    expected_inference_frames = (FRAME_COUNT + args.inference_stride - 1) // args.inference_stride
-    checks = {
-        "all_frames_accounted": len(completion_ages_ms) == FRAME_COUNT,
-        "exact_multirate_schedule": inference_frame_count == expected_inference_frames,
-        "inference_phase_offset_preserved": args.inference_phase_offset_ms
-        < 1000.0 / args.source_rate_hz,
-        "minimum_effective_timeline_fps": effective_timeline_fps
-        >= args.minimum_effective_timeline_fps,
-        "minimum_effective_inference_fps": effective_inference_fps
-        >= args.minimum_effective_inference_fps,
-        "maximum_inference_completion_p95_ms": inference_completion["p95"]
-        <= args.maximum_inference_completion_p95_ms,
-        "maximum_evidence_source_age_ms": evidence_source_age["maximum"]
-        <= args.maximum_evidence_source_age_ms,
-        "zero_capacity_drops": len(completion_ages_ms) == FRAME_COUNT,
-        "authority_remains_false": all(value is False for value in AUTHORITY.values()),
-    }
-    result: dict[str, Any] = {
-        "schema_version": SCHEMA,
-        "worker_id": "worker-006",
-        "source": {
-            "source_id": config["ravnoves"]["source_id"],
-            "frame_count": FRAME_COUNT,
-            "requested_source_rate_hz": args.source_rate_hz,
-            "raw_fisheye_immutable": True,
-            "ground_truth_available": False,
-        },
-        "candidate": {
-            "candidate_id": candidate["candidate_id"],
-            "candidate_key": "ddrnet",
-            "loaded_model_name": model_name,
-            "architecture_probe_failures": architecture_failures,
-            "checkpoint_size_bytes": args.checkpoint.stat().st_size,
-            "checkpoint_sha256": checkpoint_sha256,
-        },
-        "execution": {
-            "run_mode": "source-paced-multirate-integrated-shadow/v2",
-            "started_utc_ns": started_utc_ns,
-            "wall_seconds": round(wall_seconds, 6),
-            "effective_fps": round(effective_timeline_fps, 6),
-            "effective_timeline_fps": round(effective_timeline_fps, 6),
-            "effective_inference_fps": round(effective_inference_fps, 6),
-            "inference_stride": args.inference_stride,
-            "inference_phase_offset_ms": args.inference_phase_offset_ms,
-            "inference_frame_count": inference_frame_count,
-            "held_evidence_frame_count": FRAME_COUNT - inference_frame_count,
-            "frame_count": FRAME_COUNT,
-            "capacity_drop_count": 0,
-            "deadline_miss_count": late_deadline_count,
-            "source_decode": {
-                "mode": "bounded-compressed-scene-buffer/v1",
-                "compressed_scene_prefetch": True,
-                "compressed_scene_buffer": compressed_video_buffer,
-                "full_route_rgb_prefetch": False,
-                "candidate_local_decoder": True,
-                "runtime_target": "shared-source-frame",
-            },
-            "frame_ledger": {
-                "path": args.frame_ledger.name,
-                "rows": FRAME_COUNT,
-                "sha256": sha256(args.frame_ledger),
-            },
-        },
-        "timing": {
-            "prewarm_inference_count": len(warmup_latencies_ms),
-            "prewarm_latency_ms_first": round(warmup_latencies_ms[0], 6),
-            "prewarm_latency_ms_last": round(warmup_latencies_ms[-1], 6),
-            "completion_age_ms": completion,
-            "inference_completion_age_ms": inference_completion,
-            "semantic_evidence_source_age_ms": evidence_source_age,
-            "stage_ms": distribution(stage_latencies_ms),
-            "inference_ms": distribution(inference_latencies_ms),
-        },
-        "resource": {
-            "gpu_name": torch.cuda.get_device_name(0),
-            "peak_allocated_vram_bytes": int(torch.cuda.max_memory_allocated()),
-            "peak_reserved_vram_bytes": int(torch.cuda.max_memory_reserved()),
-            "torch_version": torch.__version__,
-            "cuda_runtime_version": torch.version.cuda,
-            "python_version": platform.python_version(),
-        },
-        "identity": {
-            "release_sha256": args.release_sha256,
-            "config_sha256": sha256(args.config),
-            "policy_sha256": sha256(args.policy),
-            "provider_map_sha256": sha256(args.provider_map),
-            "runner_sha256": sha256(Path(__file__)),
-        },
-        "predeclared_thresholds": {
-            "minimum_effective_timeline_fps": args.minimum_effective_timeline_fps,
-            "minimum_effective_inference_fps": args.minimum_effective_inference_fps,
-            "inference_phase_offset_ms": args.inference_phase_offset_ms,
-            "maximum_inference_completion_p95_ms": (
-                args.maximum_inference_completion_p95_ms
-            ),
-            "maximum_evidence_source_age_ms": args.maximum_evidence_source_age_ms,
-            "capacity_drop_count_max": 0,
-        },
-        "checks": checks,
-        "integrated_load_gate_passed": all(checks.values()),
-        "authority": AUTHORITY,
-    }
-    result["result_id"] = f"lab-v1-vegetation-integrated-{stable_digest(result)}"
-    args.output.parent.mkdir(parents=True, exist_ok=True)
-    args.output.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n", encoding="utf-8")
-    print(json.dumps({"result_id": result["result_id"], "passed": all(checks.values())}))
-    return 0 if all(checks.values()) else 2
-
-
-if __name__ == "__main__":
-    raise SystemExit(run())

experiments/perception/worker/m49_t3_travel/build_mixed_route_tgs_evidence.py

@@ -1,277 +0,0 @@
-#!/usr/bin/env python3
-"""Seal fail-closed TRAVEL/TGS evidence for mixed-route review islands."""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import json
-from pathlib import Path
-
-import numpy as np
-from build_tgs_fail_closed_evidence import (
-    TgsEvidenceError,
-    _load_float32,
-    classify_exact_input,
-    costmap_grid,
-    rasterize_costmap,
-    sha256_file,
-    write_deterministic_npz,
-)
-
-CONFIG_SCHEMA = "missioncore.mixed-route-tgs-review-profile/v1"
-INPUT_SCHEMA = "missioncore.mixed-route-tgs-input/v1"
-RESULT_SCHEMA = "missioncore.mixed-route-tgs-result/v1"
-FRAME_COUNT = 10
-
-
-def _timing(path: Path) -> dict[str, object]:
-    rows: list[dict[str, object]] = []
-    with path.open(encoding="utf-8", newline="") as stream:
-        for raw in csv.DictReader(stream, delimiter="\t"):
-            try:
-                row = {
-                    "profile_id": str(raw["profile"]),
-                    "slot": int(raw["slot"]),
-                    "wall_seconds": float(raw["wall_seconds"]),
-                    "max_rss_kib": int(raw["max_rss_kib"]),
-                }
-            except (KeyError, TypeError, ValueError) as exc:
-                raise TgsEvidenceError("TGS timing row is invalid") from exc
-            if (
-                row["profile_id"] not in {"current_increment", "causal_rolling_1s"}
-                or not 0 <= row["slot"] < FRAME_COUNT
-                or not 0 <= row["wall_seconds"] < 60
-                or not 0 < row["max_rss_kib"] < 16 * 1024 * 1024
-            ):
-                raise TgsEvidenceError("TGS timing value is invalid")
-            rows.append(row)
-    if len(rows) != FRAME_COUNT * 2:
-        raise TgsEvidenceError("TGS timing is incomplete")
-    seconds = np.asarray([row["wall_seconds"] for row in rows], dtype=np.float64)
-    return {
-        "runs": rows,
-        "wall_seconds_mean": round(float(seconds.mean()), 6),
-        "wall_seconds_p95": round(float(np.percentile(seconds, 95)), 6),
-        "max_rss_kib": max(int(row["max_rss_kib"]) for row in rows),
-    }
-
-
-def build(run_root: Path, config_path: Path, output_root: Path) -> dict[str, object]:
-    if output_root.exists():
-        raise TgsEvidenceError("mixed-route TGS evidence already exists")
-    config = json.loads(config_path.read_text(encoding="utf-8"))
-    source = config.get("source") if isinstance(config, dict) else None
-    invariants = config.get("invariants") if isinstance(config, dict) else None
-    if (
-        config.get("schema_version") != CONFIG_SCHEMA
-        or not isinstance(source, dict)
-        or not isinstance(invariants, dict)
-        or invariants.get("aos_allowed") is not False
-        or invariants.get("missing_support_means_free") is not False
-        or invariants.get("future_frames_used") is not False
-        or invariants.get("navigation_or_actuation_allowed") is not False
-        or config.get("state_codes")
-        != {
-            "UNOBSERVED": 0,
-            "GROUND_SUPPORT": 1,
-            "NONGROUND_OCCUPIED": 2,
-            "UNKNOWN_REJECTED": 3,
-        }
-    ):
-        raise TgsEvidenceError("mixed-route TGS profile changed")
-    input_manifest_path = run_root / "inputs" / "input-manifest.json"
-    input_manifest = json.loads(input_manifest_path.read_text(encoding="utf-8"))
-    if (
-        input_manifest.get("schema_version") != INPUT_SCHEMA
-        or input_manifest.get("source_pack_id") != source.get("source_pack_id")
-        or input_manifest.get("source_pack_sha256")
-        != source.get("source_pack_sha256")
-        or input_manifest.get("config_sha256") != sha256_file(config_path)
-        or input_manifest.get("coordinate_frame") != "map-gravity-local"
-        or input_manifest.get("future_frames_used") is not False
-        or input_manifest.get("frame_count") != FRAME_COUNT
-        or len(input_manifest.get("records", [])) != FRAME_COUNT * 2
-    ):
-        raise TgsEvidenceError("mixed-route TGS input manifest changed")
-    records = {
-        (str(row["profile_id"]), int(row["slot"])): row
-        for row in input_manifest["records"]
-    }
-    if len(records) != FRAME_COUNT * 2:
-        raise TgsEvidenceError("mixed-route TGS input records are not unique")
-
-    cell_size = float(config["costmap"]["cell_size_m"])
-    radius = float(config["costmap"]["radius_m"])
-    grid = costmap_grid(radius, cell_size)
-    arrays: dict[str, np.ndarray] = {
-        "costmap_cell_indices_xy": grid[:, :2].astype(np.int32),
-        "costmap_cell_centers_xy_m": grid[:, 2:].astype(np.float32),
-        "source_frame_indices": np.asarray(
-            [
-                records[("current_increment", slot)]["source_frame_index"]
-                for slot in range(FRAME_COUNT)
-            ],
-            dtype=np.int64,
-        ),
-        "session_seconds": np.asarray(
-            [
-                records[("current_increment", slot)]["session_seconds"]
-                for slot in range(FRAME_COUNT)
-            ],
-            dtype=np.float64,
-        ),
-    }
-    summaries: list[dict[str, object]] = []
-    for profile_id in ("current_increment", "causal_rolling_1s"):
-        all_points: list[np.ndarray] = []
-        all_states: list[np.ndarray] = []
-        offsets = [0]
-        grid_states: list[np.ndarray] = []
-        ground_counts: list[np.ndarray] = []
-        nonground_counts: list[np.ndarray] = []
-        rejected_counts: list[np.ndarray] = []
-        z_bounds_rows: list[np.ndarray] = []
-        for slot in range(FRAME_COUNT):
-            record = records[(profile_id, slot)]
-            native_path = run_root / "inputs" / str(record["relative_path"])
-            if (
-                not native_path.is_file()
-                or native_path.stat().st_size != record["bytes"]
-                or sha256_file(native_path) != record["sha256"]
-            ):
-                raise TgsEvidenceError("sealed mixed-route TGS input changed")
-            output = run_root / "outputs" / profile_id
-            points, states = classify_exact_input(
-                _load_float32(native_path, 4),
-                _load_float32(output / f"{slot}_ground.bin", 4),
-                _load_float32(output / f"{slot}_nonground.bin", 4),
-                min_range_m=float(config["tgs"]["min_range_m"]),
-                max_range_m=float(config["tgs"]["max_range_m"]),
-            )
-            grid_state, ground, nonground, rejected, z_bounds = rasterize_costmap(
-                points,
-                states,
-                grid,
-                cell_size_m=cell_size,
-            )
-            all_points.append(points.astype(np.float32, copy=False))
-            all_states.append(states)
-            offsets.append(offsets[-1] + points.shape[0])
-            grid_states.append(grid_state)
-            ground_counts.append(ground)
-            nonground_counts.append(nonground)
-            rejected_counts.append(rejected)
-            z_bounds_rows.append(z_bounds)
-            accounted = (
-                np.count_nonzero(states == 1)
-                + np.count_nonzero(states == 2)
-                + np.count_nonzero(states == 3)
-                == points.shape[0]
-            )
-            summaries.append(
-                {
-                    "profile_id": profile_id,
-                    "slot": slot,
-                    "frame_index": int(record["frame_index"]),
-                    "source_frame_index": int(record["source_frame_index"]),
-                    "source_sequence": int(record["source_sequence"]),
-                    "session_seconds": float(record["session_seconds"]),
-                    "point_count": int(points.shape[0]),
-                    "ground_point_count": int(np.count_nonzero(states == 1)),
-                    "nonground_point_count": int(np.count_nonzero(states == 2)),
-                    "rejected_point_count": int(np.count_nonzero(states == 3)),
-                    "ground_cell_count": int(np.count_nonzero(grid_state == 1)),
-                    "nonground_cell_count": int(np.count_nonzero(grid_state == 2)),
-                    "rejected_cell_count": int(np.count_nonzero(grid_state == 3)),
-                    "unobserved_cell_count": int(np.count_nonzero(grid_state == 0)),
-                    "all_points_accounted": bool(accounted),
-                }
-            )
-        arrays[f"{profile_id}_points_xyz_m"] = np.concatenate(all_points)
-        arrays[f"{profile_id}_point_states"] = np.concatenate(all_states)
-        arrays[f"{profile_id}_point_offsets"] = np.asarray(offsets, dtype=np.int64)
-        arrays[f"{profile_id}_costmap_states"] = np.stack(grid_states)
-        arrays[f"{profile_id}_costmap_ground_point_counts"] = np.stack(ground_counts)
-        arrays[f"{profile_id}_costmap_nonground_point_counts"] = np.stack(
-            nonground_counts
-        )
-        arrays[f"{profile_id}_costmap_rejected_point_counts"] = np.stack(
-            rejected_counts
-        )
-        arrays[f"{profile_id}_costmap_z_bounds_m"] = np.stack(z_bounds_rows)
-    if not all(bool(row["all_points_accounted"]) for row in summaries):
-        raise TgsEvidenceError("mixed-route TGS lost an eligible point")
-
-    output_root.mkdir(parents=True)
-    evidence_path = output_root / "evidence.npz"
-    write_deterministic_npz(evidence_path, arrays)
-    timing = _timing(run_root / "tgs-timing.tsv")
-    result = {
-        "schema_version": RESULT_SCHEMA,
-        "status": "passed-review-only",
-        "source": {
-            "source_id": source["source_id"],
-            "session_id": source["session_id"],
-            "review_pack_id": source["review_pack_id"],
-            "source_pack_id": source["source_pack_id"],
-            "source_pack_sha256": source["source_pack_sha256"],
-        },
-        "config_sha256": sha256_file(config_path),
-        "input_manifest_sha256": sha256_file(input_manifest_path),
-        "evidence": {
-            "path": "evidence.npz",
-            "bytes": evidence_path.stat().st_size,
-            "sha256": sha256_file(evidence_path),
-        },
-        "costmap": {
-            "coordinate_frame": "map-gravity-local",
-            "cell_size_m": cell_size,
-            "radius_m": radius,
-            "cell_count": int(grid.shape[0]),
-        },
-        "anchors": summaries,
-        "timing": timing,
-        "summary": {
-            "frame_count": FRAME_COUNT,
-            "anchor_profile_count": len(summaries),
-            "all_eligible_points_accounted": True,
-            "aos_used": False,
-            "primary_profile": "causal_rolling_1s",
-        },
-        "limitations": [
-            "Selected review islands are not a complete route timeline.",
-            (
-                "TGS separates local ground support from non-ground evidence; it does not "
-                "prove ditch or negative-obstacle detection."
-            ),
-            "Camera projection is visual evidence only and cannot clear rigid geometry.",
-        ],
-        "authority": {
-            "visual_quality_accepted": False,
-            "traversability_accepted": False,
-            "realtime_accepted": False,
-            "navigation_or_safety_accepted": False,
-            "actuation_allowed": False,
-        },
-    }
-    (output_root / "result.json").write_text(
-        json.dumps(result, indent=2, sort_keys=True) + "\n",
-        encoding="utf-8",
-    )
-    return result
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--run-root", type=Path, required=True)
-    parser.add_argument("--config", type=Path, required=True)
-    parser.add_argument("--output-root", type=Path, required=True)
-    args = parser.parse_args()
-    result = build(args.run_root, args.config, args.output_root)
-    print(json.dumps(result["summary"], sort_keys=True))
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

experiments/perception/worker/m49_t3_travel/build_vegetation_integrated_graph_evidence.py

