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/tasks/ml.py

@@ -127,7 +127,10 @@ def tag_and_embed(self, image_id: int) -> dict:
                 phase = "video_infer"
                 import numpy as np
 
-                preds = _maxpool_predictions([tagger.infer(f) for f in frames])
+                preds = _maxpool_predictions(
+                    [tagger.infer(f, store_floor=settings.tagger_store_floor)
+                     for f in frames]
+                )
                 embedding = np.mean(
                     [embedder.infer(f) for f in frames], axis=0
                 ).astype("float32")
@@ -136,7 +139,7 @@ def tag_and_embed(self, image_id: int) -> dict:
             else:
                 phase = "tag"
                 t0 = time.monotonic()
-                raw = tagger.infer(src)
+                raw = tagger.infer(src, store_floor=settings.tagger_store_floor)
                 log.info(
                     "tag_and_embed tagged in %.1fs (%d tags): %s",
                     time.monotonic() - t0, len(raw), ctx,