feat(simulation): auto-build source mesh collision

feat(simulation): add Gaussian UGV runtime pipeline
2026-08-28 13:48:44 +03:00 · 2026-08-28 11:51:48 +03:00
35 changed files with 284 additions and 3619 deletions
@@ -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") {
@@ -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;
@@ -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,
@@ -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 {
@@ -70,13 +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 VegetationShadowResult {
@@ -194,7 +186,6 @@ function visualCaseValue(
  value: unknown,
  resultId: string,
  expectedKind: "goose" | "ravnoves",
-  endpointRoot: string,
 ): VegetationVisualCase {
  const row = objectValue(value, `vegetation.${expectedKind}.case`);
  exact(row.source_kind, expectedKind, "vegetation.case.source_kind");
@@ -213,7 +204,7 @@ function visualCaseValue(
    if (!SHA256.test(sha256) || !path.startsWith(`visual/${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/${path
      .split("/")
      .map(encodeURIComponent)
      .join("/")}`;
@@ -266,9 +257,6 @@ function routeVideoValue(value: unknown): VegetationRouteVideo | null {
    "vegetation.route_video.m47_reference_graph_result_id",
  );
  const baseM4ResultId = textValue(row.base_m4_result_id, "vegetation.route_video.base_m4_result_id");
-  const viewKind = row.view_kind === undefined
-    ? "fine-semantic-prediction"
-    : textValue(row.view_kind, "vegetation.route_video.view_kind");
  if (
    !/^lab-v1-ravnoves-video-ddrnet-[a-f0-9]{64}$/.test(workerResultId)
    || !/^m47-reference-graph-lab-[a-f0-9]{64}$/.test(m47ReferenceGraphResultId)
@@ -276,15 +264,6 @@ function routeVideoValue(value: unknown): VegetationRouteVideo | null {
  ) {
    throw new VegetationShadowContractError("vegetation.route_video: identity invalid.");
  }
-  if (viewKind !== "fine-semantic-prediction" && viewKind !== "coarse-material-policy-review") {
-    throw new VegetationShadowContractError("vegetation.route_video: view kind invalid.");
-  }
-  const linkedTgsResultId = viewKind === "coarse-material-policy-review"
-    ? textValue(row.linked_tgs_result_id, "vegetation.route_video.linked_tgs_result_id")
-    : null;
-  if (linkedTgsResultId && !/^m49-tgs-full-shadow-[a-f0-9]{64}$/.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");
@@ -300,9 +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[${expectedId}]`);
@@ -315,95 +296,36 @@ function routeVideoValue(value: unknown): VegetationRouteVideo | null {
      if (color.length !== 3 || color.some((channel) => channel > 255)) {
        throw new VegetationShadowContractError("vegetation.route_video: taxonomy color invalid.");
      }
-      const disposition = item.disposition;
-      if (
-        disposition !== "labeled"
-        && disposition !== "ambiguous"
-        && disposition !== "prediction"
-        && disposition !== "undefined"
-      ) {
+      const disposition: VegetationVideoSemanticClass["disposition"] = expectedId === 0
+        ? "undefined"
+        : "prediction";
+      if (item.disposition !== disposition) {
        throw new VegetationShadowContractError("vegetation.route_video: taxonomy disposition changed.");
      }
-      if (
-        viewKind === "fine-semantic-prediction"
-        && disposition !== (expectedId === 0 ? "undefined" : "prediction")
-      ) {
-        throw new VegetationShadowContractError("vegetation.route_video: fine taxonomy disposition changed.");
-      }
-      const materialClass = item.material_class === null || item.material_class === undefined
-        ? null
-        : textValue(item.material_class, `vegetation.route_video.material[${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,
@@ -413,21 +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 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");
@@ -463,9 +376,9 @@ 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));
+    .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.");
  }
@@ -515,36 +428,5 @@ export async function fetchVegetationShadowResult(
  if (!response.ok) {
    throw new VegetationShadowContractError(`Vegetation LAB недоступна: HTTP ${response.status}.`);
  }
-  return parseResult(
-    await response.json(),
-    resultId,
-    "/api/v1/laboratory/vegetation-shadow",
-  );
-}
-
-export async function fetchVegetationBenchmarkResult(
-  resultId: string,
-  {
-    fetcher = fetch,
-    signal,
-  }: { fetcher?: LaboratoryFetch; signal?: AbortSignal } = {},
-): Promise<VegetationShadowResult> {
-  if (!BENCHMARK_RESULT_ID.test(resultId)) {
-    throw new VegetationShadowContractError("Vegetation benchmark identity недопустима.");
-  }
-  const endpointRoot = "/api/v1/laboratory/vegetation-benchmark";
-  const response = await fetcher(
-    `${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);
 }
@@ -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"] {
@@ -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} />;
  }
@@ -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={{
@@ -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"}
@@ -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.",
-          }}
-        />
-      )}
-    />
-  );
-}
@@ -1,5 +1,3 @@
-import { useEffect, useState } from "react";
-
 import {
  LaboratoryEvidence,
  LaboratoryResultSummary,
@@ -11,85 +9,44 @@ import {
  type VegetationShadowResult,
 } from "../../core/laboratory/vegetationShadow";
 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 });
 }

-function VegetationRouteEvidence({ result }: { result: VegetationShadowResult }) {
-  const route = result.routeVideo!;
-  const [tgs, setTgs] = useState<M49TgsFullShadowResult | null>(null);
-  const [tgsError, setTgsError] = useState<string | null>(null);
+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",
+};

