Attach the catalog row to accepted still-image answers

`classify_boxes` returned only the identity, so every accepted still-image
answer reached the client as a bare SKU id with no name or price. Measured
through the real server on add/image6.jpg: 41 of 41 accepted detections had no
`product`. The live path attached the row; the still path did not, which is
the F-02 finding the decision document recorded as still live in production.

The row is attached where the decision is made, so both paths carry it, and
`recognize` passes it through to the wire. Verified through the running server:
41 of 41 accepted answers now carry product_name, price, and stock counts.

Also corrects the decision document's full-resolution claim, which was
measured before the reference-upscaling and text-signature fixes: it said 77%
precision with 11 false accepts and a hub alarm, where the current lane reads
88% with 5 and no alarm. The accept line does not need to differ by input path
- correct facings sit at p25 2.07 (full) and 2.87 (live), wrong ones at p75
1.00 and 1.66, so 2.0 is the boundary in both - and the results table and the
stream G1 numbers are refreshed to the current measurements.

recognization/code/test_engine.py

@@ -244,6 +244,33 @@ class EngineClassificationTests(unittest.TestCase):
         self.assertEqual(rows[0]["identity"]["state"], "recognized")
         self.assertEqual(rows[0]["identity"]["sku_id"], "sku_a")
 
+    def test_an_accepted_detection_carries_its_catalog_row(self):
+        """The phone must not have to look up a name and price from a bare SKU.
+
+        `classify_boxes` returned only the identity, so every accepted
+        still-image answer reached the client as an id with no product row:
+        measured through the real server, 41 of 41 accepted detections had no
+        `product`. The live path attached it; the still path did not.
+        """
+        engine, _, _, _ = make_engine()
+        detections = [
+            {"id": 0, "box": [10, 10, 60, 90], "confidence": 0.9,
+             "identity": identity_payload(None)},
+        ]
+        rows = engine.classify_boxes(make_image(), detections, method="hybrid")
+        self.assertEqual(rows[0]["identity"]["state"], "recognized")
+        self.assertEqual(rows[0]["product"]["product_name"], "Alpha")
+        self.assertEqual(rows[0]["product"]["price"], 100.0)
+
+        result = engine.recognize(make_image(), method="hybrid")
+        accepted = [
+            row for row in result["detections"]
+            if row["identity"]["identity_level"] == "accepted"
+        ]
+        self.assertTrue(accepted, "the fake detector and matcher accept a box")
+        for row in accepted:
+            self.assertIn("product", row, "an accepted answer must carry its row")
+
     def test_a_failing_crop_reports_unrecognized_not_pending(self):
         """One bad crop must not fail the batch, and must not read as pending.