state the scalar baseline explicitly: 10 statistics, in-sample vs transferred

README.md

@@ -140,3 +140,27 @@ sess = ort.InferenceSession("quality_gate.onnx", sess_options=so, providers=["CP
 Measured effect on the same workload: idle CPU **192% → 16.5%** of one core, active **231% → 88%**,
 thread count 45 → 22, throughput unchanged. On edge hardware this is the difference between
 "runs alongside everything else" and "saturates the machine".
+
+## What "scalar baseline" means on this card
+
+Every margin quoted here is against a **stated baseline**, because a margin without one is not a
+measurement. The baseline is the best single-threshold classifier over **ten cheap statistics**,
+fitted optimistically:
+
+`mean` · `std` · `lapvar` · `hf` (high-frequency energy ratio) · `grad` (Sobel magnitude) ·
+`entropy` · `centre_edge` · `radial_slope` · `row_fft_peak` · `col_fft_peak`
+
+The last four are **spatially aware**, added after an earlier six-statistic baseline — all global
+aggregates — was found to systematically overstate model value on spatially structured tasks. A
+baseline that cannot see *where* anything is loses to a CNN by default. On one test task that flaw
+inflated an apparent margin from **+0.060 to +0.261**.
+
+Two questions are asked with it, and they disagree:
+
+- **in-sample** — threshold fitted on the data it is scored on. Deliberately generous. Answers
+  *is there structure beyond a low-order statistic?*
+- **transferred** — threshold fitted on the training corpus, applied unchanged to the target.
+  Answers *what should I ship?* On one task the in-sample figure was 0.954 and the transferred
+  figure **0.565**.
+
+Where this card quotes a single scalar figure without qualification, it is the in-sample one.