feat: scope detection QA to inference coverage

frontend/src/components/detection/DetectionLab.tsx

@@ -933,6 +933,33 @@ export function DetectionLab({
             <p>Mean IoU: {detectionQaResult.mean_iou?.toFixed(3) ?? 'n/a'}</p>
             <p>False positives: {detectionQaResult.false_positives}</p>
             <p>False negatives: {detectionQaResult.false_negatives}</p>
+            {detectionQaResult.coverage ? (
+              <div className="detection-qa-diagnostic">
+                <span>Inference coverage</span>
+                <strong>
+                  {detectionQaResult.coverage.applied
+                    ? `${detectionQaResult.coverage.reference_evaluated_count} of ${detectionQaResult.coverage.reference_raw_count} reference features evaluated`
+                    : 'No tile manifest coverage applied'}
+                </strong>
+                <p>
+                  {detectionQaResult.coverage.applied
+                    ? `${detectionQaResult.coverage.reference_excluded_outside_count} outside coverage, ${detectionQaResult.coverage.reference_clipped_boundary_count} clipped at the boundary, ${detectionQaResult.coverage.tile_count} tiles.`
+                    : 'This run uses the complete selected reference population.'}
+                </p>
+              </div>
+            ) : null}
+            {detectionQaResult.box_to_footprint_diagnostics ? (
+              <div className="detection-qa-diagnostic detection-qa-diagnostic-caution">
+                <span>Box-to-footprint diagnostic only</span>
+                <strong>
+                  {detectionQaResult.box_to_footprint_diagnostics.envelope_matches} envelope matches versus{' '}
+                  {detectionQaResult.box_to_footprint_diagnostics.strict_matches} canonical matches
+                </strong>
+                <p>
+                  {detectionQaResult.box_to_footprint_diagnostics.possible_box_to_footprint_mismatch_count} possible matching artifacts. Canonical precision, recall and F1 above remain footprint-IoU based.
+                </p>
+              </div>
+            ) : null}
           </div>
         ) : null}
       </div>

frontend/src/styles/premium.css

@@ -983,6 +983,38 @@ details.ai-lab-model-surface > summary strong {
   white-space: normal;
 }
 
+.detection-qa-diagnostic {
+  grid-column: 1 / -1;
+  display: grid;
+  gap: 0.2rem;
+  margin-top: 0.35rem;
+  border-left: 3px solid var(--accent);
+  padding: 0.55rem 0.65rem;
+  background: var(--accent-soft);
+}
+
+.detection-qa-diagnostic span {
+  color: var(--muted);
+  font-size: 0.68rem;
+  font-weight: 800;
+  text-transform: uppercase;
+}
+
+.detection-qa-diagnostic strong {
+  font-size: 0.82rem;
+}
+
+.detection-qa-diagnostic p {
+  margin: 0;
+  color: var(--muted);
+  font-size: 0.74rem;
+}
+
+.detection-qa-diagnostic-caution {
+  border-left-color: var(--warning);
+  background: #fff8eb;
+}
+
 .basemap-policy-notice {
   margin-bottom: 0.65rem;
   border: 0;

frontend/src/types.ts

@@ -559,6 +559,8 @@ export interface DetectionQaResult {
   reference_dataset_id: string
   candidate_feature_count: number
   reference_feature_count: number
+  candidate_feature_count_raw?: number
+  reference_feature_count_raw?: number
   matches: number
   false_positives: number
   false_negatives: number
@@ -568,6 +570,36 @@ export interface DetectionQaResult {
   mean_iou?: number | null
   iou_threshold: number
   warnings: string[]
+  coverage?: {
+    applied: boolean
+    mode: string
+    manifest_path?: string | null
+    tile_count: number
+    source_crs_values: string[]
+    candidate_raw_count: number
+    candidate_evaluated_count: number
+    candidate_excluded_outside_count: number
+    candidate_clipped_boundary_count: number
+    reference_raw_count: number
+    reference_evaluated_count: number
+    reference_excluded_outside_count: number
+    reference_clipped_boundary_count: number
+  }
+  box_to_footprint_diagnostics?: {
+    diagnostic_only: boolean
+    canonical_method: string
+    diagnostic_method: string
+    iou_threshold: number
+    strict_matches: number
+    envelope_matches: number
+    possible_box_to_footprint_mismatch_count: number
+    envelope_false_positives: number
+    envelope_false_negatives: number
+    envelope_precision?: number | null
+    envelope_recall?: number | null
+    envelope_f1_score?: number | null
+    envelope_mean_iou?: number | null
+  }
 }
 
 export type SegmentationModelCapability = DetectionModelCapability