Card: condense row notes to who scored each row

README.md

@@ -175,49 +175,11 @@ hf jobs run --flavor a100-large --timeout 2h \\
   with the four scores, so they show on the Hub leaderboard once merged. Add
   `--env MODE=specialist` if you fitted the model on `train`.
 
-### Notes on the rows
-
-- **Liquid AI d1** was measured on 2026-09-30 through Liquid's API
-  (`https://api.liquid.ai/v1/systemone`, `model: d1:free`), with the same client
-  code as the Jev row: all 2,000 decisions, zero errors. It ties Decider 1 on
-  `noul` (0.840) and `choice` (0.732 vs 0.733) and trails on `score` (0.677 vs
-  0.739), and it beats Jev on accuracy and KL. Liquid lists no per-token price
-  yet, so the row shows the free tier.
-- **Jev 1.13.0** was measured on 2026-09-18 through TypeSafe's API (`jev-latest`,
-  which reported `jev-1.13.0`): all 2,000 decisions, zero errors, $0.016 in total.
-  Its accuracy is near the 0.735 ceiling, but it puts nearly all its probability
-  on one answer, which is where the KL gap comes from. Its confidence is not badly
-  calibrated (overconfidence +0.023); the gold is a three-sample spread it does not
-  reproduce.
-- **Jeff** ([firelex/jeff](https://github.com/firelex/jeff), Apache-2.0) was scored
-  on 2026-09-29 through the same client code as Jev, against Jeff's own
-  `jeff-serve` (commit `2c1bfce`) with the calibration each checkpoint ships. All
-  three clear the Prior on accuracy but not on KL. Jeff's own 83.1% comes from a
-  different five-benchmark panel. If there is a better way to serve them, open a
-  discussion and we will rescore.
-- **Self-reported rows** come from discussions
-  [#5](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/5)
-  (prima-ratio), [#7](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/7)
-  (Bongard-mini), [#8](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/8)
-  (OpenDecider), [#4](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/4)
-  (od1), [#3](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/3)
-  (soft-decider), [#2](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/2)
-  (Laya), [#34](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/34)
-  (Arbiter-4B), [#35](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/35)
-  (linne-oak 1.0), [#36](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/36)
-  (Decider-4B) and [#39](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/39)
-  (SafeDecide, which scores each question on its own rather than all five in one request). Each submitter states the mode; we have not re-run them. If a number looks
-  wrong, say so in the discussion and we will correct it.
-- **Bekko System One v0** ([hotchpotch](https://huggingface.co/collections/hotchpotch/bekko-system-one-v0-6abc47eab9c4fe4ec83d9e30)) was scored by us on 2026-10-01 on CPU (M3 Max, one call per case with all five questions, pinned revisions `b886a1f9` 17M, `ab7685f2` 68M, `1960df56` 400M), with the same scorer as the other rows. Its training data includes this benchmark's `train` split, with our teacher labels, so it sits in the second table; none of our 400 test cases are in its training data. We mapped our option and rubric text onto its candidate format in our option order. Its model cards say the license is not yet finalized. The author invited a score on X; we are glad to re-score with a different input mapping if he prefers.
-- **Specialists** use [Adaptive Classifier](https://github.com/codelion/adaptive-classifier)
-  0.2.0, one classifier per question on a frozen encoder, tuned on a held-out
-  quarter of `train` (mean pooling, `max_length` 512, 30 epochs,
-  `prototype_weight` 0.3). Each case is entered four times, split across labels in
-  proportion to its gold, so the soft target survives hard-label training; that
-  cut KL by a third.
-- **Prior** answers each question's `train` label frequencies for every case. It
-  has the best ECE while knowing nothing, which is why KL and Brier are the
-  columns to read, not ECE.
+### Who scored each row
+
+- **Scored by us:** meraGPT Decider 1, TypeSafe Jev 1.13.0, Liquid AI d1, Featherless Simple Jev, the three Jeff models, the three rows served by Decision Models ([#38](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/38)), the Bekko and Adaptive Classifier rows, and the Prior and Uniform references.
+- **Scored by Hugging Face:** the rows linked to [#37](https://huggingface.co/datasets/LocalLLaMA/typed-decisions/discussions/37), run on Hugging Face Jobs.
+- **Reported by the model's creator:** every other row, in its own discussion.
 
 ## Reading a score