@@ -1,461 +0,0 @@
-#!/usr/bin/env python3
-"""Seal the synchronized RF-DETR, TGS and DDRNet Worker 006 load gate."""
-
-from __future__ import annotations
-
-import argparse
-import csv
-import hashlib
-import json
-import math
-import re
-from collections import defaultdict
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-
-PROFILE_SCHEMA = "missioncore.lab-v1-vegetation-integrated-shadow-profile/v3"
-M49_SCHEMA = "missioncore.m49-tgs-integrated-graph-shadow-result/v1"
-VEGETATION_SCHEMA = "missioncore.lab-v1-vegetation-integrated-load/v3"
-RESULT_SCHEMA = "missioncore.lab-v1-vegetation-integrated-shadow-result/v3"
-FRAME_COUNT = 4_489
-
-
-class VegetationIntegratedError(RuntimeError):
-    """The synchronized three-layer load evidence is incomplete."""
-
-
-def canonical_json(value: object) -> bytes:
-    return json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8")
-
-
-def sha256_file(path: Path) -> str:
-    digest = hashlib.sha256()
-    with path.open("rb") as stream:
-        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
-            digest.update(chunk)
-    return digest.hexdigest()
-
-
-def load_json(path: Path, label: str) -> dict[str, Any]:
-    try:
-        value = json.loads(path.read_text(encoding="utf-8-sig"))
-    except (OSError, json.JSONDecodeError) as exc:
-        raise VegetationIntegratedError(f"{label} is unreadable") from exc
-    if not isinstance(value, dict):
-        raise VegetationIntegratedError(f"{label} is not an object")
-    return value
-
-
-def distribution(values: list[float]) -> dict[str, float]:
-    if not values:
-        raise VegetationIntegratedError("timing distribution is empty")
-    array = np.asarray(values, dtype=np.float64)
-    return {
-        "mean": round(float(array.mean()), 6),
-        "p50": round(float(np.percentile(array, 50)), 6),
-        "p95": round(float(np.percentile(array, 95)), 6),
-        "p99": round(float(np.percentile(array, 99)), 6),
-        "maximum": round(float(array.max()), 6),
-    }
-
-
-def graph_completion_ages(path: Path) -> list[float]:
-    values: list[float] = []
-    with path.open("r", encoding="utf-8") as stream:
-        for expected, line in enumerate(stream):
-            row = json.loads(line)
-            if row.get("source_envelope", {}).get("sequence") != expected:
-                raise VegetationIntegratedError("graph frame sequence changed")
-            age = row.get("completion_age_ns")
-            if not isinstance(age, int) or age < 0:
-                raise VegetationIntegratedError("graph completion age is invalid")
-            values.append(age / 1_000_000.0)
-    if len(values) != FRAME_COUNT:
-        raise VegetationIntegratedError("graph frame ledger is incomplete")
-    return values
-
-
-def tgs_completion_ages(path: Path) -> list[float]:
-    values: list[float] = []
-    with path.open("r", encoding="utf-8", newline="") as stream:
-        for expected, row in enumerate(csv.DictReader(stream, delimiter="\t")):
-            if int(row["timeline_frame_index"]) != expected:
-                raise VegetationIntegratedError("TGS timing sequence changed")
-            age = float(row["completion_age_ms"])
-            if not math.isfinite(age) or age < 0:
-                raise VegetationIntegratedError("TGS completion age is invalid")
-            values.append(age)
-    if len(values) != FRAME_COUNT:
-        raise VegetationIntegratedError("TGS timing ledger is incomplete")
-    return values
-
-
-def vegetation_frame_metrics(
-    path: Path,
-    *,
-    inference_stride: int,
-    inference_phase_offset_ms: float,
-    source_rate_hz: float,
-) -> dict[str, object]:
-    completion_ages: list[float] = []
-    evidence_source_ages: list[float] = []
-    inference_count = 0
-    with path.open("r", encoding="utf-8") as stream:
-        for expected, line in enumerate(stream):
-            row = json.loads(line)
-            if row.get("schema_version") != "missioncore.lab-v1-vegetation-integrated-frame/v2":
-                raise VegetationIntegratedError("vegetation frame schema changed")
-            if row.get("sequence") != expected:
-                raise VegetationIntegratedError("vegetation frame sequence changed")
-            age = row.get("completion_age_ms")
-            if not isinstance(age, (int, float)) or not math.isfinite(age) or age < 0:
-                raise VegetationIntegratedError("vegetation completion age is invalid")
-            inference_executed = row.get("inference_executed")
-            expected_inference = expected % inference_stride == 0
-            if inference_executed is not expected_inference:
-                raise VegetationIntegratedError("vegetation inference schedule changed")
-            expected_phase = inference_phase_offset_ms if expected_inference else 0.0
-            phase = row.get("inference_phase_offset_ms")
-            if not isinstance(phase, (int, float)) or float(phase) != expected_phase:
-                raise VegetationIntegratedError("vegetation inference phase changed")
-            if expected_inference and float(age) + 0.001 < expected_phase:
-                raise VegetationIntegratedError("vegetation inference phase attribution changed")
-            expected_source = expected - (expected % inference_stride)
-            if row.get("semantic_source_sequence") != expected_source:
-                raise VegetationIntegratedError("vegetation evidence source changed")
-            evidence_age = row.get("semantic_evidence_source_age_ms")
-            expected_evidence_age = float(age) + (
-                (expected - expected_source) * 1000.0 / source_rate_hz
-            )
-            if (
-                not isinstance(evidence_age, (int, float))
-                or not math.isfinite(evidence_age)
-                or evidence_age < 0
-                or abs(float(evidence_age) - expected_evidence_age) > 0.001
-            ):
-                raise VegetationIntegratedError("vegetation evidence source age changed")
-            completion_ages.append(float(age))
-            evidence_source_ages.append(float(evidence_age))
-            inference_count += int(expected_inference)
-    if len(completion_ages) != FRAME_COUNT:
-        raise VegetationIntegratedError("vegetation frame ledger is incomplete")
-    return {
-        "completion_ages": completion_ages,
-        "evidence_source_ages": evidence_source_ages,
-        "inference_count": inference_count,
-        "held_count": FRAME_COUNT - inference_count,
-    }
-
-
-_SIZE = re.compile(r"^\s*([0-9.]+)\s*([kmgt]?i?b)\s*$", re.IGNORECASE)
-
-
-def size_mib(value: str) -> float:
-    match = _SIZE.fullmatch(value)
-    if match is None:
-        raise VegetationIntegratedError("container memory telemetry is invalid")
-    number = float(match.group(1))
-    scale = {
-        "b": 1.0 / (1024.0 * 1024.0),
-        "kb": 1.0 / 1024.0,
-        "kib": 1.0 / 1024.0,
-        "mb": 1.0,
-        "mib": 1.0,
-        "gb": 1024.0,
-        "gib": 1024.0,
-        "tb": 1024.0 * 1024.0,
-        "tib": 1024.0 * 1024.0,
-    }[match.group(2).lower()]
-    return number * scale
-
-
-def host_telemetry(path: Path) -> dict[str, object]:
-    roles = ("graph", "tgs", "triton", "vegetation")
-    samples: dict[str, list[dict[str, float]]] = defaultdict(list)
-    with path.open("r", encoding="utf-8-sig") as stream:
-        for line in stream:
-            row = json.loads(line)
-            role = row.get("role")
-            if role not in roles:
-                raise VegetationIntegratedError("container telemetry role changed")
-            cpu = row.get("cpu_percent")
-            memory = row.get("memory_usage")
-            memory_percent = row.get("memory_percent")
-            if not all(isinstance(value, str) for value in (cpu, memory, memory_percent)):
-                raise VegetationIntegratedError("container telemetry row is incomplete")
-            assert isinstance(cpu, str) and isinstance(memory, str)
-            assert isinstance(memory_percent, str)
-            samples[role].append(
-                {
-                    "cpu_percent": float(cpu.rstrip("%")),
-                    "memory_used_mib": size_mib(memory.split("/", 1)[0].strip()),
-                    "memory_percent": float(memory_percent.rstrip("%")),
-                }
-            )
-    if any(not samples[role] for role in roles):
-        raise VegetationIntegratedError("container telemetry does not cover every runtime role")
-    return {
-        role: {
-            "sample_count": len(samples[role]),
-            "cpu_percent": distribution([row["cpu_percent"] for row in samples[role]]),
-            "memory_used_mib": distribution(
-                [row["memory_used_mib"] for row in samples[role]]
-            ),
-            "memory_percent": distribution(
-                [row["memory_percent"] for row in samples[role]]
-            ),
-        }
-        for role in roles
-    }
-
-
-def build(
-    *,
-    profile_path: Path,
-    m49_result_path: Path,
-    graph_frames_path: Path,
-    tgs_timing_path: Path,
-    vegetation_result_path: Path,
-    vegetation_frames_path: Path,
-    telemetry_path: Path,
-    output_path: Path,
-    release_sha256: str,
-) -> dict[str, object]:
-    if output_path.exists():
-        raise VegetationIntegratedError("integrated vegetation result already exists")
-    if len(release_sha256) != 64 or any(
-        character not in "0123456789abcdef" for character in release_sha256
-    ):
-        raise VegetationIntegratedError("release SHA-256 is invalid")
-    profile = load_json(profile_path, "integrated vegetation profile")
-    m49 = load_json(m49_result_path, "M49 integrated result")
-    vegetation = load_json(vegetation_result_path, "vegetation load result")
-    if profile.get("schema_version") != PROFILE_SCHEMA:
-        raise VegetationIntegratedError("integrated vegetation profile schema changed")
-    if m49.get("schema_version") != M49_SCHEMA:
-        raise VegetationIntegratedError("M49 integrated result schema changed")
-    if vegetation.get("schema_version") != VEGETATION_SCHEMA:
-        raise VegetationIntegratedError("vegetation load result schema changed")
-
-    source_rate_hz = float(profile["source"]["requested_source_rate_hz"])
-    inference_stride = int(profile["stages"]["vegetation"]["inference_stride"])
-    inference_phase_offset_ms = float(
-        profile["stages"]["vegetation"]["inference_phase_offset_ms"]
-    )
-    if (
-        not math.isfinite(source_rate_hz)
-        or source_rate_hz <= 0
-        or inference_stride <= 0
-        or not math.isfinite(inference_phase_offset_ms)
-        or inference_phase_offset_ms < 0
-        or inference_phase_offset_ms >= 1000.0 / source_rate_hz
-    ):
-        raise VegetationIntegratedError("vegetation multirate schedule is invalid")
-    graph_ages = graph_completion_ages(graph_frames_path)
-    tgs_ages = tgs_completion_ages(tgs_timing_path)
-    vegetation_frames = vegetation_frame_metrics(
-        vegetation_frames_path,
-        inference_stride=inference_stride,
-        inference_phase_offset_ms=inference_phase_offset_ms,
-        source_rate_hz=source_rate_hz,
-    )
-    vegetation_ages = vegetation_frames["completion_ages"]
-    assert isinstance(vegetation_ages, list)
-    combined_ages = [
-        max(graph, tgs, semantic)
-        for graph, tgs, semantic in zip(
-            graph_ages, tgs_ages, vegetation_ages, strict=True
-        )
-    ]
-    combined = distribution(combined_ages)
-    telemetry = host_telemetry(telemetry_path)
-    acceptance = profile["acceptance"]
-    vegetation_execution = vegetation.get("execution", {})
-    vegetation_timing = vegetation.get("timing", {})
-    vegetation_identity = vegetation.get("identity", {})
-    vegetation_candidate = vegetation.get("candidate", {})
-    m49_performance = m49.get("performance", {})
-    m49_accounting = m49.get("accounting", {})
-    checks = {
-        "base_m49_runtime_passed": (
-            m49.get("status") == "passed"
-            and m49.get("integrated_runtime_gate_passed") is True
-            and m49.get("identity", {}).get("profile_sha256")
-            == profile["stages"]["m49_graph_tgs"]["profile_sha256"]
-        ),
-        "vegetation_identity_frozen": (
-            vegetation_candidate.get("candidate_key") == "ddrnet"
-            and vegetation_candidate.get("checkpoint_sha256")
-            == profile["stages"]["vegetation"]["checkpoint_sha256"]
-            and vegetation_identity.get("config_sha256")
-            == profile["stages"]["vegetation"]["config_sha256"]
-            and vegetation_identity.get("policy_sha256")
-            == profile["stages"]["vegetation"]["policy_sha256"]
-            and vegetation_identity.get("provider_map_sha256")
-            == profile["stages"]["vegetation"]["provider_map_sha256"]
-        ),
-        "requested_source_rate_preserved": (
-            vegetation.get("source", {}).get("requested_source_rate_hz")
-            == profile["source"]["requested_source_rate_hz"]
-        ),
-        "vegetation_load_gate_passed": vegetation.get("integrated_load_gate_passed") is True,
-        "vegetation_multirate_schedule_frozen": (
-            vegetation_execution.get("inference_stride") == inference_stride
-            and vegetation_execution.get("inference_phase_offset_ms")
-            == inference_phase_offset_ms
-            and vegetation_execution.get("inference_frame_count")
-            == vegetation_frames["inference_count"]
-            and vegetation_execution.get("held_evidence_frame_count")
-            == vegetation_frames["held_count"]
-        ),
-        "exact_three_layer_sequence_join": len(combined_ages) == FRAME_COUNT,
-        "all_graph_frames_delivered": (
-            m49_accounting.get("graph_admitted") == FRAME_COUNT
-            and m49_accounting.get("graph_delivered") == FRAME_COUNT
-        ),
-        "all_tgs_frames_accounted": m49_accounting.get("tgs_timeline_frames")
-        == FRAME_COUNT,
-        "all_vegetation_frames_accounted": vegetation_execution.get("frame_count")
-        == FRAME_COUNT,
-        "minimum_graph_world_state_fps": float(
-            m49_performance.get("effective_world_state_fps", 0.0)
-        )
-        >= float(acceptance["minimum_graph_world_state_fps"]),
-        "minimum_vegetation_timeline_fps": float(
-            vegetation_execution.get("effective_timeline_fps", 0.0)
-        )
-        >= float(acceptance["minimum_vegetation_timeline_fps"]),
-        "minimum_vegetation_inference_fps": float(
-            vegetation_execution.get("effective_inference_fps", 0.0)
-        )
-        >= float(acceptance["minimum_vegetation_inference_fps"]),
-        "maximum_vegetation_inference_completion_p95_ms": float(
-            vegetation_timing.get("inference_completion_age_ms", {}).get(
-                "p95", math.inf
-            )
-        )
-        <= float(acceptance["maximum_vegetation_inference_completion_p95_ms"]),
-        "maximum_semantic_evidence_source_age_ms": max(
-            vegetation_frames["evidence_source_ages"]
-        )
-        <= float(acceptance["maximum_semantic_evidence_source_age_ms"]),
-        "maximum_combined_output_age_p99_ms": combined["p99"]
-        <= float(acceptance["maximum_combined_output_age_p99_ms"]),
-        "zero_capacity_drops": (
-            int(m49_accounting.get("tgs_capacity_drops", -1)) == 0
-            and int(vegetation_execution.get("capacity_drop_count", -1)) == 0
-        ),
-        "host_resource_telemetry_complete": all(
-            telemetry[role]["sample_count"] > 0
-            for role in ("graph", "tgs", "triton", "vegetation")
-        ),
-        "authority_remains_false": (
-            all(value is False for value in profile["authority"].values())
-            and all(value is False for value in vegetation.get("authority", {}).values())
-        ),
-    }
-    files = {
-        label: {"bytes": path.stat().st_size, "sha256": sha256_file(path)}
-        for label, path in (
-            ("m49-result.json", m49_result_path),
-            ("graph-frames.jsonl", graph_frames_path),
-            ("tgs-timing.tsv", tgs_timing_path),
-            ("vegetation-result.json", vegetation_result_path),
-            ("vegetation-frames.jsonl", vegetation_frames_path),
-            ("container-telemetry.jsonl", telemetry_path),
-        )
-    }
-    document: dict[str, object] = {
-        "schema_version": RESULT_SCHEMA,
-        "profile_id": profile["profile_id"],
-        "status": "passed" if all(checks.values()) else "failed",
-        "source": {
-            "source_id": profile["source"]["source_id"],
-            "requested_source_rate_hz": profile["source"]["requested_source_rate_hz"],
-            "joined_frame_count": len(combined_ages),
-            "ground_truth_available": False,
-        },
-        "identity": {
-            "release_sha256": release_sha256,
-            "profile_sha256": sha256_file(profile_path),
-            "m49_result_id": m49.get("result_id"),
-            "vegetation_result_id": vegetation.get("result_id"),
-        },
-        "performance": {
-            "graph_tgs": m49_performance,
-            "vegetation": {
-                "effective_timeline_fps": vegetation_execution.get(
-                    "effective_timeline_fps"
-                ),
-                "effective_inference_fps": vegetation_execution.get(
-                    "effective_inference_fps"
-                ),
-                "completion_age_ms": vegetation_timing.get("completion_age_ms"),
-                "inference_completion_age_ms": vegetation_timing.get(
-                    "inference_completion_age_ms"
-                ),
-                "semantic_evidence_source_age_ms": distribution(
-                    vegetation_frames["evidence_source_ages"]
-                ),
-                "stage_ms": vegetation_timing.get("stage_ms"),
-                "inference_ms": vegetation_timing.get("inference_ms"),
-                "resource": vegetation.get("resource"),
-            },
-            "three_layer_output_age_ms": combined,
-            "host_containers": telemetry,
-        },
-        "accounting": {
-            "graph_frames": m49_accounting.get("graph_delivered"),
-            "tgs_frames": m49_accounting.get("tgs_timeline_frames"),
-            "vegetation_frames": vegetation_execution.get("frame_count"),
-            "vegetation_inference_frames": vegetation_frames["inference_count"],
-            "vegetation_held_evidence_frames": vegetation_frames["held_count"],
-            "capacity_drop_count": int(m49_accounting.get("tgs_capacity_drops", 0))
-            + int(vegetation_execution.get("capacity_drop_count", 0)),
-        },
-        "checks": checks,
-        "integrated_runtime_gate_passed": all(checks.values()),
-        "visual_quality_accepted": False,
-        "route_truth_available": False,
-        "production_accepted": False,
-        "authority": profile["authority"],
-        "files": files,
-    }
-    identity = hashlib.sha256(canonical_json(document)).hexdigest()
-    document["result_id"] = f"lab-v1-vegetation-integrated-shadow-{identity}"
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    output_path.write_text(json.dumps(document, indent=2, sort_keys=True) + "\n", encoding="utf-8")
-    return document
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--profile", type=Path, required=True)
-    parser.add_argument("--m49-result", type=Path, required=True)
-    parser.add_argument("--graph-frames", type=Path, required=True)
-    parser.add_argument("--tgs-timing", type=Path, required=True)
-    parser.add_argument("--vegetation-result", type=Path, required=True)
-    parser.add_argument("--vegetation-frames", type=Path, required=True)
-    parser.add_argument("--telemetry", type=Path, required=True)
-    parser.add_argument("--output", type=Path, required=True)
-    parser.add_argument("--release-sha256", required=True)
-    arguments = parser.parse_args()
-    result = build(
-        profile_path=arguments.profile,
-        m49_result_path=arguments.m49_result,
-        graph_frames_path=arguments.graph_frames,
-        tgs_timing_path=arguments.tgs_timing,
-        vegetation_result_path=arguments.vegetation_result,
-        vegetation_frames_path=arguments.vegetation_frames,
-        telemetry_path=arguments.telemetry,
-        output_path=arguments.output,
-        release_sha256=arguments.release_sha256,
-    )
-    print(json.dumps({"result_id": result["result_id"], "status": result["status"]}))
-    return 0 if result["status"] == "passed" else 2
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

experiments/perception/worker/m49_t3_travel/prepare_mixed_route_tgs_inputs.py

@@ -1,243 +0,0 @@
-#!/usr/bin/env python3
-"""Prepare exact mixed-route LiDAR islands for isolated TRAVEL/TGS review."""
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-from pathlib import Path
-
-import numpy as np
-from prepare_tgs_fail_closed_inputs import TgsInputError, gravity_local_xyzi
-
-CONFIG_SCHEMA = "missioncore.mixed-route-tgs-review-profile/v1"
-PACK_SCHEMA = "missioncore.mixed-route-lidar-pack/v1"
-INPUT_SCHEMA = "missioncore.mixed-route-tgs-input/v1"
-FRAME_COUNT = 10
-
-
-def sha256_file(path: Path) -> str:
-    digest = hashlib.sha256()
-    with path.open("rb") as stream:
-        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
-            digest.update(chunk)
-    return digest.hexdigest()
-
-
-def _slice(points: np.ndarray, offsets: np.ndarray, index: int) -> np.ndarray:
-    return points[int(offsets[index]) : int(offsets[index + 1])]
-
-
-def _validate_offsets(offsets: np.ndarray, point_count: int) -> bool:
-    return bool(
-        offsets.shape == (FRAME_COUNT + 1,)
-        and offsets.dtype == np.int64
-        and int(offsets[0]) == 0
-        and int(offsets[-1]) == point_count
-        and np.all(np.diff(offsets) > 0)
-    )
-
-
-def prepare(source_root: Path, config_path: Path, output_root: Path) -> dict[str, object]:
-    if output_root.exists():
-        raise TgsInputError("mixed-route TGS output already exists")
-    source = source_root.resolve(strict=True)
-    config = json.loads(config_path.read_text(encoding="utf-8"))
-    manifest = json.loads((source / "manifest.json").read_text(encoding="utf-8"))
-    identity = manifest.get("identity") if isinstance(manifest, dict) else None
-    artifact = manifest.get("artifact") if isinstance(manifest, dict) else None
-    source_config = config.get("source") if isinstance(config, dict) else None
-    invariants = config.get("invariants") if isinstance(config, dict) else None
-    profiles = config.get("profiles") if isinstance(config, dict) else None
-    if (
-        config.get("schema_version") != CONFIG_SCHEMA
-        or not isinstance(source_config, dict)
-        or not isinstance(invariants, dict)
-        or not isinstance(profiles, dict)
-        or set(profiles) != {"current_increment", "causal_rolling_1s"}
-        or source_config.get("input_coordinate_frame")
-        != "map-gravity-local-translation-only"
-        or invariants.get("lidar_orientation_applied_to_tgs_input") is not False
-        or invariants.get("future_frames_used") is not False
-        or invariants.get("navigation_or_actuation_allowed") is not False
-        or manifest.get("schema_version") != PACK_SCHEMA
-        or not isinstance(identity, dict)
-        or identity.get("schema_version") != PACK_SCHEMA
-        or identity.get("session_id") != source_config.get("session_id")
-        or identity.get("review_pack_id") != source_config.get("review_pack_id")
-        or manifest.get("pack_id") != source_config.get("source_pack_id")
-        or not isinstance(artifact, dict)
-        or artifact.get("path") != "lidar-pack.npz"
-        or artifact.get("sha256") != source_config.get("source_pack_sha256")
-        or identity.get("frame_count") != FRAME_COUNT
-        or identity.get("available_lidar_frames") != FRAME_COUNT
-        or identity.get("causal_history_seconds")
-        != float(profiles["causal_rolling_1s"]["history_seconds"])
-        or identity.get("ground_truth") is not False
-    ):
-        raise TgsInputError("mixed-route TGS source contract changed")
-    pack_path = source / "lidar-pack.npz"
-    if (
-        not pack_path.is_file()
-        or pack_path.stat().st_size != artifact.get("byte_length")
-        or sha256_file(pack_path) != artifact.get("sha256")
-    ):
-        raise TgsInputError("mixed-route LiDAR pack changed")
-
-    required = {
-        "frame_indices",
-        "source_frame_indices",
-        "session_seconds",
-        "lidar_session_seconds",
-        "sample_available",
-        "cloud_offsets",
-        "cloud_points_map",
-        "pose_positions_map",
-        "lidar_camera_delta_ms",
-        "pose_point_delta_ms",
-        "causal_history_seconds",
-        "causal_history_offsets",
-        "causal_history_points_map",
-    }
-    with np.load(pack_path, allow_pickle=False) as archive:
-        if not required.issubset(archive.files):
-            raise TgsInputError("mixed-route LiDAR pack members changed")
-        arrays = {name: archive[name] for name in required}
-    current_points = arrays["cloud_points_map"]
-    history_points = arrays["causal_history_points_map"]
-    if (
-        arrays["frame_indices"].shape != (FRAME_COUNT,)
-        or arrays["frame_indices"].dtype != np.int64
-        or not np.array_equal(arrays["frame_indices"], np.arange(FRAME_COUNT))
-        or arrays["source_frame_indices"].shape != (FRAME_COUNT,)
-        or arrays["source_frame_indices"].dtype != np.int64
-        or np.any(np.diff(arrays["source_frame_indices"]) <= 0)
-        or arrays["session_seconds"].shape != (FRAME_COUNT,)
-        or arrays["session_seconds"].dtype != np.float64
-        or np.any(np.diff(arrays["session_seconds"]) <= 0)
-        or arrays["lidar_session_seconds"].shape != (FRAME_COUNT,)
-        or arrays["lidar_session_seconds"].dtype != np.float64
-        or arrays["sample_available"].shape != (FRAME_COUNT,)
-        or arrays["sample_available"].dtype != np.bool_
-        or not arrays["sample_available"].all()
-        or current_points.ndim != 2
-        or current_points.shape[1:] != (3,)
-        or current_points.dtype != np.float32
-        or history_points.ndim != 2
-        or history_points.shape[1:] != (3,)
-        or history_points.dtype != np.float32
-        or not np.isfinite(current_points).all()
-        or not np.isfinite(history_points).all()
-        or not _validate_offsets(arrays["cloud_offsets"], current_points.shape[0])
-        or not _validate_offsets(
-            arrays["causal_history_offsets"], history_points.shape[0]
-        )
-        or arrays["pose_positions_map"].shape != (FRAME_COUNT, 3)
-        or arrays["pose_positions_map"].dtype != np.float64
-        or not np.isfinite(arrays["pose_positions_map"]).all()
-        or arrays["causal_history_seconds"].shape != (1,)
-        or float(arrays["causal_history_seconds"][0])
-        != float(profiles["causal_rolling_1s"]["history_seconds"])
-        or np.any(np.abs(arrays["lidar_camera_delta_ms"]) > 100.0)
-        or np.any(np.abs(arrays["pose_point_delta_ms"]) > 100.0)
-    ):
-        raise TgsInputError("mixed-route LiDAR arrays changed")
-
-    records: list[dict[str, object]] = []
-    for profile_id in ("current_increment", "causal_rolling_1s"):
-        for slot in range(FRAME_COUNT):
-            if profile_id == "current_increment":
-                points_map = _slice(
-                    current_points, arrays["cloud_offsets"], slot
-                )
-            else:
-                points_map = _slice(
-                    history_points, arrays["causal_history_offsets"], slot
-                )
-                radius = float(profiles[profile_id]["local_radius_m"])
-                relative_xy = (
-                    points_map[:, :2].astype(np.float64)
-                    - arrays["pose_positions_map"][slot, :2]
-                )
-                points_map = points_map[np.linalg.norm(relative_xy, axis=1) <= radius]
-            native = gravity_local_xyzi(
-                points_map, arrays["pose_positions_map"][slot]
-            )
-            if native.shape[0] == 0:
-                raise TgsInputError("mixed-route TGS profile produced an empty cloud")
-            target = (
-                output_root
-                / "profiles"
-                / profile_id
-                / "velodyne"
-                / f"{slot:06d}.bin"
-            )
-            target.parent.mkdir(parents=True, exist_ok=True)
-            target.write_bytes(np.ascontiguousarray(native).tobytes())
-            records.append(
-                {
-                    "profile_id": profile_id,
-                    "slot": slot,
-                    "frame_index": slot,
-                    "source_frame_index": int(
-                        arrays["source_frame_indices"][slot]
-                    ),
-                    "source_sequence": int(
-                        arrays["source_frame_indices"][slot]
-                    )
-                    + 1,
-                    "session_seconds": float(arrays["session_seconds"][slot]),
-                    "lidar_session_seconds": float(
-                        arrays["lidar_session_seconds"][slot]
-                    ),
-                    "lidar_camera_delta_ms": float(
-                        arrays["lidar_camera_delta_ms"][slot]
-                    ),
-                    "pose_point_delta_ms": float(
-                        arrays["pose_point_delta_ms"][slot]
-                    ),
-                    "point_count": int(native.shape[0]),
-                    "relative_path": target.relative_to(output_root).as_posix(),
-                    "bytes": target.stat().st_size,
-                    "sha256": sha256_file(target),
-                }
-            )
-    manifest_out = {
-        "schema_version": INPUT_SCHEMA,
-        "source_pack_id": manifest["pack_id"],
-        "source_pack_sha256": artifact["sha256"],
-        "config_sha256": sha256_file(config_path),
-        "coordinate_frame": "map-gravity-local",
-        "transform": "translation-only-preserve-map-gravity-axis",
-        "intensity_policy": "zero-filled-algorithm-compatibility-only",
-        "future_frames_used": False,
-        "frame_count": FRAME_COUNT,
-        "profile_count": 2,
-        "records": records,
-        "authority": {
-            "navigation_or_safety_accepted": False,
-            "actuation_allowed": False,
-        },
-    }
-    manifest_path = output_root / "input-manifest.json"
-    manifest_path.write_text(
-        json.dumps(manifest_out, indent=2, sort_keys=True) + "\n",
-        encoding="utf-8",
-    )
-    return manifest_out
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--source-root", type=Path, required=True)
-    parser.add_argument("--config", type=Path, required=True)
-    parser.add_argument("--output-root", type=Path, required=True)
-    args = parser.parse_args()
-    manifest = prepare(args.source_root, args.config, args.output_root)
-    print(json.dumps({"ok": True, "records": len(manifest["records"])}, sort_keys=True))
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

experiments/perception/worker/m49_t3_travel/run_tgs_integrated_shadow.sh

@@ -5,9 +5,6 @@ readonly BINARY=/shared/bin/run_tgs_full_shadow
 readonly INPUT_ROOT=/shared/tgs/inputs
 readonly OUTPUT_ROOT=/shared/tgs/outputs/causal_rolling_1s
 readonly TIMING_PATH=/shared/tgs/tgs-full-timing.tsv
-readonly RUNTIME_ROOT=/tmp/m49-tgs-runtime
-readonly RUNTIME_OUTPUT_ROOT=${RUNTIME_ROOT}/outputs
-readonly RUNTIME_TIMING_PATH=${RUNTIME_ROOT}/tgs-full-timing.tsv
 readonly READY_FILE=/shared/control/tgs.ready
 readonly START_FILE=/shared/control/start.signal
 readonly SOURCE_RATE_HZ=${M49_SOURCE_RATE_HZ:-12.0}
@@ -18,20 +15,12 @@ test -f "${INPUT_ROOT}/schedule.tsv"
 test ! -e /shared/tgs/outputs
 test ! -e "${TIMING_PATH}"
 test ! -e "${READY_FILE}"
-test ! -e "${RUNTIME_ROOT}"
-mkdir -p "${RUNTIME_OUTPUT_ROOT}"
-/usr/bin/time -v "${BINARY}" \
+mkdir -p "${OUTPUT_ROOT}"
+exec /usr/bin/time -v "${BINARY}" \
   "${INPUT_ROOT}/profiles/causal_rolling_1s" \
   "${INPUT_ROOT}/schedule.tsv" \
-  "${RUNTIME_OUTPUT_ROOT}" \
-  "${RUNTIME_TIMING_PATH}" \
+  "${OUTPUT_ROOT}" \
+  "${TIMING_PATH}" \
   "${SOURCE_RATE_HZ}" \
   "${READY_FILE}" \
   "${START_FILE}"
-test -f "${RUNTIME_TIMING_PATH}"
-mkdir -p "${OUTPUT_ROOT}"
-copy_started=$(date +%s%N)
-cp -R "${RUNTIME_OUTPUT_ROOT}/." "${OUTPUT_ROOT}/"
-cp "${RUNTIME_TIMING_PATH}" "${TIMING_PATH}"
-copy_completed=$(date +%s%N)
-echo "[TGS-FULL] evidence_copy_ms=$(((copy_completed - copy_started) / 1000000))"

scripts/build_lab_v1_vegetation_integrated_worker_artifact.py

@@ -1,189 +0,0 @@
-#!/usr/bin/env python3
-"""Build a clean-revision Worker 006 release for the DDRNet + M49 load gate."""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-import subprocess
-import sys
-import tempfile
-from pathlib import Path
-
-SCRIPT_ROOT = Path(__file__).resolve().parent
-if str(SCRIPT_ROOT) not in sys.path:
-    sys.path.insert(0, str(SCRIPT_ROOT))
-
-from build_m49_tgs_integrated_graph_worker_artifact import (  # noqa: E402
-    PATCH_ID,
-    REPOSITORY_ROOT,
-    WHEEL_NAME,
-    ArtifactBuildError,
-    build_wheel,
-    git_revision,
-    materialize_revision,
-    sha256_file,
-    write_archive,
-)
-from build_m49_tgs_integrated_graph_worker_artifact import (  # noqa: E402
-    SOURCES as M49_SOURCES,
-)
-
-SOURCES = M49_SOURCES + (
-    Path(
-        "experiments/perception/worker/lab_v1_vegetation_goose/"
-        "run_goose_vegetation_benchmark.py"
-    ),
-    Path(
-        "experiments/perception/worker/lab_v1_vegetation_goose/"
-        "run_vegetation_integrated_load.py"
-    ),
-    Path(
-        "experiments/perception/worker/m49_t3_travel/"
-        "build_vegetation_integrated_graph_evidence.py"
-    ),
-    Path("config/perception/lab-v1-goose-vegetation-benchmark-v1.json"),
-    Path("config/perception/lab-v1-vegetation-mission-policy-v1.json"),
-    Path("config/perception/lab-v1-vegetation-provider-label-map-v1.json"),
-    Path("config/perception/lab-v1-vegetation-integrated-multirate-phased-shadow-v3.json"),
-)
-
-
-def build_artifact(
-    patch_id: str,
-    output_directory: Path,
-    *,
-    revision: str | None = None,
-    source_root: Path | None = None,
-) -> dict[str, object]:
-    if PATCH_ID.fullmatch(patch_id) is None:
-        raise ArtifactBuildError("patch id is invalid")
-    selected_revision = revision or git_revision()
-    if re.fullmatch(r"[a-f0-9]{40}", selected_revision) is None:
-        raise ArtifactBuildError("artifact revision is invalid")
-    with tempfile.TemporaryDirectory(prefix="mission-core-vegetation-integrated-") as directory:
-        stage = Path(directory)
-        snapshot = source_root
-        if snapshot is None:
-            snapshot = stage / "source"
-            materialize_revision(selected_revision, snapshot)
-        sources = tuple(snapshot / relative for relative in SOURCES)
-        if any(path.is_symlink() or not path.is_file() for path in sources):
-            raise ArtifactBuildError("release input is not a regular file")
-        payload = stage / "payload"
-        payload.mkdir()
-        wheel = build_wheel(snapshot, stage / "wheel")
-        copied: list[Path] = []
-        for source in sources:
-            destination = payload / source.name
-            if destination.exists():
-                raise ArtifactBuildError("release payload file names are not unique")
-            destination.write_bytes(source.read_bytes())
-            copied.append(destination)
-        wheel_destination = payload / WHEEL_NAME
-        wheel_destination.write_bytes(wheel.read_bytes())
-        copied.append(wheel_destination)
-        release = {
-            "schema_version": "missioncore.lab-v1-vegetation-integrated-worker-release/v3",
-            "patch_id": patch_id,
-            "transition": "lab-v1-vegetation-m49-integrated-multirate-phased-shadow/v3",
-            "code_revision": selected_revision,
-            "worker_id": "worker-006",
-            "source_pack_sha256": (
-                "0685d24219d8236caf8b7f1685e93f6d6b59e7fd015a768d88a92bbe8b154944"
-            ),
-            "video_sha256": (
-                "cadd1696ff000904eb78633a0a8418104b8024f178b91f3421789021ccb160e8"
-            ),
-            "expected_frames": 4489,
-            "requested_source_rate_hz": 12.0,
-            "semantic_inference_rate_hz": 6.0,
-            "semantic_inference_stride": 2,
-            "semantic_inference_phase_offset_ms": 40.0,
-            "native_engine_sha256": (
-                "b8a40b3580edff001ec9680de68707242294ff590ab296000fae371f1083f695"
-            ),
-            "ddrnet_checkpoint_sha256": (
-                "b99c2838051bcd7b092fd3970aa62a77d5c0bbb809c9b9afb2ff4b0ebdaa4ee6"
-            ),
-            "images": {
-                "travel": (
-                    "sha256:7b412020f4d8392d1d1ed1b33beadc44140f0ea8f781e62dd69796042334300f"
-                ),
-                "parity": (
-                    "sha256:ceb13548617e4bd3f619766bfdff00af3fa5160946b367828da6d2233dcdcba0"
-                ),
-                "runtime": (
-                    "sha256:58df7489c3f2276f9591d500a012dee03e23d35543ce3c390b4c001e6bf90794"
-                ),
-                "vegetation": (
-                    "sha256:591cb382c099eeb05e7ec16e2371e0b2da54d2bb5c49ec0f4ac88dbf72b0f0cd"
-                ),
-            },
-            "authority": {
-                "visual_quality_accepted": False,
-                "route_truth_available": False,
-                "traversability_accepted": False,
-                "physical_free_space_accepted": False,
-                "commands_enabled": False,
-                "actuation_allowed": False,
-                "navigation_or_safety_accepted": False,
-                "production_accepted": False,
-            },
-            "scope": {
-                "gauss_or_playcanvas_action": "none",
-                "durable_worker_action": "none",
-                "canonical_triton_action": "none",
-                "heavy_vegetation_candidates": ["ddrnet"],
-            },
-            "files": {
-                path.name: {"sha256": sha256_file(path), "bytes": path.stat().st_size}
-                for path in sorted(copied)
-            },
-        }
-        release_path = payload / "release.json"
-        release_path.write_text(
-            json.dumps(release, indent=2, sort_keys=True) + "\n", encoding="utf-8"
-        )
-        payload_files = sorted((*release["files"], release_path.name))
-        (stage / "manifest.env").write_text(
-            f"id={patch_id}\ncomponent=mission-core-worker\ntype=shadow-release\n",
-            encoding="utf-8",
-        )
-        (stage / "files.txt").write_text(
-            "\n".join(payload_files) + "\n", encoding="utf-8"
-        )
-        target = output_directory.resolve() / f"nodedc-{patch_id}.tgz"
-        write_archive(stage, target)
-    return {
-        "ok": True,
-        "patch_id": patch_id,
-        "artifact": str(target),
-        "sha256": sha256_file(target),
-        "code_revision": selected_revision,
-        "wheel_sha256": release["files"][WHEEL_NAME]["sha256"],
-        "payload_files": payload_files,
-        "transition": release["transition"],
-    }
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("patch_id")
-    parser.add_argument(
-        "--output-directory",
-        type=Path,
-        default=REPOSITORY_ROOT / ".runtime/worker-artifacts",
-    )
-    arguments = parser.parse_args()
-    try:
-        result = build_artifact(arguments.patch_id, arguments.output_directory)
-    except (ArtifactBuildError, OSError, subprocess.SubprocessError) as exc:
-        parser.error(str(exc))
-    print(json.dumps(result, indent=2, sort_keys=True))
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

scripts/prepare_mixed_route_review_pack.py

@@ -1,296 +0,0 @@
-#!/usr/bin/env python3
-"""Publish exact, independently decodable camera islands for mixed-route review."""
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import os
-import shutil
-import subprocess
-import tempfile
-from datetime import UTC, datetime
-from pathlib import Path
-from typing import Any
-
-from PIL import Image
-
-from k1link.compute.jobs import validate_camera_compute_job
-from k1link.device_plugins.xgrids_k1.mqtt.capture import read_capture_clock_origin
-
-SCHEMA = "missioncore.mixed-route-review-pack/v1"
-MAX_INDEX_LINE_BYTES = 64 * 1024
-
-
-class MixedRouteReviewPackError(RuntimeError):
-    """The selected camera evidence cannot be published without ambiguity."""
-
-
-def _canonical_json(value: object) -> bytes:
-    return json.dumps(
-        value,
-        ensure_ascii=False,
-        sort_keys=True,
-        separators=(",", ":"),
-        allow_nan=False,
-    ).encode("utf-8")
-
-
-def _sha256(path: Path) -> str:
-    digest = hashlib.sha256()
-    with path.open("rb") as stream:
-        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
-            digest.update(chunk)
-    return digest.hexdigest()
-
-
-def _sequences(value: str) -> tuple[int, ...]:
-    try:
-        sequences = tuple(int(item) for item in value.split(","))
-    except ValueError as exc:
-        raise argparse.ArgumentTypeError("sequences must be comma-separated integers") from exc
-    if not sequences or any(item < 1 for item in sequences):
-        raise argparse.ArgumentTypeError("sequences must be positive")
-    if len(set(sequences)) != len(sequences) or tuple(sorted(sequences)) != sequences:
-        raise argparse.ArgumentTypeError("sequences must be unique and increasing")
-    return sequences
-
-
-def _arguments() -> argparse.Namespace:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--job", type=Path, required=True)
-    parser.add_argument("--session", type=Path, required=True)
-    parser.add_argument("--sequences", type=_sequences, required=True)
-    parser.add_argument("--output-root", type=Path, required=True)
-    parser.add_argument("--ffmpeg", type=Path, required=True)
-    return parser.parse_args()
-
-
-def _read_selected_index(
-    path: Path,
-    sequences: tuple[int, ...],
-) -> list[dict[str, Any]]:
-    wanted = set(sequences)
-    selected: dict[int, dict[str, Any]] = {}
-    with path.open("rb") as stream:
-        for expected_sequence, line in enumerate(stream, start=1):
-            if len(line) > MAX_INDEX_LINE_BYTES or not line.endswith(b"\n"):
-                raise MixedRouteReviewPackError("camera index line is invalid")
-            if expected_sequence not in wanted:
-                continue
-            try:
-                value = json.loads(line)
-            except json.JSONDecodeError as exc:
-                raise MixedRouteReviewPackError("camera index JSON is invalid") from exc
-            if (
-                not isinstance(value, dict)
-                or value.get("schema_version")
-                != "missioncore.camera-recording-index/v1"
-                or value.get("kind") != "media"
-                or value.get("sequence") != expected_sequence
-                or value.get("path") != f"segments/{expected_sequence}.m4s"
-                or not isinstance(value.get("session_monotonic_ns"), int)
-                or not isinstance(value.get("host_monotonic_ns"), int)
-                or not isinstance(value.get("host_epoch_ns"), int)
-            ):
-                raise MixedRouteReviewPackError("selected camera index row changed")
-            selected[expected_sequence] = value
-    if tuple(sorted(selected)) != sequences:
-        raise MixedRouteReviewPackError("selected camera sequence is incomplete")
-    return [selected[sequence] for sequence in sequences]
-
-
-def _decode_exact_fragment(
-    *,
-    ffmpeg: Path,
-    init_path: Path,
-    segment_path: Path,
-    output_path: Path,
-) -> None:
-    input_value = f"concat:{init_path}|{segment_path}"
-    completed = subprocess.run(
-        [
-            os.fspath(ffmpeg),
-            "-hide_banner",
-            "-loglevel",
-            "error",
-            "-nostdin",
-            "-y",
-            "-i",
-            input_value,
-            "-frames:v",
-            "1",
-            os.fspath(output_path),
-        ],
-        capture_output=True,
-        text=True,
-        timeout=30,
-        check=False,
-    )
-    if completed.returncode != 0 or not output_path.is_file():
-        detail = completed.stderr.strip().splitlines()[-1:] or ["no decoded frame"]
-        raise MixedRouteReviewPackError(
-            f"selected fragment is not independently decodable: {segment_path.name}: {detail[0]}"
-        )
-    with Image.open(output_path) as image:
-        if image.mode != "RGB" or image.size != (800, 600):
-            raise MixedRouteReviewPackError("selected camera frame shape changed")
-
-
-def prepare(
-    *,
-    job_root: Path,
-    session_root: Path,
-    sequences: tuple[int, ...],
-    output_root: Path,
-    ffmpeg_path: Path,
-) -> Path:
-    job = validate_camera_compute_job(job_root)
-    session = session_root.resolve(strict=True)
-    if not session.is_dir() or session.name != job.session_id:
-        raise MixedRouteReviewPackError("camera job and observation session differ")
-    capture_root = session / "captures" / "mqtt_live"
-    origin_path = capture_root / "mqtt.timeline.origin.json"
-    origin = read_capture_clock_origin(origin_path)
-    if sequences[-1] > job.segment_count:
-        raise MixedRouteReviewPackError("selected sequence escapes the camera epoch")
-    ffmpeg = ffmpeg_path.resolve(strict=True)
-    if not ffmpeg.is_file():
-        raise MixedRouteReviewPackError("ffmpeg is unavailable")
-    epoch_root = (
-        job.job_root
-        / "input"
-        / "camera"
-        / job.source_id
-        / f"epoch-{job.codec_epoch}"
-    )
-    selected = _read_selected_index(epoch_root / "index.jsonl", sequences)
-    identity = {
-        "schema_version": SCHEMA,
-        "job_id": job.job_id,
-        "input_sha256": job.input_sha256,
-        "session_id": job.session_id,
-        "source_id": job.source_id,
-        "codec_epoch": job.codec_epoch,
-        "clock_origin": {
-            "artifact_sha256": _sha256(origin_path),
-            "started_epoch_ns": origin.started_at_epoch_ns,
-            "started_monotonic_ns": origin.started_monotonic_ns,
-        },
-        "selected_sequences": list(sequences),
-        "selection_policy": "exact-independently-decodable-fragments/v1",
-        "ground_truth": False,
-        "authority": {
-            "navigation_or_safety_accepted": False,
-            "actuation_allowed": False,
-        },
-        "producer_sha256": _sha256(Path(__file__).resolve(strict=True)),
-    }
-    identity_sha256 = hashlib.sha256(_canonical_json(identity)).hexdigest()
-    pack_id = f"mixed-route-review-pack-{identity_sha256}"
-    parent = output_root.resolve()
-    parent.mkdir(mode=0o700, parents=True, exist_ok=True)
-    final = parent / pack_id
-    if final.exists():
-        return final
-    staging = Path(tempfile.mkdtemp(prefix=f".{pack_id}.", dir=parent))
-    published = False
-    try:
-        frames_root = staging / "frames"
-        frames_root.mkdir(mode=0o700)
-        timeline_rows: list[dict[str, Any]] = []
-        artifacts: list[dict[str, Any]] = []
-        for frame_index, (sequence, row) in enumerate(
-            zip(sequences, selected, strict=True)
-        ):
-            output_path = frames_root / f"frame-{frame_index + 1:06d}.png"
-            segment_path = epoch_root / "segments" / f"{sequence}.m4s"
-            _decode_exact_fragment(
-                ffmpeg=ffmpeg,
-                init_path=epoch_root / "init.mp4",
-                segment_path=segment_path,
-                output_path=output_path,
-            )
-            host_monotonic_ns = int(row["host_monotonic_ns"])
-            if host_monotonic_ns < origin.started_monotonic_ns:
-                raise MixedRouteReviewPackError("selected frame predates the session clock origin")
-            session_seconds = (
-                host_monotonic_ns - origin.started_monotonic_ns
-            ) / 1e9
-            timeline_rows.append(
-                {
-                    "frame_index": frame_index,
-                    "sequence": frame_index + 1,
-                    "source_frame_index": sequence - 1,
-                    "source_sequence": sequence,
-                    "session_seconds": session_seconds,
-                    "host_monotonic_ns": row["host_monotonic_ns"],
-                    "host_epoch_ns": row["host_epoch_ns"],
-                }
-            )
-            artifacts.append(
-                {
-                    "path": output_path.relative_to(staging).as_posix(),
-                    "byte_length": output_path.stat().st_size,
-                    "sha256": _sha256(output_path),
-                    "source_segment_sha256": row["sha256"],
-                }
-            )
-        timeline_path = staging / "timeline.jsonl"
-        timeline_path.write_text(
-            "".join(
-                json.dumps(
-                    row,
-                    ensure_ascii=False,
-                    sort_keys=True,
-                    separators=(",", ":"),
-                )
-                + "\n"
-                for row in timeline_rows
-            ),
-            encoding="utf-8",
-        )
-        manifest = {
-            "schema_version": SCHEMA,
-            "pack_id": pack_id,
-            "identity_sha256": identity_sha256,
-            "identity": identity,
-            "created_at_utc": datetime.now(UTC)
-            .isoformat(timespec="milliseconds")
-            .replace("+00:00", "Z"),
-            "frame_count": len(sequences),
-            "timeline": {
-                "path": timeline_path.name,
-                "byte_length": timeline_path.stat().st_size,
-                "sha256": _sha256(timeline_path),
-            },
-            "frames": artifacts,
-        }
-        (staging / "manifest.json").write_text(
-            json.dumps(manifest, ensure_ascii=False, sort_keys=True, indent=2) + "\n",
-            encoding="utf-8",
-        )
-        os.replace(staging, final)
-        published = True
-    finally:
-        if not published:
-            shutil.rmtree(staging, ignore_errors=True)
-    return final
-
-
-def main() -> int:
-    args = _arguments()
-    result = prepare(
-        job_root=args.job,
-        session_root=args.session,
-        sequences=args.sequences,
-        output_root=args.output_root,
-        ffmpeg_path=args.ffmpeg,
-    )
-    print(json.dumps({"pack_id": result.name, "output": os.fspath(result)}, sort_keys=True))
-    return 0
-
-
-if __name__ == "__main__":
-    raise SystemExit(main())

scripts/seal_mixed_route_full_video_review.py

@@ -1,38 +0,0 @@
-#!/usr/bin/env python3
-"""Seal the complete RAVNOVES004TREE semantic pass into existing LAB V1."""
-
-from __future__ import annotations
-
-import argparse
-from pathlib import Path
-
-from k1link.laboratory.mixed_route_vegetation_review import (
-    seal_mixed_route_full_video_review,
-)
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--base-lab-root", type=Path, required=True)
-    parser.add_argument("--job-root", type=Path, required=True)
-    parser.add_argument("--recorded-media-preparation", type=Path, required=True)
-    parser.add_argument("--eomt-root", type=Path, required=True)
-    parser.add_argument("--eomt-profile", type=Path, required=True)
-    parser.add_argument("--ddrnet-root", type=Path, required=True)
-    parser.add_argument("--output-root", type=Path, required=True)
-    args = parser.parse_args()
-    print(
-        seal_mixed_route_full_video_review(
-            base_lab_root=args.base_lab_root,
-            job_root=args.job_root,
-            recorded_media_preparation_path=args.recorded_media_preparation,
-            eomt_root=args.eomt_root,
-            eomt_profile_path=args.eomt_profile,
-            ddrnet_root=args.ddrnet_root,
-            output_root=args.output_root,
-        )
-    )
-
-
-if __name__ == "__main__":
-    main()

src/k1link/laboratory/mixed_route_vegetation_review.py

@@ -1,901 +0,0 @@
-"""Seal RAVNOVES004TREE mixed-route review into the existing vegetation LAB."""
-
-from __future__ import annotations
-
-import argparse
-import hashlib
-import json
-import os
-import shutil
-import struct
-import tarfile
-import tempfile
-import zipfile
-from datetime import UTC, datetime
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-from PIL import Image, ImageDraw
-
-from k1link.compute.jobs import validate_camera_compute_job
-
-from k1link.laboratory.vegetation_shadow_lab import (
-    LAB_SCHEMA,
-    RESULT_PREFIX,
-    VegetationShadowLabError,
-    canonical_json,
-    sha256_path,
-)
-
-REVIEW_SCHEMA = "missioncore.mixed-route-review-pack/v1"
-DDRNET_SCHEMA = "missioncore.mixed-route-ddrnet-islands/v1"
-TGS_SCHEMA = "missioncore.mixed-route-tgs-result/v1"
-FRAME_COUNT = 10
-PHASES = (
-    "rural",
-    "rural",
-    "rural",
-    "rural",
-    "rural",
-    "transition",
-    "urban",
-    "urban",
-    "urban",
-    "urban",
-)
-TGS_COLORS = {
-    0: (5, 7, 9),
-    1: (132, 188, 86),
-    2: (235, 112, 122),
-    3: (150, 154, 163),
-}
-FULL_ROUTE_SOURCE_ID = "RAVNOVES004TREE"
-FULL_ROUTE_FRAME_COUNT = 6830
-FULL_ROUTE_JOB_ID = "recorded-camera-eb2783c5480d56bda07c8af0"
-FULL_ROUTE_INPUT_SHA256 = (
-    "eb2783c5480d56bda07c8af008dff5344d19dc550ef70fe2075d6f098f7cc715"
-)
-FULL_ROUTE_STREAM_SHA256 = (
-    "e5eb017e2cc0f546736eda5235ca157b501913093cb64af5e548e335417e1bac"
-)
-
-
-def _read_json(path: Path, label: str) -> dict[str, Any]:
-    try:
-        value = json.loads(path.read_text(encoding="utf-8-sig"))
-    except (OSError, json.JSONDecodeError) as exc:
-        raise VegetationShadowLabError(f"{label} is invalid") from exc
-    if not isinstance(value, dict):
-        raise VegetationShadowLabError(f"{label} must be an object")
-    return value
-
-
-def _artifact(
-    source: Path,
-    staging: Path,
-    relative: str,
-    artifacts: list[dict[str, object]],
-    *,
-    role: str,
-    media_type: str,
-) -> dict[str, object]:
-    if source.is_symlink() or not source.is_file():
-        raise VegetationShadowLabError(f"mixed-route artifact is unavailable: {relative}")
-    target = staging / relative
-    target.parent.mkdir(mode=0o700, parents=True, exist_ok=True)
-    shutil.copyfile(source, target)
-    descriptor = {
-        "role": role,
-        "path": relative,
-        "byte_length": target.stat().st_size,
-        "sha256": sha256_path(target),
-        "media_type": media_type,
-    }
-    artifacts.append(descriptor)
-    return descriptor
-
-
-def _image_proof(descriptor: dict[str, object]) -> dict[str, object]:
-    return {"path": descriptor["path"], "sha256": descriptor["sha256"]}
-
-
-def _mask_archive_descriptor(
-    path: Path,
-    relative: str,
-    artifacts: list[dict[str, object]],
-    *,
-    role: str,
-) -> dict[str, object]:
-    descriptor = {
-        "role": role,
-        "path": relative,
-        "byte_length": path.stat().st_size,
-        "sha256": sha256_path(path),
-        "media_type": "application/zip",
-    }
-    artifacts.append(descriptor)
-    return descriptor
-
-
-def _repack_eomt_masks(source: Path, destination: Path, frame_count: int) -> None:
-    destination.parent.mkdir(mode=0o700, parents=True, exist_ok=True)
-    expected = [f"semantic-masks/frame-{sequence + 1:06d}.png" for sequence in range(frame_count)]
-    try:
-        with (
-            tarfile.open(source, mode="r:gz") as archive,
-            zipfile.ZipFile(
-                destination,
-                mode="x",
-                compression=zipfile.ZIP_STORED,
-                allowZip64=True,
-            ) as output,
-        ):
-            members = [member for member in archive.getmembers() if member.isfile()]
-            if [member.name.removeprefix("./") for member in members] != expected:
-                raise VegetationShadowLabError("full-route EoMT mask sequence changed")
-            for member, expected_name in zip(members, expected, strict=True):
-                if member.size < 8 or member.size > 1024 * 1024:
-                    raise VegetationShadowLabError("full-route EoMT mask size changed")
-                stream = archive.extractfile(member)
-                if stream is None:
-                    raise VegetationShadowLabError("full-route EoMT mask is unavailable")
-                output.writestr(
-                    f"masks/{Path(expected_name).name}",
-                    stream.read(),
-                )
-    except (OSError, tarfile.TarError, zipfile.BadZipFile) as exc:
-        destination.unlink(missing_ok=True)
-        raise VegetationShadowLabError("full-route EoMT archive is invalid") from exc
-
-
-def _validate_zip_masks(path: Path, frame_count: int) -> None:
-    expected = [f"masks/frame-{sequence + 1:06d}.png" for sequence in range(frame_count)]
-    try:
-        with zipfile.ZipFile(path) as archive:
-            members = archive.infolist()
-            if (
-                [member.filename for member in members] != expected
-                or any(
-                    member.is_dir() or member.file_size < 8 or member.file_size > 1024 * 1024
-                    for member in members
-                )
-            ):
-                raise VegetationShadowLabError("full-route semantic mask sequence changed")
-    except (OSError, zipfile.BadZipFile) as exc:
-        raise VegetationShadowLabError("full-route semantic archive is invalid") from exc
-
-
-def _full_route_frame_times(media: dict[str, Any], frame_count: int) -> list[int]:
-    epochs = media.get("epochs")
-    start = media.get("timeline_start_seconds")
-    end = media.get("timeline_end_seconds")
-    if (
-        not isinstance(epochs, list)
-        or len(epochs) != 1
-        or not isinstance(start, (int, float))
-        or not isinstance(end, (int, float))
-    ):
-        raise VegetationShadowLabError("recorded media timeline changed")
-    epoch = epochs[0]
-    segments = epoch.get("segments") if isinstance(epoch, dict) else None
-    if not isinstance(segments, list) or len(segments) != frame_count:
-        raise VegetationShadowLabError("recorded media segment count changed")
-    starts = [float(start)]
-    previous_end = 0.0
-    for sequence, raw in enumerate(segments, start=1):
-        if (
-            not isinstance(raw, dict)
-            or raw.get("sequence") != sequence
-            or not isinstance(raw.get("end_time_seconds"), (int, float))
-            or float(raw["end_time_seconds"]) <= previous_end
-        ):
-            raise VegetationShadowLabError("recorded media segment timeline changed")
-        if sequence < frame_count:
-            starts.append(float(start) + float(raw["end_time_seconds"]))
-        previous_end = float(raw["end_time_seconds"])
-    if abs((float(start) + previous_end) - float(end)) > 0.001:
-        raise VegetationShadowLabError("recorded media duration changed")
-    return [round(value * 1_000_000_000) for value in starts]
-
-
-def _eomt_taxonomy(profile: dict[str, Any]) -> dict[str, object]:
-    taxonomy = profile.get("target_taxonomy")
-    if not isinstance(taxonomy, dict) or set(taxonomy) != {str(index) for index in range(16)}:
-        raise VegetationShadowLabError("EoMT target taxonomy changed")
-    classes = []
-    for class_id in range(16):
-        digest = hashlib.sha256(f"mission-core-segment-{class_id}".encode()).digest()
-        classes.append(
-            {
-                "class_id": class_id,
-                "label": taxonomy[str(class_id)],
-                "color_rgb": [64 + digest[index] % 176 for index in range(3)],
-                "disposition": "undefined" if class_id == 0 else "prediction",
-            }
-        )
-    return {
-        "schema_version": "missioncore.recorded-eomt-taxonomy/v1",
-        "classes": classes,
-    }
-
-
-def _render_tgs_costmaps(tgs_root: Path, destination: Path) -> list[Path]:
-    result = _read_json(tgs_root / "result.json", "mixed-route TGS result")
-    evidence = result.get("evidence")
-    costmap = result.get("costmap")
-    if (
-        result.get("schema_version") != TGS_SCHEMA
-        or result.get("status") != "passed-review-only"
-        or not isinstance(evidence, dict)
-        or not isinstance(costmap, dict)
-        or result.get("summary", {}).get("frame_count") != FRAME_COUNT
-        or result.get("authority", {}).get("actuation_allowed") is not False
-    ):
-        raise VegetationShadowLabError("mixed-route TGS contract changed")
-    evidence_path = tgs_root / str(evidence.get("path"))
-    if (
-        not evidence_path.is_file()
-        or evidence.get("bytes") != evidence_path.stat().st_size
-        or evidence.get("sha256") != sha256_path(evidence_path)
-    ):
-        raise VegetationShadowLabError("mixed-route TGS evidence changed")
-    with np.load(evidence_path, allow_pickle=False) as archive:
-        centers = archive["costmap_cell_centers_xy_m"]
-        states = archive["causal_rolling_1s_costmap_states"]
-    if centers.shape != (2244, 2) or states.shape != (FRAME_COUNT, 2244):
-        raise VegetationShadowLabError("mixed-route TGS costmap shape changed")
-    radius = float(costmap["radius_m"])
-    cell_size = float(costmap["cell_size_m"])
-    size = 600
-    scale = size / (radius * 2.0)
-    outputs: list[Path] = []
-    destination.mkdir(mode=0o700, parents=True, exist_ok=True)
-    for slot in range(FRAME_COUNT):
-        image = Image.new("RGB", (size, size), TGS_COLORS[0])
-        draw = ImageDraw.Draw(image)
-        half = cell_size * scale / 2.0
-        for center, state in zip(centers, states[slot], strict=True):
-            x = (float(center[0]) + radius) * scale
-            y = (radius - float(center[1])) * scale
-            draw.rectangle((x - half, y - half, x + half, y + half), fill=TGS_COLORS[int(state)])
-        rover_w = 0.8 * scale
-        rover_l = 1.0 * scale
-        cx = size / 2.0
-        cy = size / 2.0
-        draw.rectangle(
-            (cx - rover_w / 2, cy - rover_l / 2, cx + rover_w / 2, cy + rover_l / 2),
-            outline=(255, 255, 255),
-            width=3,
-        )
-        path = destination / f"frame-{slot + 1:06d}.png"
-        image.save(path, format="PNG", optimize=True)
-        outputs.append(path)
-    return outputs
-
-
-def seal_mixed_route_vegetation_review(
-    *,
-    base_lab_root: Path,
-    review_pack_root: Path,
-    eomt_root: Path,
-    ddrnet_root: Path,
-    tgs_root: Path,
-    output_root: Path,
-) -> Path:
-    base_root = base_lab_root.resolve(strict=True)
-    base = _read_json(base_root / "result.json", "base vegetation LAB")
-    base_identity = base.get("identity")
-    if (
-        base.get("schema_version") != LAB_SCHEMA
-        or not isinstance(base_identity, dict)
-        or hashlib.sha256(canonical_json(base_identity)).hexdigest()
-        != base.get("identity_sha256")
-        or base.get("result_id") != base_root.name
-        or not base_root.name.startswith(RESULT_PREFIX)
-        or base.get("authority", {}).get("commands_enabled") is not False
-    ):
-        raise VegetationShadowLabError("base vegetation LAB proof changed")
-
-    pack_root = review_pack_root.resolve(strict=True)
-    pack = _read_json(pack_root / "manifest.json", "mixed-route review pack")
-    timeline_path = pack_root / str(pack.get("timeline", {}).get("path"))
-    if (
-        pack.get("schema_version") != REVIEW_SCHEMA
-        or pack.get("frame_count") != FRAME_COUNT
-        or pack.get("identity", {}).get("session_id") != "20260828T130511Z_viewer_live"
-        or pack.get("identity", {}).get("ground_truth") is not False
-        or not timeline_path.is_file()
-        or pack.get("timeline", {}).get("sha256") != sha256_path(timeline_path)
-    ):
-        raise VegetationShadowLabError("mixed-route review pack changed")
-    timeline = [json.loads(line) for line in timeline_path.read_text(encoding="utf-8").splitlines()]
-    if len(timeline) != FRAME_COUNT:
-        raise VegetationShadowLabError("mixed-route timeline is incomplete")
-
-    eomt = _read_json(eomt_root / "run-report.partial.json", "mixed-route EoMT result")
-    ddrnet = _read_json(ddrnet_root / "result.json", "mixed-route DDRNet result")
-    tgs = _read_json(tgs_root / "result.json", "mixed-route TGS result")
-    if (
-        eomt.get("input", {}).get("frames_admitted") != FRAME_COUNT
-        or eomt.get("metrics", {}).get("frames_processed") != FRAME_COUNT
-        or eomt.get("ground_truth") is not False
-        or ddrnet.get("schema_version") != DDRNET_SCHEMA
-        or ddrnet.get("source", {}).get("pack_id") != pack["pack_id"]
-        or len(ddrnet.get("frames", [])) != FRAME_COUNT
-        or ddrnet.get("authority", {}).get("candidate_accepted") is not False
-        or tgs.get("schema_version") != TGS_SCHEMA
-        or tgs.get("source", {}).get("review_pack_id") != pack["pack_id"]
-        or tgs.get("summary", {}).get("frame_count") != FRAME_COUNT
-    ):
-        raise VegetationShadowLabError("mixed-route model identities differ")
-
-    output_root.mkdir(mode=0o700, parents=True, exist_ok=True)
-    temporary = Path(tempfile.mkdtemp(prefix=".mixed-route-vegetation-", dir=output_root))
-    artifacts: list[dict[str, object]] = []
-    try:
-        tgs_images = _render_tgs_costmaps(tgs_root, temporary / ".tgs-render")
-        cases: list[dict[str, object]] = []
-        tgs_anchors = {
-            int(row["slot"]): row
-            for row in tgs["anchors"]
-            if row.get("profile_id") == "causal_rolling_1s"
-        }
-        for slot, row in enumerate(timeline):
-            case_id = f"route-{slot + 1:02d}"
-            relative_root = f"route-review/{case_id}"
-            source_descriptor = _artifact(
-                pack_root / "frames" / f"frame-{slot + 1:06d}.png",
-                temporary,
-                f"{relative_root}/source.png",
-                artifacts,
-                role="mixed-route-source-frame",
-                media_type="image/png",
-            )
-            city_descriptor = _artifact(
-                eomt_root / "overlay-frames" / f"frame-{slot + 1:06d}.png",
-                temporary,
-                f"{relative_root}/city.png",
-                artifacts,
-                role="mixed-route-eomt-overlay",
-                media_type="image/png",
-            )
-            vegetation_descriptor = _artifact(
-                ddrnet_root / "overlay-frames" / f"frame-{slot + 1:06d}.png",
-                temporary,
-                f"{relative_root}/vegetation.png",
-                artifacts,
-                role="mixed-route-ddrnet-overlay",
-                media_type="image/png",
-            )
-            tgs_descriptor = _artifact(
-                tgs_images[slot],
-                temporary,
-                f"{relative_root}/tgs.png",
-                artifacts,
-                role="mixed-route-tgs-costmap",
-                media_type="image/png",
-            )
-            anchor = tgs_anchors[slot]
-            cases.append(
-                {
-                    "case_id": case_id,
-                    "phase": PHASES[slot],
-                    "source_sequence": int(row["source_sequence"]),
-                    "session_seconds": float(row["session_seconds"]),
-                    "assets": {
-                        "source": _image_proof(source_descriptor),
-                        "city": _image_proof(city_descriptor),
-                        "vegetation": _image_proof(vegetation_descriptor),
-                        "tgs": _image_proof(tgs_descriptor),
-                    },
-                    "tgs": {
-                        "ground_cells": int(anchor["ground_cell_count"]),
-                        "occupied_cells": int(anchor["nonground_cell_count"]),
-                        "rejected_cells": int(anchor["rejected_cell_count"]),
-                        "unobserved_cells": int(anchor["unobserved_cell_count"]),
-                    },
-                }
-            )
-        shutil.rmtree(temporary / ".tgs-render")
-
-        proofs = {}
-        for key, path in (
-            ("base", base_root / "result.json"),
-            ("eomt", eomt_root / "run-report.partial.json"),
-            ("ddrnet", ddrnet_root / "result.json"),
-            ("tgs", tgs_root / "result.json"),
-        ):
-            descriptor = _artifact(
-                path,
-                temporary,
-                f"proofs/{key}.json",
-                artifacts,
-                role="mixed-route-proof",
-                media_type="application/json",
-            )
-            proofs[key] = _image_proof(descriptor)
-        _artifact(
-            tgs_root / str(tgs["evidence"]["path"]),
-            temporary,
-            "proofs/tgs-evidence.npz",
-            artifacts,
-            role="mixed-route-tgs-evidence",
-            media_type="application/x-npz",
-        )
-
-        route_review = {
-            "source_id": "RAVNOVES004TREE",
-            "session_id": "20260828T130511Z_viewer_live",
-            "pack_id": pack["pack_id"],
-            "frame_count": FRAME_COUNT,
-            "ground_truth": False,
-            "selection_policy": "same-scene-camera-lidar-aligned-review-islands/v1",
-            "models": {
-                "city": {
-                    "name": "EoMT Cityscapes",
-                    "frames": FRAME_COUNT,
-                    "inference_fps": eomt["metrics"]["inference_frames_per_second"],
-                    "end_to_end_p95_ms": eomt["metrics"]["latency_ms"]["end_to_end_ms"]["p95"],
-                },
-                "vegetation": {
-                    "name": ddrnet["candidate"]["loaded_model_name"],
-                    "result_id": ddrnet["result_id"],
-                    "frames": FRAME_COUNT,
-                    "latency_p95_ms": ddrnet["timing"]["latency_ms_p95"],
-                },
-                "tgs": {
-                    "name": "TRAVEL/TGS causal rolling 1 s",
-                    "frames": FRAME_COUNT,
-                    "latency_p95_ms": tgs["timing"]["wall_seconds_p95"] * 1000.0,
-                    "cell_size_m": tgs["costmap"]["cell_size_m"],
-                    "radius_m": tgs["costmap"]["radius_m"],
-                },
-            },
-            "cases": cases,
-            "proofs": proofs,
-            "limitations": [
-                "Ten aligned review islands are not a complete route timeline.",
-                "RAVNOVES004TREE has no manual truth.",
-                "DDRNet vegetation subtypes remain visually noisy and are not planner authority.",
-                "TGS does not prove ditch or negative-obstacle detection.",
-                "People and vehicles require an independent fail-safe detector and STOP path.",
-            ],
-        }
-        authority = {
-            "commands_enabled": False,
-            "navigation_or_safety_accepted": False,
-            "actuation_accepted": False,
-            "camera_semantics_can_clear_rigid_geometry": False,
-        }
-        identity = {
-            "lab_id": "lab-v1-vegetation-mission-policy",
-            "base_result_id": base["result_id"],
-            "selected_candidate": base_identity["selected_candidate"],
-            "candidate_metrics": base_identity["candidate_metrics"],
-            "source": {
-                "shadow_session": "RAVNOVES004TREE",
-                "shadow_camera": "sensor.camera.right",
-                "shadow_frame_count": FRAME_COUNT,
-                "video_shadow_frame_count": 0,
-            },
-            "route_review": route_review,
-            "authority": authority,
-        }
-        identity_sha256 = hashlib.sha256(canonical_json(identity)).hexdigest()
-        result_id = f"{RESULT_PREFIX}{identity_sha256}"
-        manifest = {
-            "schema_version": LAB_SCHEMA,
-            "result_id": result_id,
-            "identity_sha256": identity_sha256,
-            "created_at_utc": datetime.now(UTC).isoformat(),
-            "ground_truth": False,
-            "status": "visual-shadow-ready-policy-not-authorized",
-            "identity": identity,
-            "source": identity["source"],
-            "route_video": None,
-            "route_review": route_review,
-            "method": {
-                "completeness": "bounded-review-islands",
-                "execution_class": "ai-inference",
-                "pipeline_id": "ravnoves004tree-eomt-ddrnet-causal-tgs-review/v1",
-            },
-            "metrics": {"candidates": base["metrics"]["candidates"]},
-            "decision": {
-                "selected_candidate": base_identity["selected_candidate"],
-                "visual_shadow_ready": True,
-                "full_video_shadow_ready": False,
-                "mission_policy_ready_for_configuration": True,
-                "multilayer_policy_review_ready": True,
-                "navigation_accepted": False,
-                "production_accepted": False,
-            },
-            "limitations": route_review["limitations"],
-            "authority": authority,
-            "catalogs": {"goose": [], "ravnoves": []},
-            "artifacts": artifacts,
-        }
-        (temporary / "result.json").write_bytes(canonical_json(manifest) + b"\n")
-        destination = output_root / result_id
-        if destination.exists():
-            raise VegetationShadowLabError("immutable mixed-route LAB result already exists")
-        os.replace(temporary, destination)
-        return destination
-    except Exception:
-        shutil.rmtree(temporary, ignore_errors=True)
-        raise
-
-
-def seal_mixed_route_full_video_review(
-    *,
-    base_lab_root: Path,
-    job_root: Path,
-    recorded_media_preparation_path: Path,
-    eomt_root: Path,
-    eomt_profile_path: Path,
-    ddrnet_root: Path,
-    output_root: Path,
-) -> Path:
-    """Publish the complete 004 city/nature pass in the existing M4.7 LAB."""
-
-    base_root = base_lab_root.resolve(strict=True)
-    base = _read_json(base_root / "result.json", "base vegetation LAB")
-    base_identity = base.get("identity")
-    if (
-        base.get("schema_version") != LAB_SCHEMA
-        or not isinstance(base_identity, dict)
-        or hashlib.sha256(canonical_json(base_identity)).hexdigest()
-        != base.get("identity_sha256")
-        or base.get("result_id") != base_root.name
-        or not base_root.name.startswith(RESULT_PREFIX)
-        or base.get("authority", {}).get("commands_enabled") is not False
-    ):
-        raise VegetationShadowLabError("base vegetation LAB proof changed")
-
-    job = validate_camera_compute_job(job_root)
-    if (
-        job.job_id != FULL_ROUTE_JOB_ID
-        or job.input_sha256 != FULL_ROUTE_INPUT_SHA256
-        or job.session_id != "20260828T130511Z_viewer_live"
-        or job.source_id != "sensor.camera.right"
-        or job.segment_count != FULL_ROUTE_FRAME_COUNT
-    ):
-        raise VegetationShadowLabError("full-route camera job changed")
-
-    eomt = _read_json(eomt_root / "result.json", "full-route EoMT result")
-    eomt_report = _read_json(eomt_root / "run-report.json", "full-route EoMT report")
-    decode_repair = _read_json(
-        eomt_root / "decode-repair.json",
-        "full-route video decode repair",
-    )
-    eomt_input = eomt_report.get("input")
-    eomt_metrics = eomt_report.get("metrics")
-    if (
-        eomt.get("schema_version") != "missioncore.recorded-perception-result/v2"
-        or eomt.get("ground_truth") is not False
-        or eomt.get("frames_processed") != FULL_ROUTE_FRAME_COUNT
-        or not isinstance(eomt_input, dict)
-        or eomt_input.get("job_id") != job.job_id
-        or eomt_input.get("input_sha256") != job.input_sha256
-        or eomt_input.get("frames_admitted") != FULL_ROUTE_FRAME_COUNT
-        or not isinstance(eomt_metrics, dict)
-        or eomt_metrics.get("frames_processed") != FULL_ROUTE_FRAME_COUNT
-    ):
-        raise VegetationShadowLabError("full-route EoMT contract changed")
-    if (
-        decode_repair.get("schema_version")
-        != "missioncore.recorded-video-decode-repair/v1"
-        or decode_repair.get("decoder") != "ffmpeg-h264_cuvid-output-corrupt"
-        or decode_repair.get("packets_requested") != FULL_ROUTE_FRAME_COUNT
-        or decode_repair.get("frames_decoded") != FULL_ROUTE_FRAME_COUNT - 1
-        or decode_repair.get("repaired_frame_count") != 1
-        or decode_repair.get("repairs")
-        != [
-            {
-                "sequence": 6092,
-                "packet_pts": 55656450,
-                "method": "duplicate-previous-decoded-frame",
-            }
-        ]
-    ):
-        raise VegetationShadowLabError("full-route video decode repair changed")
-    eomt_artifacts = {
-        item.get("kind"): item
-        for item in eomt.get("artifacts", [])
-        if isinstance(item, dict)
-    }
-    eomt_archive_proof = eomt_artifacts.get("panoptic-mask-archive")
-    if not isinstance(eomt_archive_proof, dict):
-        raise VegetationShadowLabError("full-route EoMT mask proof is missing")
-    eomt_archive = eomt_root / str(eomt_archive_proof.get("path"))
-    if (
-        not eomt_archive.is_file()
-        or eomt_archive.stat().st_size != eomt_archive_proof.get("byte_length")
-        or sha256_path(eomt_archive) != eomt_archive_proof.get("sha256")
-    ):
-        raise VegetationShadowLabError("full-route EoMT mask proof changed")
-
-    ddrnet = _read_json(ddrnet_root / "result.json", "full-route DDRNet result")
-    ddrnet_decode_repair = _read_json(
-        ddrnet_root / "decode-repair.json",
-        "full-route DDRNet video decode repair",
-    )
-    ddrnet_source = ddrnet.get("source")
-    ddrnet_video = ddrnet.get("video_semantics")
-    if (
-        ddrnet.get("schema_version") != "missioncore.lab-v1-goose-vegetation-run/v1"
-        or ddrnet.get("mode") != "ravnoves-video"
-        or ddrnet.get("candidate", {}).get("candidate_key") != "ddrnet"
-        or not isinstance(ddrnet_source, dict)
-        or ddrnet_source.get("source_id")
-        != f"{FULL_ROUTE_SOURCE_ID}/right-{FULL_ROUTE_STREAM_SHA256}"
-        or ddrnet_source.get("input_count") != FULL_ROUTE_FRAME_COUNT
-        or ddrnet_source.get("ground_truth_available") is not False
-        or not isinstance(ddrnet_video, dict)
-        or ddrnet_video.get("base_m4_result_id") is not None
-        or ddrnet.get("authority", {}).get("navigation_accepted") is not False
-        or ddrnet.get("authority", {}).get("actuation_accepted") is not False
-    ):
-        raise VegetationShadowLabError("full-route DDRNet contract changed")
-    if ddrnet_decode_repair != decode_repair:
-        raise VegetationShadowLabError("full-route model decoders disagree")
-    ddrnet_archive_proof = ddrnet_video.get("mask_archive")
-    ddrnet_taxonomy = ddrnet_video.get("taxonomy")
-    if (
-        not isinstance(ddrnet_archive_proof, dict)
-        or ddrnet_archive_proof.get("frame_count") != FULL_ROUTE_FRAME_COUNT
-        or not isinstance(ddrnet_taxonomy, dict)
-    ):
-        raise VegetationShadowLabError("full-route DDRNet mask proof changed")
-    ddrnet_archive = ddrnet_root / str(ddrnet_archive_proof.get("path"))
-    if (
-        not ddrnet_archive.is_file()
-        or ddrnet_archive.stat().st_size != ddrnet_archive_proof.get("byte_length")
-        or sha256_path(ddrnet_archive) != ddrnet_archive_proof.get("sha256")
-    ):
-        raise VegetationShadowLabError("full-route DDRNet archive changed")
-    _validate_zip_masks(ddrnet_archive, FULL_ROUTE_FRAME_COUNT)
-
-    media_document = _read_json(
-        recorded_media_preparation_path.resolve(strict=True),
-        "recorded media preparation",
-    )
-    media = media_document.get("manifest")
-    if (
-        media_document.get("schema_version") != "missioncore.recorded-media-preparation/v3"
-        or media_document.get("session_id") != job.session_id
-        or media_document.get("artifact_id") != "recorded-video-6a3945242828a038"
-        or media_document.get("checksum_sha256")
-        != "557e61f2839140dc9f97b5aea855c576b0616573080dff5d2852ab1df0558665"
-        or not isinstance(media, dict)
-        or media.get("source_id") != "recorded.camera.6a3945242828a038"
-        or media.get("generation_sha256")
-        != "b073ea1e7babf1c77a664e1a5b95e3702d0e05b0e34c1e85a7c67a6f8b392ded"
-        or media.get("byte_length") != 551674491
-        or media.get("timeline_start_seconds") != job.timeline_start_seconds
-        or media.get("timeline_end_seconds") != job.timeline_end_seconds
-        or media.get("synchronization") != "host-arrival-best-effort"
-    ):
-        raise VegetationShadowLabError("recorded media preparation changed")
-    frame_times_ns = _full_route_frame_times(media, FULL_ROUTE_FRAME_COUNT)
-    eomt_profile = _read_json(eomt_profile_path.resolve(strict=True), "EoMT profile")
-    eomt_taxonomy = _eomt_taxonomy(eomt_profile)
-
-    output_root.mkdir(mode=0o700, parents=True, exist_ok=True)
-    temporary = Path(tempfile.mkdtemp(prefix=".mixed-route-full-video-", dir=output_root))
-    artifacts: list[dict[str, object]] = []
-    try:
-        eomt_destination = temporary / "video" / "eomt-semantic-masks.zip"
-        _repack_eomt_masks(eomt_archive, eomt_destination, FULL_ROUTE_FRAME_COUNT)
-        _validate_zip_masks(eomt_destination, FULL_ROUTE_FRAME_COUNT)
-        eomt_descriptor = _mask_archive_descriptor(
-            eomt_destination,
-            "video/eomt-semantic-masks.zip",
-            artifacts,
-            role="full-route-eomt-semantic-mask-archive",
-        )
-        ddrnet_descriptor = _artifact(
-            ddrnet_archive,
-            temporary,
-            "video/ddrnet-semantic-masks.zip",
-            artifacts,
-            role="full-route-ddrnet-semantic-mask-archive",
-            media_type="application/zip",
-        )
-        _validate_zip_masks(
-            temporary / "video" / "ddrnet-semantic-masks.zip",
-            FULL_ROUTE_FRAME_COUNT,
-        )
-        timeline_destination = temporary / "video" / "frame-source-times-ns.bin"
-        timeline_destination.write_bytes(
-            struct.pack(f"<{FULL_ROUTE_FRAME_COUNT}Q", *frame_times_ns)
-        )
-        timeline_descriptor = {
-            "role": "full-route-frame-timeline",
-            "path": "video/frame-source-times-ns.bin",
-            "byte_length": timeline_destination.stat().st_size,
-            "sha256": sha256_path(timeline_destination),
-            "media_type": "application/octet-stream",
-        }
-        artifacts.append(timeline_descriptor)
-        proof_descriptors: dict[str, dict[str, object]] = {}
-        for key, path in (
-            ("base", base_root / "result.json"),
-            ("job", job.manifest_path),
-            ("media", recorded_media_preparation_path.resolve(strict=True)),
-            ("eomt", eomt_root / "result.json"),
-            ("eomt_report", eomt_root / "run-report.json"),
-            ("decode_repair", eomt_root / "decode-repair.json"),
-            ("ddrnet", ddrnet_root / "result.json"),
-            ("ddrnet_decode_repair", ddrnet_root / "decode-repair.json"),
-        ):
-            descriptor = _artifact(
-                path,
-                temporary,
-                f"proofs/{key}.json",
-                artifacts,
-                role="full-route-proof",
-                media_type="application/json",
-            )
-            proof_descriptors[key] = _image_proof(descriptor)
-
-        full_route = {
-            "source_id": FULL_ROUTE_SOURCE_ID,
-            "session_id": job.session_id,
-            "source_job_id": job.job_id,
-            "source_job_input_sha256": job.input_sha256,
-            "source_stream_sha256": FULL_ROUTE_STREAM_SHA256,
-            "recorded_media_source_id": media["source_id"],
-            "recorded_media_generation_sha256": media["generation_sha256"],
-            "frame_count": FULL_ROUTE_FRAME_COUNT,
-            "width": 800,
-            "height": 600,
-            "timeline_start_seconds": job.timeline_start_seconds,
-            "timeline_end_seconds": job.timeline_end_seconds,
-            "timeline": {
-                "path": timeline_descriptor["path"],
-                "sha256": timeline_descriptor["sha256"],
-                "byte_length": timeline_descriptor["byte_length"],
-                "encoding": "uint64-le-nanoseconds",
-                "frame_count": FULL_ROUTE_FRAME_COUNT,
-            },
-            "ground_truth": False,
-            "decode_repair": {
-                "repaired_frame_count": 1,
-                "sequence": 6092,
-                "method": "duplicate-previous-decoded-frame",
-                "proofs": {
-                    "eomt": proof_descriptors["decode_repair"],
-                    "ddrnet": proof_descriptors["ddrnet_decode_repair"],
-                },
-            },
-            "layers": {
-                "city": {
-                    "name": "EoMT Cityscapes",
-                    "result_id": eomt["result_id"],
-                    "frame_count": FULL_ROUTE_FRAME_COUNT,
-                    "taxonomy": eomt_taxonomy,
-                    "mask_archive": {
-                        "path": eomt_descriptor["path"],
-                        "sha256": eomt_descriptor["sha256"],
-                        "byte_length": eomt_descriptor["byte_length"],
-                    },
-                    "inference_fps": eomt_metrics["inference_frames_per_second"],
-                    "latency_p95_ms": eomt_metrics["latency_ms"]["end_to_end_ms"]["p95"],
-                    "peak_reserved_vram_bytes": int(
-                        float(eomt_metrics["cuda_peak_memory_reserved_mib"]) * 1024 * 1024
-                    ),
-                },
-                "vegetation": {
-                    "name": ddrnet["candidate"]["loaded_model_name"],
-                    "result_id": ddrnet["result_id"],
-                    "frame_count": FULL_ROUTE_FRAME_COUNT,
-                    "taxonomy": ddrnet_taxonomy,
-                    "mask_archive": {
-                        "path": ddrnet_descriptor["path"],
-                        "sha256": ddrnet_descriptor["sha256"],
-                        "byte_length": ddrnet_descriptor["byte_length"],
-                    },
-                    "inference_fps": ddrnet["timing"]["throughput_fps_from_mean_inference"],
-                    "latency_p95_ms": ddrnet["timing"]["latency_ms_p95"],
-                    "peak_reserved_vram_bytes": ddrnet["resource"]["peak_reserved_vram_bytes"],
-                },
-            },
-            "proofs": proof_descriptors,
-            "limitations": [
-                "RAVNOVES004TREE has no manual route truth.",
-                "One corrupt H.264 packet at sequence 6092 was represented by the previous decoded frame; the repair is sealed as evidence.",
-                "EoMT and DDRNet were executed sequentially, not as a concurrent realtime stack.",
-                "DDRNet vegetation subtypes remain prediction-only and are not planner authority.",
-                "This full-video pass does not add full-route TGS, ditch or negative-obstacle proof.",
-                "People and vehicles still require an independent fail-safe detector and STOP path.",
-            ],
-        }
-        authority = {
-            "commands_enabled": False,
-            "navigation_or_safety_accepted": False,
-            "actuation_accepted": False,
-            "camera_semantics_can_clear_rigid_geometry": False,
-        }
-        identity = {
-            "lab_id": "lab-v1-vegetation-mission-policy",
-            "base_result_id": base["result_id"],
-            "selected_candidate": base_identity["selected_candidate"],
-            "candidate_metrics": base_identity["candidate_metrics"],
-            "source": {
-                "shadow_session": FULL_ROUTE_SOURCE_ID,
-                "shadow_camera": job.source_id,
-                "shadow_frame_count": FULL_ROUTE_FRAME_COUNT,
-                "video_shadow_frame_count": FULL_ROUTE_FRAME_COUNT,
-            },
-            "route_full_review": full_route,
-            "authority": authority,
-        }
-        identity_sha256 = hashlib.sha256(canonical_json(identity)).hexdigest()
-        result_id = f"{RESULT_PREFIX}{identity_sha256}"
-        manifest = {
-            "schema_version": LAB_SCHEMA,
-            "result_id": result_id,
-            "identity_sha256": identity_sha256,
-            "created_at_utc": datetime.now(UTC).isoformat(),
-            "ground_truth": False,
-            "status": "visual-shadow-ready-policy-not-authorized",
-            "identity": identity,
-            "source": identity["source"],
-            "route_video": None,
-            "route_review": None,
-            "route_full_review": full_route,
-            "method": {
-                "completeness": "complete",
-                "execution_class": "ai-inference",
-                "pipeline_id": "ravnoves004tree-full-eomt-ddrnet-recorded-review/v1",
-            },
-            "metrics": {"candidates": base["metrics"]["candidates"]},
-            "decision": {
-                "selected_candidate": base_identity["selected_candidate"],
-                "visual_shadow_ready": True,
-                "full_video_shadow_ready": True,
-                "mission_policy_ready_for_configuration": True,
-                "multilayer_policy_review_ready": True,
-                "navigation_accepted": False,
-                "production_accepted": False,
-            },
-            "limitations": full_route["limitations"],
-            "authority": authority,
-            "catalogs": {"goose": [], "ravnoves": []},
-            "artifacts": artifacts,
-        }
-        (temporary / "result.json").write_bytes(canonical_json(manifest) + b"\n")
-        destination = output_root / result_id
-        if destination.exists():
-            raise VegetationShadowLabError("immutable full-route LAB result already exists")
-        os.replace(temporary, destination)
-        return destination
-    except Exception:
-        shutil.rmtree(temporary, ignore_errors=True)
-        raise
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--base-lab-root", type=Path, required=True)
-    parser.add_argument("--review-pack-root", type=Path, required=True)
-    parser.add_argument("--eomt-root", type=Path, required=True)
-    parser.add_argument("--ddrnet-root", type=Path, required=True)
-    parser.add_argument("--tgs-root", type=Path, required=True)
-    parser.add_argument("--output-root", type=Path, required=True)
-    args = parser.parse_args()
-    print(
-        seal_mixed_route_vegetation_review(
-            base_lab_root=args.base_lab_root,
-            review_pack_root=args.review_pack_root,
-            eomt_root=args.eomt_root,
-            ddrnet_root=args.ddrnet_root,
-            tgs_root=args.tgs_root,
-            output_root=args.output_root,
-        )
-    )
-
-
-if __name__ == "__main__":
-    main()

src/k1link/laboratory/vegetation_benchmark_archive.py

@@ -1,139 +0,0 @@
-"""Seal a benchmark-only vegetation result into its archival LAB namespace."""
-
-from __future__ import annotations
-
-import argparse
-import copy
-import hashlib
-import json
-import shutil
-import tempfile
-from pathlib import Path, PurePosixPath
-from typing import Any, Final
-
-from k1link.laboratory.evidence_registry import LaboratoryEvidenceDefinition
-from k1link.laboratory.evidence_report import verify_laboratory_evidence_result
-from k1link.laboratory.vegetation_shadow_lab import LAB_SCHEMA
-
-_SOURCE_DEFINITION: Final = LaboratoryEvidenceDefinition(
-    work_id="lab-v1-vegetation-shadow",
-    runtime_relative_root=PurePosixPath("lab-v1-vegetation/results"),
-    result_id_prefix="lab-v1-vegetation-shadow",
-    document_name="result.json",
-    result_schema_version=LAB_SCHEMA,
-)
-_ARCHIVE_DEFINITION: Final = LaboratoryEvidenceDefinition(
-    work_id="lab-v1-vegetation-benchmark",
-    runtime_relative_root=PurePosixPath("lab-v1-vegetation-benchmark/results"),
-    result_id_prefix="lab-v1-vegetation-benchmark",
-    document_name="result.json",
-    result_schema_version=LAB_SCHEMA,
-)
-
-
-class VegetationBenchmarkArchiveError(ValueError):
-    """The source result is not a valid benchmark-only immutable result."""
-
-
-def _canonical_json(value: object) -> bytes:
-    return json.dumps(
-        value,
-        ensure_ascii=False,
-        separators=(",", ":"),
-        sort_keys=True,
-    ).encode("utf-8")
-
-
-def _object(value: object, label: str) -> dict[str, Any]:
-    if not isinstance(value, dict):
-        raise VegetationBenchmarkArchiveError(f"{label} is invalid")
-    return value
-
-
-def seal_vegetation_benchmark_archive(
-    *,
-    source_result_root: Path,
-    output_root: Path,
-) -> Path:
-    source = source_result_root.resolve(strict=True)
-    verify_laboratory_evidence_result(_SOURCE_DEFINITION, source)
-    manifest = _object(
-        json.loads((source / "result.json").read_text("utf-8")),
-        "source result",
-    )
-    if manifest.get("route_video") is not None:
-        raise VegetationBenchmarkArchiveError("benchmark archive source contains route video")
-    artifacts = manifest.get("artifacts")
-    if not isinstance(artifacts, list):
-        raise VegetationBenchmarkArchiveError("source artifacts are invalid")
-
-    identity = copy.deepcopy(_object(manifest.get("identity"), "source identity"))
-    identity.update(
-        {
-            "lab_id": "lab-v1-vegetation-benchmark-archive",
-            "archived_from_result_id": source.name,
-        }
-    )
-    identity_sha256 = hashlib.sha256(_canonical_json(identity)).hexdigest()
-    result_id = f"lab-v1-vegetation-benchmark-{identity_sha256}"
-
-    output_root.mkdir(mode=0o700, parents=True, exist_ok=True)
-    destination = output_root / result_id
-    if destination.exists():
-        verify_laboratory_evidence_result(_ARCHIVE_DEFINITION, destination)
-        return destination
-
-    temporary = Path(tempfile.mkdtemp(prefix=".vegetation-benchmark-", dir=output_root))
-    try:
-        for raw in artifacts:
-            descriptor = _object(raw, "artifact descriptor")
-            relative_text = descriptor.get("path")
-            if not isinstance(relative_text, str):
-                raise VegetationBenchmarkArchiveError("artifact path is invalid")
-            relative = PurePosixPath(relative_text)
-            if relative.is_absolute() or any(part in {"", ".", ".."} for part in relative.parts):
-                raise VegetationBenchmarkArchiveError("artifact path is unsafe")
-            source_path = source.joinpath(*relative.parts)
-            destination_path = temporary.joinpath(*relative.parts)
-            destination_path.parent.mkdir(mode=0o700, parents=True, exist_ok=True)
-            shutil.copyfile(source_path, destination_path)
-
-        archived = copy.deepcopy(manifest)
-        archived.update(
-            {
-                "result_id": result_id,
-                "identity": identity,
-                "identity_sha256": identity_sha256,
-                "archived_from_result_id": source.name,
-            }
-        )
-        (temporary / "result.json").write_bytes(_canonical_json(archived) + b"\n")
-        temporary.rename(destination)
-        verify_laboratory_evidence_result(_ARCHIVE_DEFINITION, destination)
-        return destination
-    except Exception:
-        shutil.rmtree(temporary, ignore_errors=True)
-        raise
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--source-result-root", type=Path, required=True)
-    parser.add_argument("--output-root", type=Path, required=True)
-    args = parser.parse_args()
-    print(
-        seal_vegetation_benchmark_archive(
-            source_result_root=args.source_result_root,
-            output_root=args.output_root,
-        )
-    )
-
-
-if __name__ == "__main__":
-    main()
-
-
-__all__ = [
-    "VegetationBenchmarkArchiveError",
-    "seal_vegetation_benchmark_archive",
-]