-  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.");
-      }
+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,
+      },
+    };
  });
-    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}
-    </>
-  );
 }

 export function VegetationShadowResultView({
@@ -99,117 +56,143 @@ export function VegetationShadowResultView({
  rigLabel: string;
  result: VegetationShadowResult;
 }) {
-  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 ? (
+      evidence={(
+        <>
          <LaboratoryEvidence
-          eyebrow="M4.7 · RAVNOVES00 FULL VIDEO"
-          title="EoMT CITY / DDRNet VEGETATION + YOLOX + CAUSAL TGS · 4489/4489 · TRUTH отсутствует"
+            eyebrow="M4.8 · GOOSE VEGETATION HARD CASES"
+            title="ERROR: красный — пропуск · жёлтый — лишнее · фиолетовый — перепутан тип · зелёный — совпадение"
            kind="diagnostic-model"
            resizable
          >
-          <VegetationRouteEvidence result={result} />
+            <M48MaskComparisonVisual
+              cases={comparisonCases(result)}
+              initialCandidate={result.selectedCandidate}
+            />
          </LaboratoryEvidence>
-      ) : (
+          {result.routeVideo ? (
            <LaboratoryEvidence
              eyebrow="M4.7 · RAVNOVES00 FULL VIDEO"
-          title="ROUTE ARCHIVE отсутствует"
+              title="DDRNet PREDICTION · 4489/4489 кадров · TRUTH для этой записи отсутствует"
              kind="diagnostic-model"
+              resizable
            >
-          <div className="m4-replay-threat-visual__pane-status" role="alert">
-            Для этой immutable identity нет полного route video evidence.
-          </div>
+              <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.",
          }}
        />
      )}
@@ -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",
@@ -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",
@@ -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/,
@@ -5,7 +5,6 @@ import { after, before, test } from "node:test";
 import { createServer } from "vite";

 let server;
-let fetchVegetationBenchmarkResult;
 let fetchVegetationShadowResult;

 before(async () => {
@@ -14,7 +13,7 @@ before(async () => {
    logLevel: "silent",
    server: { middlewareMode: true },
  });
-  ({ fetchVegetationBenchmarkResult, fetchVegetationShadowResult } = await server.ssrLoadModule(
+  ({ fetchVegetationShadowResult } = await server.ssrLoadModule(
    "/src/core/laboratory/vegetationShadow.ts",
  ));
 });
@@ -24,7 +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 {
@@ -106,50 +104,12 @@ 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 labPayload(route = routeVideo()) {
-  return {
+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({
        schema_version: "missioncore.lab-v1-vegetation-shadow/v1",
        result_id: resultId,
        created_at_utc: "2026-08-27T20:00:00Z",
@@ -180,20 +140,9 @@ function labPayload(route = routeVideo()) {
          goose: Array.from({ length: 12 }, (_, index) => visualCase("goose", index)),
          ravnoves: [],
        },
-    route_video: route,
+        route_video: routeVideo(),
        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" },
-      });
+      }), { status: 200, headers: { "Content-Type": "application/json" } });
    },
  });
  assert.equal(
@@ -205,8 +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\//);
@@ -218,64 +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 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(
+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",
-    ),
-    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, 2);
-  assert.match(resultSource, /linkedTgsResultId/);
-  assert.match(benchmarkSource, /M48MaskComparisonVisual/);
-  assert.doesNotMatch(benchmarkSource, /M49TgsFullShadowEvidence/);
-  assert.equal(benchmarkSource.match(/<LaboratoryEvidence\b/g)?.length, 1);
+  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)),
@@ -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"
-  }
-}
@@ -212,7 +212,6 @@
    }
  ],
  "legacy_work_ids": [
-    "lab-v1-vegetation-benchmark",
    "m48r3-static-occupancy-shadow",
    "m47-reference-graph-shadow",
    "e31-source-binding",
@@ -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-394a5bca860e49a619a90c0f267259fd17e550a0abc4b51f821b1f482040c00e",
-      "signal": "progress",
+      "evidence_id": "lab-v1-vegetation-shadow-ad4d9fbbb21ff8a270b77f559b4e78dcdaf0455afd61afb5033009623984e554",
+      "signal": "failed",
      "lifecycle": "current",
      "visual_evidence": "available"
    }
@@ -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
-  }
-}
@@ -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
-  }
-}
@@ -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
-  }
-}
@@ -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 `
-        --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 $ExpectedArtifactSha256
-        Assert-LastExitCode "M49 integrated vegetation evidence gate"
-    }
+    Assert-LastExitCode "M49 integrated evidence gate"
 } finally {
    foreach ($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"
@@ -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())
@@ -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())
@@ -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))"
@@ -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())
@@ -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",
-]
@@ -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"]
@@ -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",
-]
@@ -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)

@@ -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: (
@@ -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()
@@ -196,13 +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():
@@ -216,10 +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()):
@@ -237,39 +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"))
@@ -283,7 +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"]
@@ -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
@@ -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",
@@ -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",
    }
@@ -1,69 +1,23 @@
 from __future__ import annotations

 import hashlib
-import io
 import json
-import shutil
 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()

@@ -275,100 +229,3 @@ def test_vegetation_shadow_lab_seals_autonomous_visual_evidence(
        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"
Author	SHA1	Message	Date
DCCONSTRUCTIONS	2e44e27967	feat(simulation): auto-build source mesh collision	2026-08-28 13:48:44 +03:00
DCCONSTRUCTIONS	2ccf172319	feat(simulation): add Gaussian UGV runtime pipeline	2026-08-28 11:51:48 +03:00