Harden detection result review

scripts/README.md

@@ -793,6 +793,24 @@ used only when source IDs are absent. Invalid or missing geometry fails the
 audit instead of being silently skipped. The tools do not run inference,
 create QA records, mutate model defaults or download data/models.
 
+Audit the false-positive review load of one persisted evidence portfolio before
+turning detections into hard-negative training input:
+
+```bash
+python scripts/audit_detection_false_positive_evidence.py \
+  --portfolio artifacts/model-review/small-building-candidate/evidence-portfolio/calibration_evidence_portfolio.json \
+  --output-dir artifacts/model-review/small-building-candidate/false-positive-audit
+```
+
+The command validates the portfolio role counts against each persisted evidence
+GeoJSON, rejects invalid/non-polygon geometry, computes WGS84 geodesic area and
+size buckets, and reports false-positive pressure per AOI, class and
+AOI-qualified source tile. It writes `detection_false_positive_audit.json`, a
+Markdown handoff and combined `false_positives.geojson` for map review. Original
+evidence properties and geometry are preserved. Confidence statistics are only
+computed when confidence is actually present in persisted evidence; missing
+coverage is reported explicitly and never inferred from the run threshold.
+
 Docker images install only the GIS runtime by default. To build a local/Tower
 image with PyTorch/Ultralytics available for the configured-YOLO preflight and
 runtime path, set: