fix(migration): 0045 backfill filters to >= store floor (supersedes #764 prune)

The #764 in-place prune (rewrite tagger_predictions to >=0.70) is too slow on
100 GB of TOAST and fails at its soft limit (interrupts a query mid-flight ->
'another command is already in progress'). #768 supersedes it: extract only
the >=floor predictions into image_prediction via this set-based backfill,
then drop the column (step 3) — reading 100 GB once + writing ~840k small rows
beats rewriting 100 GB in place.

So this backfill no longer assumes the prune ran: it filters by
ml_settings.tagger_store_floor (default 0.70) itself, handling the full or
partially-pruned JSON identically.

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

alembic/versions/0045_image_prediction_table.py

@@ -51,6 +51,10 @@ def upgrade() -> None:
     # Backfill from the JSON blob. json_each expands {name: {category,
     # confidence}} into one row per prediction. category defaults to 'general'
     # to mirror the suggestion read path; rows with no confidence are skipped.
+    # Filter to >= the store floor (ml_settings.tagger_store_floor, default
+    # 0.70) right here so this is self-sufficient — it does NOT depend on the
+    # #764 prune having run, and extracting only the >=floor tail keeps
+    # image_prediction small (~tens of rows/image) even from the full JSON.
     op.execute(
         """
         INSERT INTO image_prediction (image_record_id, raw_name, category, score)
@@ -62,6 +66,11 @@ def upgrade() -> None:
              json_each(ir.tagger_predictions) je
         WHERE ir.tagger_predictions IS NOT NULL
           AND je.value ->> 'confidence' IS NOT NULL
+          AND (je.value ->> 'confidence')::double precision
+              >= COALESCE(
+                  (SELECT tagger_store_floor FROM ml_settings WHERE id = 1),
+                  0.70
+              )
         ON CONFLICT (image_record_id, raw_name) DO NOTHING
         """
     )