give segmentation QA the same threshold-independent view as detection

Detection QA reports a precision/recall curve, average precision and a
calibration sweep; segmentation QA reported a single operating point. Both rank
their outputs by confidence, so the same view applies, and the asymmetry meant
the two panels answered different questions about comparable runs — an
inconsistency introduced when detection gained the curve.

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

backend/app/schemas/segmentation.py

@@ -32,6 +32,8 @@ class SegmentationQaRequest(BaseModel):
     iou_threshold: float = Field(default=0.5, ge=0.0, le=1.0)
     class_name: str | None = None
     min_confidence: float | None = Field(default=None, ge=0.0, le=1.0)
+    # Read off the one matching pass, exactly as for detection.
+    calibration_thresholds: list[float] = Field(default_factory=list, max_length=32)
 
 
 class SegmentationRunResponse(BaseModel):