serve a promoted model at the post-processing it was gated on

scripts/evaluate_belgium_building_candidate.py freezes its post-processing
before the protected test — NMS IoU and a containment threshold selected during
calibration, defaulting to 1.0. The runtime applied a hardcoded 0.85, so a
promoted candidate was served under suppression its evaluation never measured
and dropped detections the gate had counted. Neither report showed the
difference. That constant was mine, added without noticing the evaluation
pipeline already had a tuned value for the same concept.

Containment is now configuration, recorded on every run beside the duplicate
IoU threshold, so an operator can serve a candidate at the value it was gated
at and afterwards see which value produced a given score.

Two runs that post-processed differently produced different candidate sets from
the same model output, so the comparison endpoint refuses to rank them. Runs
recorded before those values were persisted carry none, and absence is not
treated as a difference.

The remaining gap is deliberate and documented rather than closed: the gate
scores the model on its protected test set, the workbench scores the whole
pipeline including coverage clipping and the tile-edge filter. A promoted
candidate will not reproduce its gate F1 exactly, and pretending otherwise
would be the worse answer.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

backend/app/core/config.py

@@ -391,6 +391,12 @@ class Settings(BaseSettings):
     yolo_suppress_tile_edge_detections: bool = Field(
         default=True, validation_alias="YOLO_SUPPRESS_TILE_EDGE_DETECTIONS"
     )
+    # Intersection over the smaller box. The candidate evaluation freezes this
+    # during calibration; serving a promoted model at a different value means
+    # the runtime suppresses detections the gate counted.
+    yolo_containment_nms_threshold: float = Field(
+        default=0.85, ge=0.0, le=1.0, validation_alias="YOLO_CONTAINMENT_NMS_THRESHOLD"
+    )
     yolo_batch_size: int = Field(default=1, validation_alias="YOLO_BATCH_SIZE")
     yolo_seg_enabled: bool = Field(default=False, validation_alias="YOLO_SEG_ENABLED")
     yolo_seg_model_path: str | None = Field(default=None, validation_alias="YOLO_SEG_MODEL_PATH")