Document Stockfish zero-depth WDL configuration

README.md

@@ -6,6 +6,7 @@ language:
 tags:
 - chess
 - leela-chess-zero
+- stockfish
 - chess-game
 - parquet
 - fen
@@ -19,6 +20,12 @@ configs:
     path: data/source_split=train/**/*.parquet
   - split: test
     path: data/source_split=test/**/*.parquet
+- config_name: stockfish_zero_wdl
+  data_files:
+  - split: train
+    path: stockfish-zero-wdl/source_split=train/**/*.parquet
+  - split: test
+    path: stockfish-zero-wdl/source_split=test/**/*.parquet
 ---
 
 # Zero Evaluator High-Variance Chess Positions
@@ -32,6 +39,12 @@ The selection deliberately balances opening, middlegame, and endgame coverage
 and retains the original source train/test split. The complete variance audit
 is in `variance-report.json` and `RESULTS.md`.
 
+Two configurations are available:
+
+- `default`: the original unlabeled positions;
+- `stockfish_zero_wdl`: the same positions with immediate static Stockfish
+  NNUE WDL labels and per-row engine provenance.
+
 ## Data layout
 
 The release consists of six Zstandard-compressed Parquet files partitioned by:
@@ -48,6 +61,10 @@ The release consists of six Zstandard-compressed Parquet files partitioned by:
 `data/_manifest.json` contains file sizes, row counts, ID bounds, and SHA-256
 checksums.
 
+The labeled derivative mirrors the same six partitions under
+`stockfish-zero-wdl/`. Its `_manifest.json` records the source Hub revision,
+engine identity, binary checksum, row counts, file sizes, and checksums.
+
 ## Load the dataset
 
 Hugging Face Datasets:
@@ -59,6 +76,19 @@ positions = load_dataset("Pawitt/zero-evaluator")
 print(positions["train"][0]["fen"])
 ```
 
+Load the Stockfish-labeled configuration with:
+
+```python
+from datasets import load_dataset
+
+positions = load_dataset(
+    "Pawitt/zero-evaluator",
+    "stockfish_zero_wdl",
+)
+row = positions["train"][0]
+print(row["fen"], row["wdl_win"], row["wdl_draw"], row["wdl_loss"])
+```
+
 For Hive partition columns and streaming Arrow batches, use PyArrow directly:
 
 ```python
@@ -88,8 +118,41 @@ side to move, piece and material statistics, legal-move count, check state,
 castling mask, and halfmove clock. See `FORMAT.md` for exact semantics.
 
 The source-game `result` is provenance metadata. It is **not** an lc0
-depth-zero WDL label. Static WDL values can be added by running the positions
-through an lc0 zero-search evaluation pass.
+depth-zero WDL label.
+
+The `stockfish_zero_wdl` configuration adds:
+
+| Column | Type | Meaning |
+| --- | --- | --- |
+| `wdl_win` | `uint16` | Static win probability on a 0–1000 scale |
+| `wdl_draw` | `uint16` | Static draw probability on a 0–1000 scale |
+| `wdl_loss` | `uint16` | Static loss probability on a 0–1000 scale |
+| `wdl_engine_name` | string | Evaluating engine family |
+| `wdl_engine_version` | string | Exact engine build identity |
+| `wdl_engine_weights` | string | NNUE network identity |
+
+WDL is from the perspective of the side to move and always sums to 1000.
+
+## Stockfish zero-depth labeling
+
+The labeled configuration was produced with a patched Stockfish command,
+`go depth 0`. It performs one immediate NNUE evaluation without tree search,
+then applies Stockfish's calibrated WDL conversion. This is a static evaluator
+label, not a searched game-theoretic result or a native three-output neural
+head.
+
+Provenance:
+
+- engine: `Stockfish dev-20260822-d95a3013-zero-wdl`;
+- NNUE: `nn-1a298aa575a0.nnue`;
+- engine binary SHA-256:
+  `57ca9adcf657338ac907b3c0c667f1f705885c06865603134f23c6e6e402682d`;
+- source dataset revision:
+  `7e7d453311882b4b8686aeb885e1c2eb9e2911f9`;
+- terminal adjudication: `python-chess outcome(claim_draw=True)`.
+
+Across all rows, the mean WDL is `186.773 / 578.242 / 234.985`. Stockfish's
+static calibration is draw-heavy: the median draw value is `875/1000`.
 
 ## Validation
 
@@ -98,6 +161,13 @@ through an lc0 zero-search evaluation pass.
 - All 26 row groups use Zstandard compression.
 - All six file checksums match the manifest.
 - 6,000 sampled FEN records were reconstructed successfully with python-chess.
+- The Stockfish derivative contains exactly 1,509,201 rows in the same six
+  partitions.
+- Every labeled partition matches its manifest row count, byte size, and
+  SHA-256 checksum.
+- Every labeled row has WDL values summing to 1000.
+- All labeled files carry `zero_wdl_complete=true` and consistent engine
+  provenance.
 
 ## Source