dataset page: install line

README.md

@@ -120,20 +120,12 @@ traj.aux         # (T′, P) float32, MPa
 traj.time        # (T′,) float64, s
 ```
 
-## Splits
-
-The benchmark's fixed split assignment, by case id (the file name without `.h5`). `train` fits the model and `val` selects it; every other split is held out for reporting — `test_interp` sits inside the training parameter range, `test_extrap` beyond it, `probe` cases are off-grid stress tests. Files shipped outside these splits (`held_aside` in the manifest) are not part of the protocol.
-
-- **train** (12): `W1D-200-1`, `W1D-200-2`, `W1D-200-4`, `W1D-200-8`, `W1D-300-1`, `W1D-300-8`, `W1D-400-1`, `W1D-400-8`, `W1D-500-1`, `W1D-500-2`, `W1D-500-4`, `W1D-500-8`
-- **val** (2): `W1D-300-2`, `W1D-400-4`
-- **test_interp** (2): `W1D-300-4`, `W1D-400-2`
-
 ## Benchmark protocol
 
 This archive backs the **Wave1D-Propagation** benchmark in StructBench. Task: autoregressive transition (ADR-0025); auxiliary target `axial_stress` (MPa); 6 input frames, horizon full, scored at native output times; quantities of interest: arrival_time_25, arrival_time_50, arrival_time_75, peak_stress. The full evaluation protocol and its rationale, the baseline recipes and checkpoints, and the current leaderboard live on the benchmark page in the code repository — <https://github.com/qilinli/StructBench/blob/main/docs/benchmarks/wave_propagation_1d.md> — so the numbers have a single home. To train a baseline on this archive:
 
 ```bash
-pip install git+https://github.com/qilinli/StructBench  # or: pip install -e . from a clone
+pip install git+https://github.com/qilinli/StructBench # or: pip install -e .
 structbench-train --mode train --config configs/wave_propagation_1d/cgn.toml \
     --data-root /path/to/this/folder --out runs/wave_propagation_1d-cgn
 ```