calibrate a confidence threshold from one inference pass

Threshold calibration ran the model over every tile once per threshold — three
GPU passes to compare 0.50, 0.25 and 0.15 on a hundred-tile raster. The answer
is already in a single run at the lowest value: detections above a higher cut
are a subset of it, and duplicate suppression walks candidates in descending
confidence, so a lower-confidence box can never displace a higher-confidence
one. The kept set above any cut is identical whichever threshold the run used,
which is what makes one pass sufficient rather than merely cheaper.

QA now takes calibration_thresholds and reads each operating point off the same
precision/recall walk it already performs, marking the F1-optimal cut. The lab
runs inference once and fills its table from the sweep.

The contract test asserted the per-threshold loop by name, pinning the waste it
was meant to describe. It now states what calibration owes an operator: a row
per requested threshold, from one run.

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

backend/app/api/routes/detection.py

@@ -249,5 +249,6 @@ def compare_detection_run_with_reference(
             iou_threshold=payload.iou_threshold,
             class_name=payload.class_name,
             min_confidence=payload.min_confidence,
+            calibration_thresholds=payload.calibration_thresholds,
         )
     )