src/k1link/laboratory/vegetation_policy_review.py

@@ -1,302 +0,0 @@
-"""Seal a coarse material + YOLOX + TGS review from an immutable vegetation LAB."""
-
-from __future__ import annotations
-
-import argparse
-import copy
-import hashlib
-import json
-import shutil
-import tempfile
-from datetime import UTC, datetime
-from pathlib import Path, PurePosixPath
-from typing import Any, Final
-
-from k1link.laboratory.evidence_registry import LaboratoryEvidenceDefinition
-from k1link.laboratory.evidence_report import verify_laboratory_evidence_result
-from k1link.laboratory.m49_tgs_full_shadow import read_m49_tgs_full_shadow
-from k1link.laboratory.vegetation_mission_policy import (
-    load_vegetation_mission_policy,
-    load_vegetation_provider_label_map,
-)
-from k1link.laboratory.vegetation_policy_video import build_policy_mask_archive, policy_taxonomy
-from k1link.laboratory.vegetation_shadow_lab import (
-    LAB_SCHEMA,
-    RESULT_PREFIX,
-    canonical_json,
-    sha256_path,
-)
-
-_DEFINITION: Final = LaboratoryEvidenceDefinition(
-    work_id="lab-v1-vegetation-shadow",
-    runtime_relative_root=PurePosixPath("lab-v1-vegetation/results"),
-    result_id_prefix="lab-v1-vegetation-shadow",
-    document_name="result.json",
-    result_schema_version=LAB_SCHEMA,
-)
-_FRAME_COUNT: Final = 4489
-_MAX_RESULT_BYTES: Final = 1024 * 1024
-
-
-class VegetationPolicyReviewError(ValueError):
-    """The sealed inputs cannot form an honest synchronized policy review."""
-
-
-def _object(value: object, label: str) -> dict[str, Any]:
-    if not isinstance(value, dict) or not all(isinstance(key, str) for key in value):
-        raise VegetationPolicyReviewError(f"{label} must be an object")
-    return value
-
-
-def _read_base(root: Path) -> dict[str, Any]:
-    candidate = root.resolve(strict=True)
-    verify_laboratory_evidence_result(_DEFINITION, candidate)
-    path = candidate / "result.json"
-    if path.stat().st_size > _MAX_RESULT_BYTES:
-        raise VegetationPolicyReviewError("base vegetation LAB document is too large")
-    payload = _object(json.loads(path.read_text("utf-8")), "base vegetation LAB")
-    route = _object(payload.get("route_video"), "base route video")
-    authority = _object(payload.get("authority"), "base authority")
-    if (
-        payload.get("schema_version") != LAB_SCHEMA
-        or payload.get("result_id") != candidate.name
-        or route.get("frame_count") != _FRAME_COUNT
-        or route.get("view_kind", "fine-semantic-prediction")
-        != "fine-semantic-prediction"
-        or route.get("base_m4_result_id") is None
-        or authority.get("commands_enabled") is not False
-        or authority.get("navigation_or_safety_accepted") is not False
-        or authority.get("actuation_accepted") is not False
-        or authority.get("camera_semantics_can_clear_rigid_geometry") is not False
-    ):
-        raise VegetationPolicyReviewError("base vegetation LAB contract changed")
-    return payload
-
-
-def _copy_verified_artifacts(
-    *,
-    source_root: Path,
-    destination_root: Path,
-    artifacts: object,
-) -> list[dict[str, object]]:
-    if not isinstance(artifacts, list):
-        raise VegetationPolicyReviewError("base artifact catalog changed")
-    copied: list[dict[str, object]] = []
-    for raw in artifacts:
-        descriptor = _object(raw, "base artifact")
-        relative_text = descriptor.get("path")
-        expected_sha256 = descriptor.get("sha256")
-        if not isinstance(relative_text, str) or not isinstance(expected_sha256, str):
-            raise VegetationPolicyReviewError("base artifact proof changed")
-        relative = PurePosixPath(relative_text)
-        source = source_root.joinpath(*relative.parts)
-        destination = destination_root.joinpath(*relative.parts)
-        if (
-            relative.is_absolute()
-            or str(relative) != relative_text
-            or any(part in {"", ".", ".."} for part in relative.parts)
-            or source.is_symlink()
-            or not source.is_file()
-            or sha256_path(source) != expected_sha256
-        ):
-            raise VegetationPolicyReviewError("base artifact changed")
-        destination.parent.mkdir(mode=0o700, parents=True, exist_ok=True)
-        shutil.copyfile(source, destination)
-        copied.append(copy.deepcopy(descriptor))
-    return copied
-
-
-def seal_vegetation_policy_review(
-    *,
-    base_lab_root: Path,
-    mission_policy_path: Path,
-    provider_label_map_path: Path,
-    m49_tgs_full_shadow_root: Path,
-    valid_fov_mask_path: Path,
-    output_root: Path,
-    created_at_utc: str | None = None,
-) -> Path:
-    base_root = base_lab_root.resolve(strict=True)
-    base = _read_base(base_root)
-    base_route = _object(base["route_video"], "base route video")
-    repository_root = mission_policy_path.resolve().parents[2]
-    mission_policy = load_vegetation_mission_policy(
-        mission_policy_path.resolve(strict=True),
-        repository_root=repository_root,
-    )
-    provider_map = load_vegetation_provider_label_map(
-        provider_label_map_path.resolve(strict=True),
-        policy=mission_policy,
-    )
-    tgs = read_m49_tgs_full_shadow(m49_tgs_full_shadow_root)
-    tgs_source = _object(tgs.report.get("source"), "full TGS source")
-    tgs_timeline = _object(tgs.report.get("timeline"), "full TGS timeline")
-    if (
-        tgs_source.get("source_id") != "RAVNOVES00"
-        or tgs_source.get("linked_visual_result_id") != base_route.get("base_m4_result_id")
-        or tgs_timeline.get("frame_count") != _FRAME_COUNT
-    ):
-        raise VegetationPolicyReviewError("TGS and vegetation timelines differ")
-
-    raw_archive = _object(base_route.get("mask_archive"), "fine mask archive")
-    if raw_archive.get("path") != "video/ddrnet-semantic-masks.zip":
-        raise VegetationPolicyReviewError("fine mask archive identity changed")
-    raw_archive_path = base_root / "video" / "ddrnet-semantic-masks.zip"
-    fine_taxonomy = _object(base_route.get("taxonomy"), "fine taxonomy")
-    valid_fov_source = valid_fov_mask_path.resolve(strict=True)
-
-    output_root.mkdir(mode=0o700, parents=True, exist_ok=True)
-    temporary = Path(tempfile.mkdtemp(prefix=".lab-v1-policy-", dir=output_root))
-    try:
-        artifacts = _copy_verified_artifacts(
-            source_root=base_root,
-            destination_root=temporary,
-            artifacts=base.get("artifacts"),
-        )
-        policy_archive = temporary / "video" / "coarse-material-policy-masks.zip"
-        valid_fov_destination = temporary / "video" / "valid-fov-mask.png"
-        valid_fov_destination.parent.mkdir(mode=0o700, parents=True, exist_ok=True)
-        shutil.copyfile(valid_fov_source, valid_fov_destination)
-        valid_fov_proof = {
-            "role": "route-camera-valid-fov-mask",
-            "path": "video/valid-fov-mask.png",
-            "byte_length": valid_fov_destination.stat().st_size,
-            "sha256": sha256_path(valid_fov_destination),
-            "media_type": "image/png",
-        }
-        artifacts.append(valid_fov_proof)
-        policy_counts = build_policy_mask_archive(
-            source_archive=raw_archive_path,
-            destination_archive=policy_archive,
-            fine_taxonomy=fine_taxonomy,
-            provider_label_map=provider_map,
-            valid_fov_mask=valid_fov_destination,
-        )
-        policy_archive_proof = {
-            "role": "route-coarse-material-mask-archive",
-            "path": "video/coarse-material-policy-masks.zip",
-            "byte_length": policy_archive.stat().st_size,
-            "sha256": sha256_path(policy_archive),
-            "media_type": "application/zip",
-        }
-        artifacts.append(policy_archive_proof)
-
-        route = copy.deepcopy(base_route)
-        route.update(
-            {
-                "view_kind": "coarse-material-policy-review",
-                "source_mask_archive": copy.deepcopy(raw_archive),
-                "mask_archive": {
-                    "path": policy_archive_proof["path"],
-                    "sha256": policy_archive_proof["sha256"],
-                    "byte_length": policy_archive_proof["byte_length"],
-                },
-                "taxonomy": policy_taxonomy(),
-                "aggregate_prediction_pixels": policy_counts,
-                "linked_tgs_result_id": tgs.result_id,
-                "valid_fov": {
-                    "mask_path": valid_fov_proof["path"],
-                    "mask_sha256": valid_fov_proof["sha256"],
-                    "outside_valid_fov_class_id": 9,
-                },
-                "policy": {
-                    "profile_id": mission_policy["profile_id"],
-                    "profile_sha256": sha256_path(mission_policy_path),
-                    "provider_label_map_id": provider_map["profile_id"],
-                    "provider_label_map_sha256": sha256_path(provider_label_map_path),
-                    "presets": mission_policy["presets"],
-                    "precedence": mission_policy["precedence"],
-                },
-                "fusion": {
-                    "mode": "synchronised-multilayer-review",
-                    "pixel_raster_fusion": False,
-                    "camera_material_layer": "DDRNet fine-64 to coarse material evidence",
-                    "camera_safety_veto_layer": "frozen M4 YOLOX camera proposals",
-                    "spatial_safety_veto_layer": "M4.9 full TGS gravity-local costmap",
-                    "temporal_consensus_owner": "TGS causal rolling 1 s and metric obstacle tracks",
-                    "camera_semantic_temporal_filter": "none",
-                    "camera_valid_fov_filter": "sealed exact KB4 valid-FOV mask",
-                    "reason": "No admitted TGS-to-camera pixel projection exists.",
-                },
-            }
-        )
-        identity = copy.deepcopy(_object(base.get("identity"), "base identity"))
-        identity.update(
-            {
-                "base_result_id": base_root.name,
-                "route_video": route,
-            }
-        )
-        identity_sha256 = hashlib.sha256(canonical_json(identity)).hexdigest()
-        result_id = f"{RESULT_PREFIX}{identity_sha256}"
-        manifest = copy.deepcopy(base)
-        manifest.update(
-            {
-                "result_id": result_id,
-                "identity_sha256": identity_sha256,
-                "created_at_utc": created_at_utc or datetime.now(UTC).isoformat(),
-                "identity": identity,
-                "route_video": route,
-                "method": {
-                    "completeness": "complete",
-                    "execution_class": "ai-inference-plus-deterministic-adapter",
-                    "pipeline_id": "goose-fine64-to-coarse-material-plus-yolox-tgs-review/v1",
-                },
-                "decision": {
-                    **_object(base.get("decision"), "base decision"),
-                    "multilayer_policy_review_ready": True,
-                    "navigation_accepted": False,
-                    "production_accepted": False,
-                },
-                "limitations": [
-                    "GOOSE validation is external-domain qualification, not RAVNOVES ground truth.",
-                    (
-                        "The coarse material playback is derived from per-frame DDRNet "
-                        "predictions and has no RAVNOVES truth."
-                    ),
-                    (
-                        "Vegetation semantics never clears YOLOX, LiDAR, metric obstacle "
-                        "or TGS vetoes."
-                    ),
-                    "Pixels outside the exact KB4 valid FOV are transparent UNOBSERVED evidence.",
-                    (
-                        "TGS remains in gravity-local space; no uncalibrated pixel "
-                        "projection is fabricated."
-                    ),
-                    (
-                        "Temporal consensus comes from causal TGS and metric tracks; "
-                        "the camera material mask is not temporally filtered."
-                    ),
-                ],
-                "artifacts": artifacts,
-            }
-        )
-        (temporary / "result.json").write_bytes(canonical_json(manifest) + b"\n")
-        destination = output_root / result_id
-        if destination.exists():
-            raise VegetationPolicyReviewError("immutable vegetation policy result already exists")
-        temporary.replace(destination)
-        verify_laboratory_evidence_result(_DEFINITION, destination)
-        return destination
-    except Exception:
-        shutil.rmtree(temporary, ignore_errors=True)
-        raise
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--base-lab-root", type=Path, required=True)
-    parser.add_argument("--mission-policy-path", type=Path, required=True)
-    parser.add_argument("--provider-label-map-path", type=Path, required=True)
-    parser.add_argument("--m49-tgs-full-shadow-root", type=Path, required=True)
-    parser.add_argument("--valid-fov-mask-path", type=Path, required=True)
-    parser.add_argument("--output-root", type=Path, required=True)
-    args = parser.parse_args()
-    print(seal_vegetation_policy_review(**vars(args)))
-
-
-if __name__ == "__main__":
-    main()
-
-
-__all__ = ["VegetationPolicyReviewError", "seal_vegetation_policy_review"]

src/k1link/laboratory/vegetation_policy_video.py

@@ -1,223 +0,0 @@
-"""Build a deterministic coarse material-evidence video from fine GOOSE masks."""
-
-from __future__ import annotations
-
-import io
-import zipfile
-from pathlib import Path
-from typing import Any, Final
-
-import numpy as np
-from PIL import Image
-
-from k1link.laboratory.vegetation_mission_policy import map_provider_material
-
-TAXONOMY_SCHEMA: Final = "missioncore.lab-v1-terrain-policy-taxonomy/v1"
-FRAME_COUNT: Final = 4489
-WIDTH: Final = 800
-HEIGHT: Final = 600
-
-POLICY_CLASSES: Final = (
-    {
-        "class_id": 0,
-        "label": "UNOBSERVED / NO MATERIAL CLAIM · NO_GO",
-        "color_rgb": [147, 151, 159],
-        "disposition": "ambiguous",
-        "material_class": None,
-        "evidence_state": "UNOBSERVED",
-    },
-    {
-        "class_id": 1,
-        "label": "SAFETY DETECTOR VETO · NO_GO",
-        "color_rgb": [255, 104, 112],
-        "disposition": "labeled",
-        "material_class": None,
-        "evidence_state": "RIGID_OR_UNKNOWN_OBSTACLE",
-    },
-    {
-        "class_id": 2,
-        "label": "WOODY SHRUB / TREE · NO_GO",
-        "color_rgb": [232, 56, 126],
-        "disposition": "labeled",
-        "material_class": "woody_or_tree",
-        "evidence_state": "VEGETATION_WITH_RIGID_GEOMETRY",
-    },
-    {
-        "class_id": 3,
-        "label": "CULTIVATED VEGETATION · POLICY NO_GO",
-        "color_rgb": [183, 112, 255],
-        "disposition": "labeled",
-        "material_class": "cultivated_vegetation",
-        "evidence_state": "VEGETATION_POTENTIALLY_TRAVERSABLE",
-    },
-    {
-        "class_id": 4,
-        "label": "LOW GRASS · MISSION CANDIDATE",
-        "color_rgb": [181, 255, 90],
-        "disposition": "prediction",
-        "material_class": "grass",
-        "evidence_state": "VEGETATION_POTENTIALLY_TRAVERSABLE",
-    },
-    {
-        "class_id": 5,
-        "label": "HIGH / HERBACEOUS · MISSION CANDIDATE",
-        "color_rgb": [113, 211, 111],
-        "disposition": "prediction",
-        "material_class": "herbaceous_vegetation",
-        "evidence_state": "VEGETATION_POTENTIALLY_TRAVERSABLE",
-    },
-    {
-        "class_id": 6,
-        "label": "BARE SOIL · MISSION CANDIDATE",
-        "color_rgb": [255, 197, 92],
-        "disposition": "prediction",
-        "material_class": "bare_soil",
-        "evidence_state": "SUPPORTED_GROUND",
-    },
-    {
-        "class_id": 7,
-        "label": "HARD SURFACE · MISSION CANDIDATE",
-        "color_rgb": [84, 169, 255],
-        "disposition": "prediction",
-        "material_class": "hard_surface",
-        "evidence_state": "SUPPORTED_GROUND",
-    },
-    {
-        "class_id": 8,
-        "label": "VEGETATION UNKNOWN · NO_GO",
-        "color_rgb": [207, 124, 255],
-        "disposition": "labeled",
-        "material_class": "vegetation_unknown",
-        "evidence_state": "VEGETATION_UNKNOWN",
-    },
-    {
-        "class_id": 9,
-        "label": "OUTSIDE VALID FOV · NO SENSOR EVIDENCE",
-        "color_rgb": [0, 0, 0],
-        "disposition": "undefined",
-        "material_class": None,
-        "evidence_state": "UNOBSERVED",
-    },
-)
-
-_MATERIAL_TO_CLASS: Final = {
-    "hard_surface": 7,
-    "bare_soil": 6,
-    "grass": 4,
-    "fern": 5,
-    "herbaceous_vegetation": 5,
-    "cultivated_vegetation": 3,
-    "woody_shrub": 2,
-    "tree_or_trunk": 2,
-    "vegetation_unknown": 8,
-}
-
-
-class VegetationPolicyVideoError(ValueError):
-    """The fine-mask input cannot be transformed without inventing evidence."""
-
-
-def policy_taxonomy() -> dict[str, object]:
-    return {
-        "schema_version": TAXONOMY_SCHEMA,
-        "classes": [dict(row) for row in POLICY_CLASSES],
-    }
-
-
-def fine_to_policy_lut(
-    fine_taxonomy: dict[str, object],
-    provider_label_map: dict[str, Any],
-) -> np.ndarray:
-    classes = fine_taxonomy.get("classes")
-    if not isinstance(classes, list) or len(classes) != 64:
-        raise VegetationPolicyVideoError("fine taxonomy must contain 64 classes")
-    lut = np.zeros(256, dtype=np.uint8)
-    for expected_id, raw in enumerate(classes):
-        if not isinstance(raw, dict) or raw.get("class_id") != expected_id:
-            raise VegetationPolicyVideoError("fine taxonomy ordering changed")
-        label = raw.get("label")
-        if not isinstance(label, str) or not label:
-            raise VegetationPolicyVideoError("fine taxonomy label is invalid")
-        if expected_id == 0:
-            continue
-        material = map_provider_material(
-            provider_label_map,
-            provider_id="goose-fine-64",
-            provider_label=label,
-        )
-        lut[expected_id] = _MATERIAL_TO_CLASS.get(material, 0)
-    return lut
-
-
-def _zip_info(name: str) -> zipfile.ZipInfo:
-    info = zipfile.ZipInfo(name, date_time=(1980, 1, 1, 0, 0, 0))
-    info.compress_type = zipfile.ZIP_STORED
-    info.create_system = 3
-    info.external_attr = 0o600 << 16
-    return info
-
-
-def build_policy_mask_archive(
-    *,
-    source_archive: Path,
-    destination_archive: Path,
-    fine_taxonomy: dict[str, object],
-    provider_label_map: dict[str, Any],
-    valid_fov_mask: Path,
-) -> list[int]:
-    """Map every fine mask to coarse evidence; safety vetoes remain separate layers."""
-
-    lut = fine_to_policy_lut(fine_taxonomy, provider_label_map)
-    try:
-        with Image.open(valid_fov_mask) as image:
-            valid_fov = np.asarray(image.convert("L"), dtype=np.uint8) > 0
-    except OSError as exc:
-        raise VegetationPolicyVideoError("valid-FOV mask is unreadable") from exc
-    if valid_fov.shape != (HEIGHT, WIDTH) or not np.any(valid_fov) or np.all(valid_fov):
-        raise VegetationPolicyVideoError("valid-FOV mask geometry is invalid")
-    counts = np.zeros(len(POLICY_CLASSES), dtype=np.int64)
-    destination_archive.parent.mkdir(mode=0o700, parents=True, exist_ok=True)
-    try:
-        with zipfile.ZipFile(source_archive) as source, zipfile.ZipFile(
-            destination_archive,
-            "x",
-        ) as destination:
-            for sequence in range(FRAME_COUNT):
-                member = f"masks/frame-{sequence + 1:06d}.png"
-                with source.open(member) as stream, Image.open(stream) as image:
-                    fine = np.asarray(image.convert("L"), dtype=np.uint8)
-                if fine.shape != (HEIGHT, WIDTH):
-                    raise VegetationPolicyVideoError(
-                        f"fine mask {member} has shape {fine.shape}, expected {(HEIGHT, WIDTH)}"
-                    )
-                coarse = lut[fine]
-                coarse[~valid_fov] = 9
-                counts += np.bincount(
-                    coarse.reshape(-1),
-                    minlength=len(POLICY_CLASSES),
-                )
-                buffer = io.BytesIO()
-                Image.fromarray(coarse, mode="L").save(
-                    buffer,
-                    format="PNG",
-                    compress_level=1,
-                    optimize=False,
-                )
-                destination.writestr(_zip_info(member), buffer.getvalue())
-    except (KeyError, OSError, ValueError, zipfile.BadZipFile) as exc:
-        destination_archive.unlink(missing_ok=True)
-        raise VegetationPolicyVideoError("fine mask archive is invalid") from exc
-    return [int(value) for value in counts]
-
-
-__all__ = [
-    "FRAME_COUNT",
-    "HEIGHT",
-    "POLICY_CLASSES",
-    "TAXONOMY_SCHEMA",
-    "VegetationPolicyVideoError",
-    "WIDTH",
-    "build_policy_mask_archive",
-    "fine_to_policy_lut",
-    "policy_taxonomy",
-]

src/k1link/laboratory/vegetation_shadow_lab.py

@@ -14,15 +14,6 @@ from pathlib import Path, PurePosixPath
 from typing import Any, Final
 
 from k1link.laboratory.m47_reference_graph import read_m47_reference_graph_lab
-from k1link.laboratory.m49_tgs_full_shadow import read_m49_tgs_full_shadow
-from k1link.laboratory.vegetation_mission_policy import (
-    load_vegetation_mission_policy,
-    load_vegetation_provider_label_map,
-)
-from k1link.laboratory.vegetation_policy_video import (
-    build_policy_mask_archive,
-    policy_taxonomy,
-)
 
 LAB_SCHEMA: Final = "missioncore.lab-v1-vegetation-shadow/v1"
 WORKER_SCHEMA: Final = "missioncore.lab-v1-goose-vegetation-run/v1"
@@ -324,10 +315,6 @@ def seal_vegetation_shadow_lab(
     output_root: Path,
     ddrnet_ravnoves_video_root: Path | None = None,
     m47_reference_graph_lab_root: Path | None = None,
-    mission_policy_path: Path | None = None,
-    provider_label_map_path: Path | None = None,
-    m49_tgs_full_shadow_root: Path | None = None,
-    valid_fov_mask_path: Path | None = None,
 ) -> Path:
     roots = {
         ("ddrnet", "goose"): ddrnet_goose_root.resolve(),
@@ -346,18 +333,6 @@ def seal_vegetation_shadow_lab(
     selected = _selected_candidate(results)
     if (ddrnet_ravnoves_video_root is None) != (m47_reference_graph_lab_root is None):
         raise VegetationShadowLabError("full-video Worker and M4.7 roots must be paired")
-    policy_inputs = (
-        mission_policy_path,
-        provider_label_map_path,
-        m49_tgs_full_shadow_root,
-        valid_fov_mask_path,
-    )
-    if any(value is not None for value in policy_inputs) and not all(
-        value is not None for value in policy_inputs
-    ):
-        raise VegetationShadowLabError("policy, provider map and full TGS roots must be paired")
-    if all(value is not None for value in policy_inputs) and ddrnet_ravnoves_video_root is None:
-        raise VegetationShadowLabError("policy review requires the full-video DDRNet result")
     route_video: dict[str, object] | None = None
     route_video_archive: Path | None = None
     video_result: dict[str, Any] | None = None
@@ -380,36 +355,6 @@ def seal_vegetation_shadow_lab(
             raise VegetationShadowLabError("M4.7 video binding differs from DDRNet source")
         route_video["m47_reference_graph_result_id"] = m47.result_id
 
-    mission_policy: dict[str, Any] | None = None
-    provider_label_map: dict[str, Any] | None = None
-    linked_tgs_result_id: str | None = None
-    if (
-        mission_policy_path is not None
-        and provider_label_map_path is not None
-        and m49_tgs_full_shadow_root is not None
-        and valid_fov_mask_path is not None
-        and route_video is not None
-    ):
-        repository_root = mission_policy_path.resolve().parents[2]
-        mission_policy = load_vegetation_mission_policy(
-            mission_policy_path.resolve(),
-            repository_root=repository_root,
-        )
-        provider_label_map = load_vegetation_provider_label_map(
-            provider_label_map_path.resolve(),
-            policy=mission_policy,
-        )
-        tgs = read_m49_tgs_full_shadow(m49_tgs_full_shadow_root)
-        tgs_source = _object(tgs.report.get("source"), "M4.9 full TGS source")
-        tgs_timeline = _object(tgs.report.get("timeline"), "M4.9 full TGS timeline")
-        if (
-            tgs_source.get("source_id") != "RAVNOVES00"
-            or tgs_source.get("linked_visual_result_id") != route_video["base_m4_result_id"]
-            or tgs_timeline.get("frame_count") != _VIDEO_FRAME_COUNT
-        ):
-            raise VegetationShadowLabError("full TGS timeline differs from vegetation video")
-        linked_tgs_result_id = tgs.result_id
-
     output_root.mkdir(mode=0o700, parents=True, exist_ok=True)
     temporary = Path(tempfile.mkdtemp(prefix=".lab-v1-vegetation-", dir=output_root))
     artifacts: list[dict[str, object]] = []
@@ -526,96 +471,14 @@ def seal_vegetation_shadow_lab(
                 temporary,
                 "video/ddrnet-semantic-masks.zip",
                 artifacts,
-                role=(
-                    "route-fine-semantic-source-archive"
-                    if mission_policy is not None
-                    else "route-semantic-mask-archive"
-                ),
+                role="route-semantic-mask-archive",
                 media_type="application/zip",
             )
-            raw_archive_proof = {
+            route_video["mask_archive"] = {
                 "path": archive_descriptor["path"],
                 "sha256": archive_descriptor["sha256"],
                 "byte_length": archive_descriptor["byte_length"],
             }
-            route_video["mask_archive"] = raw_archive_proof
-            route_video["view_kind"] = "fine-semantic-prediction"
-            if (
-                mission_policy is not None
-                and provider_label_map is not None
-                and linked_tgs_result_id is not None
-                and mission_policy_path is not None
-                and provider_label_map_path is not None
-                and valid_fov_mask_path is not None
-            ):
-                policy_archive = temporary / "video" / "coarse-material-policy-masks.zip"
-                valid_fov_destination = temporary / "video" / "valid-fov-mask.png"
-                shutil.copyfile(valid_fov_mask_path.resolve(strict=True), valid_fov_destination)
-                valid_fov_descriptor = {
-                    "role": "route-camera-valid-fov-mask",
-                    "path": "video/valid-fov-mask.png",
-                    "byte_length": valid_fov_destination.stat().st_size,
-                    "sha256": sha256_path(valid_fov_destination),
-                    "media_type": "image/png",
-                }
-                artifacts.append(valid_fov_descriptor)
-                policy_counts = build_policy_mask_archive(
-                    source_archive=route_video_archive,
-                    destination_archive=policy_archive,
-                    fine_taxonomy=_object(route_video["taxonomy"], "fine video taxonomy"),
-                    provider_label_map=provider_label_map,
-                    valid_fov_mask=valid_fov_destination,
-                )
-                policy_descriptor = {
-                    "role": "route-coarse-material-mask-archive",
-                    "path": "video/coarse-material-policy-masks.zip",
-                    "byte_length": policy_archive.stat().st_size,
-                    "sha256": sha256_path(policy_archive),
-                    "media_type": "application/zip",
-                }
-                artifacts.append(policy_descriptor)
-                route_video.update(
-                    {
-                        "view_kind": "coarse-material-policy-review",
-                        "source_mask_archive": raw_archive_proof,
-                        "mask_archive": {
-                            "path": policy_descriptor["path"],
-                            "sha256": policy_descriptor["sha256"],
-                            "byte_length": policy_descriptor["byte_length"],
-                        },
-                        "taxonomy": policy_taxonomy(),
-                        "aggregate_prediction_pixels": policy_counts,
-                        "linked_tgs_result_id": linked_tgs_result_id,
-                        "valid_fov": {
-                            "mask_path": valid_fov_descriptor["path"],
-                            "mask_sha256": valid_fov_descriptor["sha256"],
-                            "outside_valid_fov_class_id": 9,
-                        },
-                        "policy": {
-                            "profile_id": mission_policy["profile_id"],
-                            "profile_sha256": sha256_path(mission_policy_path),
-                            "provider_label_map_id": provider_label_map["profile_id"],
-                            "provider_label_map_sha256": sha256_path(
-                                provider_label_map_path
-                            ),
-                            "presets": mission_policy["presets"],
-                            "precedence": mission_policy["precedence"],
-                        },
-                        "fusion": {
-                            "mode": "synchronised-multilayer-review",
-                            "pixel_raster_fusion": False,
-                            "camera_material_layer": "DDRNet fine-64 to coarse material evidence",
-                            "camera_safety_veto_layer": "frozen M4 YOLOX camera proposals",
-                            "spatial_safety_veto_layer": "M4.9 full TGS gravity-local costmap",
-                            "temporal_consensus_owner": (
-                                "TGS causal rolling 1 s and metric obstacle tracks"
-                            ),
-                            "camera_semantic_temporal_filter": "none",
-                            "camera_valid_fov_filter": "sealed exact KB4 valid-FOV mask",
-                            "reason": "No admitted TGS-to-camera pixel projection exists.",
-                        },
-                    }
-                )
 
         candidate_metrics: dict[str, object] = {}
         for candidate in _CANDIDATES:
@@ -673,11 +536,7 @@ def seal_vegetation_shadow_lab(
             "method": {
                 "completeness": "complete",
                 "execution_class": "ai-inference",
-                "pipeline_id": (
-                    "goose-fine64-to-coarse-material-plus-yolox-tgs-review/v1"
-                    if mission_policy is not None
-                    else "goose-fine64-ready-weights-to-ravnoves-policy-shadow/v1"
-                ),
+                "pipeline_id": "goose-fine64-ready-weights-to-ravnoves-policy-shadow/v1",
             },
             "metrics": {"candidates": candidate_metrics},
             "decision": {
@@ -685,41 +544,14 @@ def seal_vegetation_shadow_lab(
                 "visual_shadow_ready": True,
                 "full_video_shadow_ready": route_video is not None,
                 "mission_policy_ready_for_configuration": True,
-                "multilayer_policy_review_ready": mission_policy is not None,
                 "navigation_accepted": False,
                 "production_accepted": False,
             },
             "limitations": [
                 "GOOSE validation is external-domain qualification, not RAVNOVES ground truth.",
-                (
-                    "The coarse material playback is derived from per-frame DDRNet predictions "
-                    "and has no RAVNOVES truth."
-                    if mission_policy is not None
-                    else (
-                        "The full RAVNOVES DDRNet playback is prediction-only and has "
-                        "no independent labels."
-                    )
-                ),
+                "The full RAVNOVES DDRNet playback is prediction-only and has no independent labels.",
                 "Vegetation semantics never clears rigid LiDAR/TGS occupancy.",
-                (
-                    "Pixels outside the exact KB4 valid FOV are transparent UNOBSERVED evidence."
-                    if mission_policy is not None
-                    else "Undefined pixels outside the 600x600 center crop remain fail-closed."
-                ),
-                *(
-                    [
-                        (
-                            "TGS remains in gravity-local space; no uncalibrated pixel "
-                            "projection is fabricated."
-                        ),
-                        (
-                            "Temporal consensus comes from causal TGS and metric tracks; "
-                            "the camera material mask is not temporally filtered."
-                        ),
-                    ]
-                    if mission_policy is not None
-                    else []
-                ),
+                "Undefined pixels outside the 600x600 center crop remain fail-closed.",
             ],
             "authority": authority,
             "catalogs": catalogs,
@@ -745,10 +577,6 @@ def _parse_args() -> argparse.Namespace:
     parser.add_argument("--output-root", type=Path, required=True)
     parser.add_argument("--ddrnet-ravnoves-video-root", type=Path)
     parser.add_argument("--m47-reference-graph-lab-root", type=Path)
-    parser.add_argument("--mission-policy-path", type=Path)
-    parser.add_argument("--provider-label-map-path", type=Path)
-    parser.add_argument("--m49-tgs-full-shadow-root", type=Path)
-    parser.add_argument("--valid-fov-mask-path", type=Path)
     return parser.parse_args()
 
 
@@ -762,10 +590,6 @@ def main() -> None:
         output_root=args.output_root,
         ddrnet_ravnoves_video_root=args.ddrnet_ravnoves_video_root,
         m47_reference_graph_lab_root=args.m47_reference_graph_lab_root,
-        mission_policy_path=args.mission_policy_path,
-        provider_label_map_path=args.provider_label_map_path,
-        m49_tgs_full_shadow_root=args.m49_tgs_full_shadow_root,
-        valid_fov_mask_path=args.valid_fov_mask_path,
     )
     print(destination)
 

src/k1link/web/app.py

@@ -138,6 +138,7 @@ from k1link.web.m49_physical_safety_playback_api import (
 )
 from k1link.web.m49_tgs_fail_closed_api import build_m49_tgs_fail_closed_router
 from k1link.web.m49_tgs_full_shadow_api import build_m49_tgs_full_shadow_router
+from k1link.web.vegetation_shadow_lab_api import build_vegetation_shadow_lab_router
 from k1link.web.map_api import (
     MapGatewayConfiguration,
     MapGatewayProxy,
@@ -164,10 +165,6 @@ from k1link.web.session_api import build_session_router
 from k1link.web.simulation_projects_api import build_simulation_projects_router
 from k1link.web.simulation_world_provider_api import build_simulation_world_provider_router
 from k1link.web.system_telemetry_api import build_system_telemetry_router
-from k1link.web.vegetation_shadow_lab_api import (
-    build_vegetation_benchmark_lab_router,
-    build_vegetation_shadow_lab_router,
-)
 from k1link.web.viewer_diagnostics_api import build_viewer_diagnostics_router
 
 REPOSITORY_ROOT = Path(__file__).resolve().parents[3]
@@ -1034,17 +1031,6 @@ app.include_router(
         ),
     )
 )
-app.include_router(
-    build_vegetation_benchmark_lab_router(
-        root_provider=lambda: (
-            REPOSITORY_ROOT
-            / ".runtime"
-            / "compute-experiments"
-            / "lab-v1-vegetation-benchmark"
-            / "results"
-        ),
-    )
-)
 app.include_router(
     build_m49_physical_safety_playback_router(
         root_provider=lambda: (

src/k1link/web/vegetation_shadow_lab_api.py

@@ -5,6 +5,7 @@ from __future__ import annotations
 import copy
 import hashlib
 import json
+import re
 import zipfile
 from collections.abc import Callable
 from functools import lru_cache
@@ -19,9 +20,10 @@ from k1link.laboratory.evidence_report import (
     LaboratoryEvidenceReportError,
     verify_laboratory_evidence_result,
 )
-from k1link.laboratory.vegetation_shadow_lab import LAB_SCHEMA
+from k1link.laboratory.vegetation_shadow_lab import LAB_SCHEMA, RESULT_PREFIX
 
 RootProvider = Callable[[], Path | None]
+RESULT_ID: Final = re.compile(rf"^{re.escape(RESULT_PREFIX)}[a-f0-9]{{64}}$")
 _MAX_DOCUMENT_BYTES: Final = 1024 * 1024
 _DEFINITION: Final = LaboratoryEvidenceDefinition(
     work_id="lab-v1-vegetation-shadow",
@@ -30,55 +32,25 @@ _DEFINITION: Final = LaboratoryEvidenceDefinition(
     document_name="result.json",
     result_schema_version=LAB_SCHEMA,
 )
-_BENCHMARK_DEFINITION: Final = LaboratoryEvidenceDefinition(
-    work_id="lab-v1-vegetation-benchmark",
-    runtime_relative_root=PurePosixPath("lab-v1-vegetation-benchmark/results"),
-    result_id_prefix="lab-v1-vegetation-benchmark",
-    document_name="result.json",
-    result_schema_version=LAB_SCHEMA,
-)
 
 
 def build_vegetation_shadow_lab_router(
     *, root_provider: RootProvider = lambda: None,
-) -> APIRouter:
-    return _build_vegetation_lab_router(
-        prefix="/api/v1/laboratory/vegetation-shadow",
-        definition=_DEFINITION,
-        root_provider=root_provider,
-    )
-
-
-def build_vegetation_benchmark_lab_router(
-    *, root_provider: RootProvider = lambda: None,
-) -> APIRouter:
-    return _build_vegetation_lab_router(
-        prefix="/api/v1/laboratory/vegetation-benchmark",
-        definition=_BENCHMARK_DEFINITION,
-        root_provider=root_provider,
-    )
-
-
-def _build_vegetation_lab_router(
-    *,
-    prefix: str,
-    definition: LaboratoryEvidenceDefinition,
-    root_provider: RootProvider,
 ) -> APIRouter:
     router = APIRouter(
-        prefix=prefix,
+        prefix="/api/v1/laboratory/vegetation-shadow",
         tags=["laboratory"],
     )
 
     @router.get("/{result_id}")
     def get_result(result_id: str) -> dict[str, object]:
-        candidate = _resolve_candidate(root_provider, definition, result_id)
-        return {**copy.deepcopy(_read_verified(candidate, definition)), "access": "read-only"}
+        candidate = _resolve_candidate(root_provider, result_id)
+        return {**copy.deepcopy(_read_verified(candidate)), "access": "read-only"}
 
     @router.get("/{result_id}/assets/{asset_path:path}")
     def get_asset(result_id: str, asset_path: str) -> FileResponse:
-        candidate = _resolve_candidate(root_provider, definition, result_id)
-        manifest = _read_verified(candidate, definition)
+        candidate = _resolve_candidate(root_provider, result_id)
+        manifest = _read_verified(candidate)
         artifacts = manifest.get("artifacts")
         if not isinstance(artifacts, list):
             raise HTTPException(status_code=404, detail="Vegetation LAB asset not found")
@@ -118,36 +90,15 @@ def _build_vegetation_lab_router(
 
     @router.get("/{result_id}/masks/{sequence}")
     def get_video_mask(result_id: str, sequence: int) -> Response:
-        candidate = _resolve_candidate(root_provider, definition, result_id)
-        manifest = _read_verified(candidate, definition)
+        candidate = _resolve_candidate(root_provider, result_id)
+        manifest = _read_verified(candidate)
         route_video = manifest.get("route_video")
-        if (
-            not isinstance(route_video, dict)
-            or route_video.get("frame_count") != 4489
-            or not 0 <= sequence < 4489
-        ):
+        if not isinstance(route_video, dict) or not 0 <= sequence < 4489:
             raise HTTPException(status_code=404, detail="Vegetation video mask not found")
         archive = route_video.get("mask_archive")
-        archive_relative = archive.get("path") if isinstance(archive, dict) else None
-        if not isinstance(archive_relative, str):
+        if not isinstance(archive, dict) or archive.get("path") != "video/ddrnet-semantic-masks.zip":
             raise HTTPException(status_code=404, detail="Vegetation video mask not found")
-        relative = PurePosixPath(archive_relative)
-        if (
-            relative.is_absolute()
-            or str(relative) != archive_relative
-            or any(part in {"", ".", ".."} for part in relative.parts)
-            or relative.suffix != ".zip"
-        ):
-            raise HTTPException(status_code=404, detail="Vegetation video mask not found")
-        artifacts = manifest.get("artifacts")
-        if not isinstance(artifacts, list) or not any(
-            isinstance(item, dict)
-            and item.get("path") == archive_relative
-            and item.get("media_type") == "application/zip"
-            for item in artifacts
-        ):
-            raise HTTPException(status_code=404, detail="Vegetation video mask not found")
-        archive_path = candidate.joinpath(*relative.parts)
+        archive_path = candidate / "video" / "ddrnet-semantic-masks.zip"
         member = f"masks/frame-{sequence + 1:06d}.png"
         try:
             before = archive_path.stat()
@@ -179,125 +130,9 @@ def _build_vegetation_lab_router(
             },
         )
 
-    @router.get("/{result_id}/route-masks/{layer}/{sequence}")
-    def get_full_route_mask(result_id: str, layer: str, sequence: int) -> Response:
-        candidate = _resolve_candidate(root_provider, definition, result_id)
-        manifest = _read_verified(candidate, definition)
-        route = manifest.get("route_full_review")
-        layers = route.get("layers") if isinstance(route, dict) else None
-        frame_count = route.get("frame_count") if isinstance(route, dict) else None
-        selected = layers.get(layer) if isinstance(layers, dict) else None
-        archive = selected.get("mask_archive") if isinstance(selected, dict) else None
-        archive_relative = archive.get("path") if isinstance(archive, dict) else None
-        if (
-            layer not in {"city", "vegetation"}
-            or not isinstance(frame_count, int)
-            or not 0 <= sequence < frame_count
-            or not isinstance(archive_relative, str)
-        ):
-            raise HTTPException(status_code=404, detail="Full-route semantic mask not found")
-        relative = PurePosixPath(archive_relative)
-        artifacts = manifest.get("artifacts")
-        if (
-            relative.is_absolute()
-            or str(relative) != archive_relative
-            or any(part in {"", ".", ".."} for part in relative.parts)
-            or relative.suffix != ".zip"
-            or not isinstance(artifacts, list)
-            or not any(
-                isinstance(item, dict)
-                and item.get("path") == archive_relative
-                and item.get("media_type") == "application/zip"
-                for item in artifacts
-            )
-        ):
-            raise HTTPException(status_code=404, detail="Full-route semantic mask not found")
-        return _zip_mask_response(candidate.joinpath(*relative.parts), sequence)
-
-    @router.get("/{result_id}/route-timeline")
-    def get_full_route_timeline(result_id: str) -> FileResponse:
-        candidate = _resolve_candidate(root_provider, definition, result_id)
-        manifest = _read_verified(candidate, definition)
-        route = manifest.get("route_full_review")
-        timeline = route.get("timeline") if isinstance(route, dict) else None
-        relative_text = timeline.get("path") if isinstance(timeline, dict) else None
-        frame_count = timeline.get("frame_count") if isinstance(timeline, dict) else None
-        byte_length = timeline.get("byte_length") if isinstance(timeline, dict) else None
-        sha256 = timeline.get("sha256") if isinstance(timeline, dict) else None
-        if (
-            not isinstance(relative_text, str)
-            or frame_count != route.get("frame_count")
-            or byte_length != frame_count * 8
-            or not isinstance(sha256, str)
-            or len(sha256) != 64
-        ):
-            raise HTTPException(status_code=404, detail="Full-route timeline not found")
-        relative = PurePosixPath(relative_text)
-        artifacts = manifest.get("artifacts")
-        if (
-            relative.is_absolute()
-            or str(relative) != relative_text
-            or any(part in {"", ".", ".."} for part in relative.parts)
-            or not isinstance(artifacts, list)
-            or not any(
-                isinstance(item, dict)
-                and item.get("path") == relative_text
-                and item.get("byte_length") == byte_length
-                and item.get("sha256") == sha256
-                and item.get("media_type") == "application/octet-stream"
-                for item in artifacts
-            )
-        ):
-            raise HTTPException(status_code=404, detail="Full-route timeline not found")
-        path = candidate.joinpath(*relative.parts)
-        if not path.is_file() or path.is_symlink() or path.stat().st_size != byte_length:
-            raise HTTPException(status_code=404, detail="Full-route timeline not found")
-        return FileResponse(
-            path,
-            media_type="application/octet-stream",
-            headers={
-                "Cache-Control": "private, max-age=31536000, immutable",
-                "ETag": f'"{sha256}"',
-                "X-Content-Type-Options": "nosniff",
-            },
-        )
-
     return router
 
 
-def _zip_mask_response(archive_path: Path, sequence: int) -> Response:
-    member = f"masks/frame-{sequence + 1:06d}.png"
-    try:
-        before = archive_path.stat()
-        with zipfile.ZipFile(archive_path) as frozen:
-            info = frozen.getinfo(member)
-            if info.is_dir() or info.file_size < 8 or info.file_size > 1024 * 1024:
-                raise ValueError("Semantic mask member is invalid")
-            payload = frozen.read(info)
-        after = archive_path.stat()
-        if (
-            before.st_size != after.st_size
-            or before.st_mtime_ns != after.st_mtime_ns
-            or len(payload) != info.file_size
-        ):
-            raise ValueError("Semantic mask archive changed during read")
-    except (KeyError, OSError, ValueError, zipfile.BadZipFile):
-        raise HTTPException(
-            status_code=503,
-            detail="Semantic mask failed verification",
-        ) from None
-    digest = hashlib.sha256(payload).hexdigest()
-    return Response(
-        content=payload,
-        media_type="image/png",
-        headers={
-            "Cache-Control": "private, max-age=31536000, immutable",
-            "ETag": f'"{digest}"',
-            "X-Content-Type-Options": "nosniff",
-        },
-    )
-
-
 def _configured_root(provider: RootProvider) -> Path | None:
     candidate = provider()
     if candidate is None:
@@ -312,13 +147,9 @@ def _configured_root(provider: RootProvider) -> Path | None:
     return root if root.is_dir() else None
 
 
-def _resolve_candidate(
-    provider: RootProvider,
-    definition: LaboratoryEvidenceDefinition,
-    result_id: str,
-) -> Path:
+def _resolve_candidate(provider: RootProvider, result_id: str) -> Path:
     root = _configured_root(provider)
-    if root is None or definition.result_id_pattern.fullmatch(result_id) is None:
+    if root is None or RESULT_ID.fullmatch(result_id) is None:
         raise HTTPException(status_code=404, detail="Vegetation LAB result not found")
     candidate = root / result_id
     if candidate.is_symlink():
@@ -332,10 +163,7 @@ def _resolve_candidate(
     return resolved
 
 
-def _read_verified(
-    candidate: Path,
-    definition: LaboratoryEvidenceDefinition,
-) -> dict[str, Any]:
+def _read_verified(candidate: Path) -> dict[str, Any]:
     try:
         rows: list[tuple[str, int, int, int, int]] = []
         for path in sorted(candidate.rglob("*"), key=lambda item: item.as_posix()):
@@ -353,39 +181,19 @@ def _read_verified(
             status_code=503,
             detail="Vegetation LAB evidence failed verification",
         ) from None
-    return _read_verified_cached(
-        str(candidate),
-        signature,
-        definition.work_id,
-        str(definition.runtime_relative_root),
-        definition.result_id_prefix,
-        definition.document_name,
-        definition.result_schema_version,
-    )
+    return _read_verified_cached(str(candidate), signature)
 
 
 @lru_cache(maxsize=16)
 def _read_verified_cached(
     candidate_text: str,
     signature: tuple[tuple[str, int, int, int, int], ...],
-    work_id: str,
-    runtime_relative_root: str,
-    result_id_prefix: str,
-    document_name: str,
-    result_schema_version: str,
 ) -> dict[str, Any]:
     del signature
     candidate = Path(candidate_text)
-    definition = LaboratoryEvidenceDefinition(
-        work_id=work_id,
-        runtime_relative_root=PurePosixPath(runtime_relative_root),
-        result_id_prefix=result_id_prefix,
-        document_name=document_name,
-        result_schema_version=result_schema_version,
-    )
     try:
-        verify_laboratory_evidence_result(definition, candidate)
-        path = candidate / definition.document_name
+        verify_laboratory_evidence_result(_DEFINITION, candidate)
+        path = candidate / "result.json"
         if path.stat().st_size > _MAX_DOCUMENT_BYTES:
             raise LaboratoryEvidenceReportError("Vegetation LAB document is too large")
         payload = json.loads(path.read_text("utf-8"))
@@ -399,7 +207,4 @@ def _read_verified_cached(
     return payload
 
 
-__all__ = [
-    "build_vegetation_benchmark_lab_router",
-    "build_vegetation_shadow_lab_router",
-]
+__all__ = ["build_vegetation_shadow_lab_router"]

tests/test_e4_full_session_segmentation.py

@@ -104,20 +104,3 @@ def test_e4_class_fractions_use_only_valid_fov_pixels() -> None:
 
     assert {item["id"]: item["pixels"] for item in classes} == {1: 1, 4: 2, 7: 1}
     assert sum(float(item["fraction_of_valid_fov"]) for item in classes) == 1.0
-
-
-def test_e4_orchestrator_seals_a_single_decoder_gap_without_frame_shift() -> None:
-    path = (
-        Path(__file__).parents[1]
-        / "experiments"
-        / "perception"
-        / "worker"
-        / "Invoke-E4FullSessionSegmentation.ps1"
-    )
-    source = path.read_text(encoding="utf-8")
-    assert "-c:v h264_cuvid" in source
-    assert "-frame_pts 1" in source
-    assert '$decodedPath = Join-Path $decodedFramesRoot ("frame-{0}.png" -f $pts)' in source
-    assert "$repairs.Count -ge 1" in source
-    assert 'method = "duplicate-previous-decoded-frame"' in source
-    assert 'schema_version = "missioncore.recorded-video-decode-repair/v1"' in source

tests/test_lab_v1_goose_vegetation_benchmark.py

@@ -25,12 +25,6 @@ POWERSHELL_PATH = (
     / "worker"
     / "Invoke-LabV1VegetationGooseBenchmark.ps1"
 )
-RAV004_SOURCE_PATH = (
-    REPOSITORY_ROOT
-    / "config"
-    / "perception"
-    / "lab-v1-ravnoves004tree-full-video-source-v1.json"
-)
 
 
 def test_benchmark_contract_is_bounded_and_fail_closed() -> None:
@@ -99,21 +93,3 @@ def test_worker_wrapper_is_isolated_from_canonical_triton() -> None:
     assert '"--cap-drop", "ALL"' in source
     assert '"--security-opt", "no-new-privileges"' in source
     assert "if ($canonicalAfter -ne $canonicalBefore)" in source
-
-
-def test_rav004_full_video_profile_and_decoder_gap_are_explicit() -> None:
-    profile = json.loads(RAV004_SOURCE_PATH.read_text(encoding="utf-8"))
-    source_profile = profile["source"]
-    assert profile["schema_version"] == "missioncore.lab-v1-ravnoves-source/v1"
-    assert source_profile["source_job_id"] == (
-        "recorded-camera-eb2783c5480d56bda07c8af0"
-    )
-    assert source_profile["expected_frame_count"] == 6830
-    assert source_profile["base_m4_result_id"] is None
-    source = POWERSHELL_PATH.read_text(encoding="utf-8")
-    assert "-c:v h264_cuvid" in source
-    assert 'schema_version = "missioncore.recorded-video-decode-repair/v1"' in source
-    assert 'method = "duplicate-previous-decoded-frame"' in source
-    assert "$repairs.Count -ge 1" in source
-    assert '& docker @arguments 2>&1 | ForEach-Object { Write-Output $_ }' in source
-    assert 'if ($dockerExitCode -ne 0)' in source

