Datasets:

codelion
/

neuralcraft-world-data

@@ -110,8 +110,24 @@ Ground truth is the reason to prefer this dataset, not a reason to distrust it.
 ## Choosing what to train on
-Half the frames have a nearly still camera, `W` is held ~89% of the time, and `sneak` is essentially
-never pressed. With 5.5M frames, signal is the constraint, not quantity.
 `quality_*.jsonl` rank every run with ≥48 frames (32,877 runs, 3,097,088 frames) on six
 rank-normalised measures plus length: `control` (does the player act at all), `agreement` (does the
@@ -151,6 +167,34 @@ usage of one shard. Do not try to select shards; select runs.
 > exactly the ones the stride corrupted. The corrected top-35% differs from the old one on 20% of its
 > selections. If you pulled a selection before that date, re-pull it.
 ## Layout
 The `.tar` files are **not a WebDataset**. Each holds many run directories:

 ## Choosing what to train on
+With 5.5M frames, signal is the constraint, not quantity. Measured over 171,158 frames sampled from
+10 shards:
+| channel | ON-rate | | channel | mean | std | p5 | p95 | exactly 0 |
+|---|---|---|---|---|---|---|---|---|
+| `W` | 65.5% | | `cam_dx` | −0.004 | 0.313 | −0.55 | +0.54 | 31.0% |
+| `S` | 2.6% | | `cam_dy` | +0.000 | 0.185 | −0.31 | +0.31 | 35.6% |
+| `A` | 10.5% | | `speed` | +0.120 | 0.425 | −0.58 | +0.93 | 0.0% |
+| `D` | 9.1% | | `turn_rate` | −0.002 | 0.415 | −0.76 | +0.76 | 0.0% |
+| `jump` | 25.5% |
+| `sneak` | 4.5% |
+| `sprint` | 29.2% |
+| `attack` | 24.6% |
+| `use` | 4.9% |
+`W` dominates and roughly a third of frames have a perfectly still camera, so a uniform sample is
+mostly "walking forward, looking nowhere". The camera channels are symmetric about zero, as they
+should be.
 `quality_*.jsonl` rank every run with ≥48 frames (32,877 runs, 3,097,088 frames) on six
 rank-normalised measures plus length: `control` (does the player act at all), `agreement` (does the
 > exactly the ones the stride corrupted. The corrected top-35% differs from the old one on 20% of its
 > selections. If you pulled a selection before that date, re-pull it.
+## Manifest file schema
+`quality_all.jsonl` / `quality_top{20,35,50}.jsonl` — one JSON object per scored run, sorted by
+`score` descending. `integrity.jsonl` — one object per run, **all** runs, unsorted.
+| field | in | meaning |
+|---|---|---|
+| `run` | both | run directory name, e.g. `s00123_r0007` |
+| `tar` | both | which shard holds it, e.g. `data/shard_00123.tar` |
+| `frames` / `actions` | both | file count and action-line count |
+| `count_delta` | integrity | `frames - actions`; 0 everywhere in this release |
+| `corrupt_jpeg` | integrity | files failing the FFD9 end-of-image check; 0 everywhere |
+| `framefile_mismatch` | integrity | records whose `frame_file` names the wrong frame; 0 everywhere |
+| `width_ok`, `nan`, `out_of_range`, `nonbinary_keys` | integrity | action-vector sanity |
+| `scored` | both | false if the run is under 48 frames |
+| `score` | quality | the combined rank-normalised ranking value, 0–1 |
+| `control` | quality | how much the player acts: camera magnitude + key-change rate |
+| `agreement` | quality | \|corr\| between the steering signal and horizontal optical flow |
+| `best_lag`, `lag_r` | quality | lag maximising that correlation, and the signed r there |
+| `smooth` | quality | lag-1 autocorrelation of frame difference; low means jittery timing |
+| `motion` | quality | mean optical-flow magnitude; excludes AFK stretches |
+| `detail` | quality | mean Laplacian variance; excludes featureless sky and cave walls |
+| `clean` | quality | 1 − near-duplicate-frame rate |
+`score` weights these as `(2·control + 2·agreement + 1.5·length + smooth + motion + detail + clean) / 9.5`,
+each rank-normalised across runs first so no raw scale dominates. Every component ships, so you can
+re-weight for your own priorities instead of accepting ours.
 ## Layout
 The `.tar` files are **not a WebDataset**. Each holds many run directories: