feat: finish the enrollment protocol's reporting checks

Three items from DECISION §8's enrollment protocol were unimplemented and none
of them needed Tier-2 data:

- Eval reports now record provenance: catalog hash, label-file hash, image hash
  and the code revision, so a number can be traced to the inputs that produced
  it. The catalog hash covers the CSV bytes and the view names, not image bytes:
  a re-encoded view of the same product is the same catalog.
- rebuild_catalog flags cross-SKU near-duplicates as text-discriminated
  siblings. These are the near-unknown case no threshold rejects - the geometry
  is real because the sibling shares the brand's artwork - so they are reported
  rather than refused.
- --shelf-tags turns the completeness rule into a live-ready verdict. Without
  the shelf's SKU list the verdict is 'unknown', not a guess.

Adds test_rebuild_catalog.py (11 tests; the tool had no test file). Suite 261
-> 272. Also corrects score_crop_lanes' docstring, which still described the
live path as averaging local scores when the local scale decides on the newest
crop.

recognization/code/engine.py

@@ -527,9 +527,12 @@ class RecognitionEngine:
         """Both lanes' evidence for one crop: local scores and text ranking.
 
         The live path accumulates evidence across looks, so it needs the lanes
-        separately rather than one fused decision: it averages the local scores
-        over the track's window and keeps the newest text reading, then decides
-        once. The still path calls `decide_crop`, which fuses a single crop.
+        separately rather than one fused decision. How it combines them depends
+        on the scale: the local lane decides on the newest crop (its value is a
+        margin over the runner-up, and averaging in an empty observation halves
+        it below the accept line), while the cosine and inlier scales average
+        the scores over the track's window. The text reading is always the
+        newest. The still path calls `decide_crop`, which fuses a single crop.
         """
         scores = self.score_crop(crop, method)
         text_lane = self.identity_text_lane()