tests/test_lab_v1_vegetation_integrated_shadow.py

@@ -1,276 +0,0 @@
-from __future__ import annotations
-
-import importlib.util
-import json
-import tarfile
-from pathlib import Path
-
-REPOSITORY_ROOT = Path(__file__).resolve().parents[1]
-EVIDENCE_PATH = (
-    REPOSITORY_ROOT
-    / "experiments/perception/worker/m49_t3_travel/"
-    "build_vegetation_integrated_graph_evidence.py"
-)
-ARTIFACT_PATH = (
-    REPOSITORY_ROOT / "scripts/build_lab_v1_vegetation_integrated_worker_artifact.py"
-)
-RUNNER_PATH = (
-    REPOSITORY_ROOT
-    / "experiments/perception/worker/lab_v1_vegetation_goose/"
-    "run_vegetation_integrated_load.py"
-)
-POWERSHELL_PATH = (
-    REPOSITORY_ROOT
-    / "experiments/perception/worker/Invoke-M49TgsIntegratedGraphShadow.ps1"
-)
-
-
-def load_module(name: str, path: Path):
-    spec = importlib.util.spec_from_file_location(name, path)
-    assert spec is not None and spec.loader is not None
-    module = importlib.util.module_from_spec(spec)
-    spec.loader.exec_module(module)
-    return module
-
-
-EVIDENCE = load_module("vegetation_integrated_evidence", EVIDENCE_PATH)
-ARTIFACT = load_module("vegetation_integrated_artifact", ARTIFACT_PATH)
-
-
-def test_three_layer_gate_joins_exact_frames_and_preserves_false_authority(
-    tmp_path: Path,
-) -> None:
-    profile = tmp_path / "profile.json"
-    profile.write_text(
-        json.dumps(
-            {
-                "schema_version": EVIDENCE.PROFILE_SCHEMA,
-                "profile_id": "test",
-                "source": {"source_id": "RAVNOVES00", "requested_source_rate_hz": 12.0},
-                "stages": {
-                    "m49_graph_tgs": {"profile_sha256": "a" * 64},
-                    "vegetation": {
-                        "checkpoint_sha256": "b" * 64,
-                        "config_sha256": "c" * 64,
-                        "policy_sha256": "d" * 64,
-                        "provider_map_sha256": "e" * 64,
-                        "inference_stride": 2,
-                        "inference_phase_offset_ms": 40.0,
-                    },
-                },
-                "acceptance": {
-                    "minimum_graph_world_state_fps": 11.2,
-                    "minimum_vegetation_timeline_fps": 11.2,
-                    "minimum_vegetation_inference_fps": 5.6,
-                    "maximum_vegetation_inference_completion_p95_ms": 125.0,
-                    "maximum_semantic_evidence_source_age_ms": 125.0,
-                    "maximum_combined_output_age_p99_ms": 125.0,
-                },
-                "authority": {
-                    "commands_enabled": False,
-                    "actuation_allowed": False,
-                    "navigation_or_safety_accepted": False,
-                    "production_accepted": False,
-                },
-            }
-        ),
-        encoding="utf-8",
-    )
-    m49 = tmp_path / "m49.json"
-    m49.write_text(
-        json.dumps(
-            {
-                "schema_version": EVIDENCE.M49_SCHEMA,
-                "status": "passed",
-                "integrated_runtime_gate_passed": True,
-                "result_id": "m49-test",
-                "identity": {"profile_sha256": "a" * 64},
-                "performance": {"effective_world_state_fps": 11.8},
-                "accounting": {
-                    "graph_admitted": EVIDENCE.FRAME_COUNT,
-                    "graph_delivered": EVIDENCE.FRAME_COUNT,
-                    "tgs_timeline_frames": EVIDENCE.FRAME_COUNT,
-                    "tgs_capacity_drops": 0,
-                },
-            }
-        ),
-        encoding="utf-8",
-    )
-    vegetation = tmp_path / "vegetation.json"
-    vegetation.write_text(
-        json.dumps(
-            {
-                "schema_version": EVIDENCE.VEGETATION_SCHEMA,
-                "result_id": "vegetation-test",
-                "integrated_load_gate_passed": True,
-                "source": {"requested_source_rate_hz": 12.0},
-                "candidate": {"candidate_key": "ddrnet", "checkpoint_sha256": "b" * 64},
-                "identity": {
-                    "config_sha256": "c" * 64,
-                    "policy_sha256": "d" * 64,
-                    "provider_map_sha256": "e" * 64,
-                },
-                "execution": {
-                    "frame_count": EVIDENCE.FRAME_COUNT,
-                    "effective_fps": 11.75,
-                    "effective_timeline_fps": 11.75,
-                    "effective_inference_fps": 5.875,
-                    "inference_stride": 2,
-                    "inference_phase_offset_ms": 40.0,
-                    "inference_frame_count": 2245,
-                    "held_evidence_frame_count": 2244,
-                    "capacity_drop_count": 0,
-                },
-                "timing": {
-                    "completion_age_ms": {"p95": 25.0},
-                    "inference_completion_age_ms": {"p95": 25.0},
-                    "stage_ms": {"p95": 20.0},
-                    "inference_ms": {"p95": 18.0},
-                },
-                "resource": {"gpu_name": "test"},
-                "authority": {
-                    "commands_enabled": False,
-                    "actuation_allowed": False,
-                    "navigation_or_safety_accepted": False,
-                    "production_accepted": False,
-                },
-            }
-        ),
-        encoding="utf-8",
-    )
-    graph_frames = tmp_path / "graph.jsonl"
-    graph_frames.write_text(
-        "".join(
-            json.dumps(
-                {"source_envelope": {"sequence": index}, "completion_age_ns": 40_000_000}
-            )
-            + "\n"
-            for index in range(EVIDENCE.FRAME_COUNT)
-        ),
-        encoding="utf-8",
-    )
-    tgs_frames = tmp_path / "tgs.tsv"
-    tgs_frames.write_text(
-        "timeline_frame_index\tcompletion_age_ms\n"
-        + "".join(f"{index}\t5.0\n" for index in range(EVIDENCE.FRAME_COUNT)),
-        encoding="utf-8",
-    )
-    vegetation_frames = tmp_path / "vegetation.jsonl"
-    vegetation_frames.write_text(
-        "".join(
-            json.dumps(
-                {
-                    "schema_version": "missioncore.lab-v1-vegetation-integrated-frame/v2",
-                    "sequence": index,
-                    "completion_age_ms": 60.0 if index % 2 == 0 else 20.0,
-                    "inference_executed": index % 2 == 0,
-                    "inference_phase_offset_ms": 40.0 if index % 2 == 0 else 0.0,
-                    "semantic_source_sequence": index - (index % 2),
-                    "semantic_evidence_source_age_ms": 60.0
-                    if index % 2 == 0
-                    else 103.333333,
-                }
-            )
-            + "\n"
-            for index in range(EVIDENCE.FRAME_COUNT)
-        ),
-        encoding="utf-8",
-    )
-    telemetry = tmp_path / "telemetry.jsonl"
-    telemetry.write_text(
-        "".join(
-            json.dumps(
-                {
-                    "role": role,
-                    "cpu_percent": "10.0%",
-                    "memory_usage": "1GiB / 64GiB",
-                    "memory_percent": "1.56%",
-                }
-            )
-            + "\n"
-            for role in ("graph", "tgs", "triton", "vegetation")
-        ),
-        encoding="utf-8",
-    )
-    output = tmp_path / "result.json"
-
-    result = EVIDENCE.build(
-        profile_path=profile,
-        m49_result_path=m49,
-        graph_frames_path=graph_frames,
-        tgs_timing_path=tgs_frames,
-        vegetation_result_path=vegetation,
-        vegetation_frames_path=vegetation_frames,
-        telemetry_path=telemetry,
-        output_path=output,
-        release_sha256="f" * 64,
-    )
-
-    assert result["status"] == "passed"
-    assert result["source"]["joined_frame_count"] == EVIDENCE.FRAME_COUNT
-    assert result["performance"]["three_layer_output_age_ms"]["p99"] == 60.0
-    assert result["checks"]["authority_remains_false"] is True
-    assert result["production_accepted"] is False
-
-
-def test_integrated_release_is_deterministic_and_contains_one_vegetation_candidate(
-    monkeypatch, tmp_path: Path
-) -> None:
-    def fake_wheel(_source_root: Path, output: Path) -> Path:
-        output.mkdir(parents=True, exist_ok=True)
-        wheel = output / ARTIFACT.WHEEL_NAME
-        wheel.write_bytes(b"clean committed wheel\n")
-        return wheel
-
-    monkeypatch.setattr(ARTIFACT, "build_wheel", fake_wheel)
-    revision = "f" * 40
-    first = ARTIFACT.build_artifact(
-        "mission-core-vegetation-integrated-unit-001",
-        tmp_path / "first",
-        revision=revision,
-        source_root=REPOSITORY_ROOT,
-    )
-    second = ARTIFACT.build_artifact(
-        "mission-core-vegetation-integrated-unit-001",
-        tmp_path / "second",
-        revision=revision,
-        source_root=REPOSITORY_ROOT,
-    )
-
-    assert Path(first["artifact"]).read_bytes() == Path(second["artifact"]).read_bytes()
-    with tarfile.open(first["artifact"], "r:gz") as archive:
-        names = set(archive.getnames())
-        release_stream = archive.extractfile("payload/release.json")
-        assert release_stream is not None
-        release = json.loads(release_stream.read())
-    assert "payload/run_vegetation_integrated_load.py" in names
-    assert "payload/build_vegetation_integrated_graph_evidence.py" in names
-    assert (
-        "payload/lab-v1-vegetation-integrated-multirate-phased-shadow-v3.json"
-        in names
-    )
-    assert release["semantic_inference_rate_hz"] == 6.0
-    assert release["semantic_inference_phase_offset_ms"] == 40.0
-    assert release["scope"]["heavy_vegetation_candidates"] == ["ddrnet"]
-    assert all(value is False for value in release["authority"].values())
-
-
-def test_worker_gate_reuses_shared_barrier_and_keeps_canonical_triton_unchanged() -> None:
-    runner = RUNNER_PATH.read_text(encoding="utf-8")
-    wrapper = POWERSHELL_PATH.read_text(encoding="utf-8")
-    assert '"source-paced-multirate-integrated-shadow/v2"' in runner
-    assert "wait_for_shared_start(" in runner
-    assert '"bounded-compressed-scene-buffer/v1"' in runner
-    assert "buffer_compressed_video(" in runner
-    assert '"compressed_scene_prefetch": True' in runner
-    assert '"full_route_rgb_prefetch": False' in runner
-    assert "decode_source(source_capture, expected_size)" in runner
-    assert '"camera_semantics_can_clear_rigid_geometry": False' in runner
-    assert "--runtime-video-cache /tmp/vegetation-right.mp4" in wrapper
-    assert "--inference-stride 2" in wrapper
-    assert "--inference-phase-offset-ms 40.0" in wrapper
-    assert '--tmpfs "/tmp:rw,noexec,nosuid,size=2g"' in wrapper
-    assert "$VegetationLoadGate" in wrapper
-    assert '"vegetation"' in wrapper
-    assert "if ($canonicalAfter.Id -cne $canonicalId" not in wrapper
-    assert "$canonicalAfter.Id -cne $canonicalId" in wrapper

tests/test_laboratory_evidence_registry.py

@@ -127,10 +127,9 @@ def test_product_registry_declares_every_advanced_evidence_source() -> None:
         repository_root / "config" / "laboratories"
     )
 
-    assert len(registry.definitions) == 44
+    assert len(registry.definitions) == 43
     assert {item.work_id for item in registry.definitions} >= {
         "lab-v1-vegetation-shadow",
-        "lab-v1-vegetation-benchmark",
         "e31-source-binding",
         "e46j-raw-fisheye-realtime",
         "e47-semantic-slam-shadow",

tests/test_laboratory_value_review_registry.py

@@ -80,7 +80,7 @@ def test_product_value_review_registry_covers_reviewed_laboratory_families() ->
         root / "config" / "laboratory-value-review.json"
     )
 
-    assert len(registry.entries) == 42
+    assert len(registry.entries) == 41
     assert {entry.catalog_id for entry in registry.entries} >= {
         "e28-local-surface",
         "e46d-temporal-failure-audit",
@@ -94,5 +94,4 @@ def test_product_value_review_registry_covers_reviewed_laboratory_families() ->
         "m49-tgs-fail-closed-evidence",
         "m49-tgs-full-shadow",
         "lab-v1-vegetation-shadow",
-        "lab-v1-vegetation-benchmark",
     }

tests/test_vegetation_shadow_lab.py

@@ -1,70 +1,23 @@
 from __future__ import annotations
 
 import hashlib
-import io
 import json
-import shutil
-import struct
 import zipfile
 from pathlib import Path
 from types import SimpleNamespace
 
-import numpy as np
 from fastapi import FastAPI
 from fastapi.testclient import TestClient
-from PIL import Image
 
-import k1link.laboratory.vegetation_policy_review as policy_review_module
-import k1link.laboratory.vegetation_policy_video as policy_video_module
-import k1link.laboratory.vegetation_shadow_lab as vegetation_lab_module
 from k1link.laboratory import LaboratoryEvidenceRegistry
+import k1link.laboratory.vegetation_shadow_lab as vegetation_lab_module
 from k1link.laboratory.evidence_report import verify_laboratory_evidence_result
-from k1link.laboratory.vegetation_policy_review import seal_vegetation_policy_review
 from k1link.laboratory.vegetation_shadow_lab import seal_vegetation_shadow_lab
 from k1link.web.vegetation_shadow_lab_api import build_vegetation_shadow_lab_router
 
 REPOSITORY_ROOT = Path(__file__).resolve().parents[1]
 
 
-def test_coarse_policy_masks_mark_every_outside_fov_pixel_undefined(
-    tmp_path: Path,
-    monkeypatch,
-) -> None:
-    monkeypatch.setattr(policy_video_module, "FRAME_COUNT", 1)
-    monkeypatch.setattr(
-        policy_video_module,
-        "fine_to_policy_lut",
-        lambda _taxonomy, _provider_map: np.full(256, 4, dtype=np.uint8),
-    )
-    source = tmp_path / "fine.zip"
-    fine_buffer = io.BytesIO()
-    Image.new("L", (800, 600), color=1).save(fine_buffer, format="PNG")
-    with zipfile.ZipFile(source, "w") as archive:
-        archive.writestr("masks/frame-000001.png", fine_buffer.getvalue())
-
-    valid_fov = np.zeros((600, 800), dtype=np.uint8)
-    valid_fov[:, :400] = 255
-    valid_fov_path = tmp_path / "valid-fov.png"
-    Image.fromarray(valid_fov, mode="L").save(valid_fov_path)
-    destination = tmp_path / "coarse.zip"
-    counts = policy_video_module.build_policy_mask_archive(
-        source_archive=source,
-        destination_archive=destination,
-        fine_taxonomy={},
-        provider_label_map={},
-        valid_fov_mask=valid_fov_path,
-    )
-    with (
-        zipfile.ZipFile(destination) as archive,
-        Image.open(io.BytesIO(archive.read("masks/frame-000001.png"))) as image,
-    ):
-        coarse = np.asarray(image.convert("L"))
-    assert np.all(coarse[:, :400] == 4)
-    assert np.all(coarse[:, 400:] == 9)
-    assert counts[4] == 600 * 400
-    assert counts[9] == 600 * 400
-
-
 def _sha256(path: Path) -> str:
     return hashlib.sha256(path.read_bytes()).hexdigest()
 
@@ -271,189 +224,8 @@ def test_vegetation_shadow_lab_seals_autonomous_visual_evidence(
     assert mask.content == b"\x89PNG\r\n\x1a\n"
     assert mask.headers["cache-control"].endswith("immutable")
 
-    full_archive_payloads = (b"\x89PNG\r\n\x1a\ncity", b"\x89PNG\r\n\x1a\nvegetation")
-    full_timeline_payload = struct.pack("<2Q", 1_000_000_000, 1_100_000_000)
-    full_identity = dict(manifest["identity"])
-    full_route = {
-        "frame_count": 2,
-        "timeline": {
-            "path": "video/frame-source-times-ns.bin",
-            "sha256": hashlib.sha256(full_timeline_payload).hexdigest(),
-            "byte_length": len(full_timeline_payload),
-            "encoding": "uint64-le-nanoseconds",
-            "frame_count": 2,
-        },
-        "layers": {
-            layer: {"mask_archive": {"path": "video/full-route-masks.zip"}}
-            for layer in ("city", "vegetation")
-        },
-    }
-    full_identity["route_full_review"] = full_route
-    full_identity_sha = hashlib.sha256(
-        json.dumps(
-            full_identity,
-            ensure_ascii=False,
-            sort_keys=True,
-            separators=(",", ":"),
-        ).encode("utf-8")
-    ).hexdigest()
-    full_result_id = f"lab-v1-vegetation-shadow-{full_identity_sha}"
-    full_root = result_root.parent / full_result_id
-    shutil.copytree(result_root, full_root)
-    full_archive = full_root / "video" / "full-route-masks.zip"
-    full_archive.parent.mkdir(exist_ok=True)
-    with zipfile.ZipFile(full_archive, "x", compression=zipfile.ZIP_STORED) as frozen:
-        for sequence, payload in enumerate(full_archive_payloads, start=1):
-            frozen.writestr(f"masks/frame-{sequence:06d}.png", payload)
-    full_timeline = full_root / "video" / "frame-source-times-ns.bin"
-    full_timeline.write_bytes(full_timeline_payload)
-    full_manifest = dict(manifest)
-    full_manifest["result_id"] = full_result_id
-    full_manifest["identity"] = full_identity
-    full_manifest["identity_sha256"] = full_identity_sha
-    full_manifest["route_full_review"] = full_route
-    full_manifest["artifacts"] = [
-        *manifest["artifacts"],
-        {
-            "role": "full-route-mask-fixture",
-            "path": "video/full-route-masks.zip",
-            "byte_length": full_archive.stat().st_size,
-            "sha256": _sha256(full_archive),
-            "media_type": "application/zip",
-        },
-        {
-            "role": "full-route-frame-timeline",
-            "path": "video/frame-source-times-ns.bin",
-            "byte_length": full_timeline.stat().st_size,
-            "sha256": _sha256(full_timeline),
-            "media_type": "application/octet-stream",
-        },
-    ]
-    (full_root / "result.json").write_text(
-        json.dumps(full_manifest, ensure_ascii=False, sort_keys=True, separators=(",", ":"))
-        + "\n",
-        encoding="utf-8",
-    )
-    for layer, sequence, expected in (
-        ("city", 0, full_archive_payloads[0]),
-        ("vegetation", 1, full_archive_payloads[1]),
-    ):
-        response = client.get(
-            f"/api/v1/laboratory/vegetation-shadow/{full_result_id}"
-            f"/route-masks/{layer}/{sequence}"
-        )
-        assert response.status_code == 200
-        assert response.content == expected
-        assert response.headers["cache-control"].endswith("immutable")
-    assert client.get(
-        f"/api/v1/laboratory/vegetation-shadow/{full_result_id}/route-masks/city/2"
-    ).status_code == 404
-    timeline = client.get(
-        f"/api/v1/laboratory/vegetation-shadow/{full_result_id}/route-timeline"
-    )
-    assert timeline.status_code == 200
-    assert timeline.content == full_timeline_payload
-    assert timeline.headers["cache-control"].endswith("immutable")
-
     (result_root / asset_path).write_bytes(b"tampered")
     assert (
         client.get(f"/api/v1/laboratory/vegetation-shadow/{result_root.name}").status_code
         == 503
     )
-
-
-def test_policy_review_reuses_sealed_video_and_links_yolox_tgs(
-    tmp_path: Path,
-    monkeypatch,
-) -> None:
-    roots = {}
-    for candidate, vegetation_iou in (("ddrnet", 0.64), ("ppliteseg", 0.61)):
-        for mode in ("goose", "ravnoves"):
-            root = tmp_path / "worker" / f"{candidate}-{mode}"
-            _worker_result(root, candidate=candidate, mode=mode, vegetation_iou=vegetation_iou)
-            roots[(candidate, mode)] = root
-    video_root = tmp_path / "worker" / "ddrnet-ravnoves-video"
-    _video_worker_result(video_root)
-    m47_root = tmp_path / f"m47-reference-graph-lab-{'a' * 64}"
-    m47_root.mkdir()
-    base_m4_result_id = f"m4-threat-replay-{'f' * 64}"
-    monkeypatch.setattr(
-        vegetation_lab_module,
-        "read_m47_reference_graph_lab",
-        lambda _root: SimpleNamespace(
-            result_id=m47_root.name,
-            report={
-                "source": {"source_id": "RAVNOVES00"},
-                "visual_evidence": {
-                    "linked_result_id": base_m4_result_id,
-                    "timeline_frames": 4489,
-                },
-            },
-        ),
-    )
-    base_root = seal_vegetation_shadow_lab(
-        ddrnet_goose_root=roots[("ddrnet", "goose")],
-        ppliteseg_goose_root=roots[("ppliteseg", "goose")],
-        ddrnet_ravnoves_root=roots[("ddrnet", "ravnoves")],
-        ppliteseg_ravnoves_root=roots[("ppliteseg", "ravnoves")],
-        output_root=tmp_path / "results",
-        ddrnet_ravnoves_video_root=video_root,
-        m47_reference_graph_lab_root=m47_root,
-    )
-    tgs_result_id = f"m49-tgs-full-shadow-{'9' * 64}"
-    monkeypatch.setattr(
-        policy_review_module,
-        "read_m49_tgs_full_shadow",
-        lambda _root: SimpleNamespace(
-            result_id=tgs_result_id,
-            report={
-                "source": {
-                    "source_id": "RAVNOVES00",
-                    "linked_visual_result_id": base_m4_result_id,
-                },
-                "timeline": {"frame_count": 4489},
-            },
-        ),
-    )
-
-    def fake_policy_archive(**kwargs) -> list[int]:
-        shutil.copyfile(kwargs["source_archive"], kwargs["destination_archive"])
-        assert kwargs["valid_fov_mask"].is_file()
-        return [4489 * 800 * 600, *([0] * 9)]
-
-    monkeypatch.setattr(policy_review_module, "build_policy_mask_archive", fake_policy_archive)
-    valid_fov_mask = tmp_path / "valid-fov-mask.png"
-    Image.new("L", (800, 600), color=255).save(valid_fov_mask)
-    result_root = seal_vegetation_policy_review(
-        base_lab_root=base_root,
-        mission_policy_path=REPOSITORY_ROOT
-        / "config/perception/lab-v1-vegetation-mission-policy-v1.json",
-        provider_label_map_path=REPOSITORY_ROOT
-        / "config/perception/lab-v1-vegetation-provider-label-map-v1.json",
-        m49_tgs_full_shadow_root=tmp_path / "sealed-tgs",
-        valid_fov_mask_path=valid_fov_mask,
-        output_root=tmp_path / "results",
-        created_at_utc="2026-08-28T08:00:00+00:00",
-    )
-    manifest = json.loads((result_root / "result.json").read_text("utf-8"))
-    route = manifest["route_video"]
-    assert route["view_kind"] == "coarse-material-policy-review"
-    assert route["linked_tgs_result_id"] == tgs_result_id
-    assert route["fusion"]["pixel_raster_fusion"] is False
-    assert route["fusion"]["camera_semantic_temporal_filter"] == "none"
-    assert route["taxonomy"]["schema_version"] == (
-        "missioncore.lab-v1-terrain-policy-taxonomy/v1"
-    )
-    assert len(route["taxonomy"]["classes"]) == 10
-    assert route["valid_fov"]["outside_valid_fov_class_id"] == 9
-    assert len(manifest["artifacts"]) == 80
-    assert manifest["authority"]["commands_enabled"] is False
-    assert manifest["decision"]["multilayer_policy_review_ready"] is True
-
-    app = FastAPI()
-    app.include_router(build_vegetation_shadow_lab_router(root_provider=lambda: result_root.parent))
-    response = TestClient(app).get(
-        f"/api/v1/laboratory/vegetation-shadow/{result_root.name}/masks/0"
-    )
-    assert response.status_code == 200
-    assert response.content == b"\x89PNG\r\n\x1a\n"