Correct the Gemma comparison, and give rounds 07 and 08 their gold figures

README.md

@@ -109,8 +109,12 @@ residual that shrank as curation improved and are marked accordingly.
 | `round04` | Retrained on the 502-reagent lexicon | — | — | **abandoned**: trained on 36% duplicate rows |
 | `round05` | Deduplicated training set, 95,818 distinct pairs | 87.58% | 93.41% | first frozen-benchmark round |
 | `round06` | Full epoch, rank 16, 6,856 empty-answer examples | **90.52%** at iter 2,000 | 94.36% | **the one to use** — the *final* adapter, see below |
-| `round07` | 32B-teacher labels, 92.6% precise, 0.23 epochs | — | — | **regressed**: precision 99.6% → 92.3% |
-| `round08` | Same idea, labels 97.6% precise, 1.06 epochs | — | — | **regressed**: precision 94.5%. Closes the line of work |
+| `round07` | 32B-teacher labels, 92.6% precise, 0.23 epochs | not run — scored on gold instead: 93.6% / 89.1% | — | **regressed** against round 06 |
+| `round08` | Same idea, labels 97.6% precise, 1.06 epochs | not run — scored on gold instead: 95.3% / 89.1% | — | **regressed**. See the confound below |
+
+Rounds 07 and 08 skipped the frozen benchmark deliberately. By then the hand-labelled gold set
+existed, and `identification` had already given three wrong answers to "how long should this
+train"; they were measured against labelled truth instead, which is the stricter test.
 
 ### Which checkpoint to use, and a correction
 
@@ -206,6 +210,25 @@ useful as an **inference-time ensemble**, not as a training signal: the survivin
 correct to 10 wrong, and rounds 07 and 08 established that this student absorbs label noise rather
 than averaging it out.
 
+**Correction, 2026-08-03: Gemma is not the better teacher, and an earlier version of this card
+implied it was.** The `rules + Gemma alone` row above sits beside a Qwen figure measured over a
+different set of records, which is not a comparison. Scored like for like — the 79 gold records
+both models answered and whose labels the lexicon can resolve:
+
+| Teacher, alone | Precision | Recall | F1 | F0.5 | False positives |
+|---|---|---|---|---|---|
+| Gemma-4-31b | 89.7% | 87.9% | 88.8 | 89.4 | 24 |
+| **Qwen2.5-32B** | **93.3%** | 87.9% | **90.5** | **92.2** | **15** |
+
+Both find exactly the same reagents — 210 true positives and 29 misses each. Gemma simply asserts
+nine more that are not in the text. It is also the less reliable harness: **82 of 96 generations
+parsed, against Qwen's 96 of 96**, because its reasoning block consumes the token budget before it
+closes the JSON.
+
+Gemma was chosen for **architectural independence**, which is what an agreement gate requires and
+which it does provide — Qwen2.5 and Gemma-4 fail differently, and that is the whole mechanism. It
+was never the stronger reader, and nothing above depends on it being one.
+
 ## Using it
 
 ```python