feat(ml): DB-backed tagger_store_floor (default 0.70), the ingest confidence floor

Promotes the prediction store-floor from the TAGGER_STORE_FLOOR env (default
0.05) to a DB-backed, Settings-UI-tunable ml_settings column (default 0.70).
Storing every tag down to 0.05 from a ~10k-tag tagger is what grew
image_record's TOAST to ~100 GB; the suggestion path already filters at 0.70
and the centroid/learned path covers lower-confidence preferred tags, so the
sub-0.70 tail is redundant. Foundation for plan-task #764 (backfill + reclaim
land next; this only changes the write gate for NEW imports).

- ml_settings.tagger_store_floor (migration 0044, default 0.70)
- tagger.Tagger.infer(store_floor=...); ml task passes settings.tagger_store_floor
- ML admin GET/PATCH expose it; PATCH rejects a category suggestion threshold
  below the floor (nothing below the floor is stored, so the gap surfaces
  nothing) — server backstop for the UI slider clamp
- Settings → ML: store-floor slider + caption; category sliders min-bound to it

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

backend/app/services/ml/tagger.py

@@ -33,8 +33,13 @@ _MODEL_DIR = Path(os.environ.get("ML_MODEL_DIR", "/models")) / "camie"
 _MODEL_FILE = f"{MODEL_NAME}.onnx"
 _METADATA_FILE = f"{MODEL_NAME}-metadata.json"
 
-# Below this confidence, predictions aren't stored (keeps the JSON compact).
-STORE_FLOOR = float(os.environ.get("TAGGER_STORE_FLOOR", "0.05"))
+# Ingest floor below which predictions aren't stored (keeps the JSON compact).
+# DEFAULT/fallback only — the live value is DB-backed
+# (ml_settings.tagger_store_floor) and passed into infer() per call by the ml
+# task. 0.70: the suggestion path already filters there and the centroid path
+# covers lower-confidence preferred tags, so the sub-0.70 tail is redundant
+# (it had bloated image_record's TOAST to ~100 GB; plan-task #764).
+DEFAULT_STORE_FLOOR = 0.70
 
 # The categories FC-2b surfaces in the UI. Others (meta/rating/year) are
 # still stored but the suggestion service filters them out.
@@ -145,10 +150,13 @@ class Tagger:
         arr = arr.transpose(2, 0, 1)                        # HWC -> CHW
         return arr[np.newaxis, :, :, :]                     # NCHW
 
-    def infer(self, image_path: Path) -> dict[str, TagPrediction]:
+    def infer(
+        self, image_path: Path, *, store_floor: float = DEFAULT_STORE_FLOOR,
+    ) -> dict[str, TagPrediction]:
         """Run Camie v2 on one image. Returns {name: TagPrediction} with
-        confidence >= STORE_FLOOR (across all categories — the suggestion
-        service does category filtering later).
+        confidence >= store_floor (across all categories — the suggestion
+        service does category filtering later). store_floor is the DB-backed
+        ml_settings.tagger_store_floor, passed in by the ml task.
 
         v2 emits multiple outputs; we use the refined predictions
         (output[1] per onnx_inference.py). Sigmoid is applied to raw
@@ -167,7 +175,7 @@ class Tagger:
         cats = self._tag_categories
         for idx, score in enumerate(probs):
             conf = float(score)
-            if conf < STORE_FLOOR:
+            if conf < store_floor:
                 continue
             if idx >= len(names):
                 # Output longer than metadata declared — shouldn't happen but