Goldilocks CSLR quantile random forest for k-index prediction model

A quantile random forest that predicts which mesh a periodic material needs from Goldilocks Core's ordered ladder of k-point meshes. It returns a rung on that ladder, not a k-point spacing: rung 1 is the Gamma-only (1, 1, 1) mesh, indices are 1-based, and the ladder was enumerated down to a minimum k-distance of 0.03 inverse angstrom rather than a fixed count per axis.

Trained on PSDI record 52713-55d86: the same 17757 MC3D structures as the superseded record d5ds2-64f16, relabelled under the corrected floor-based ladder — not a uniform shift, since the 1-based enumeration also drops a small number of repeated meshes that the old per-axis count did not. Convergence is judged on total energy alone, to within 1 meV/atom across three consecutive rungs; no force criterion is applied. Split 70/10/10/10 and grouped by reduced composition so that polymorphs of one composition cannot straddle the split. Inputs are 174 composition, structure, direct-lattice and reciprocal-lattice descriptors, matching the extractor Goldilocks Core already uses. No SOAP and no learned metallicity representation.

The record contains the fitted forest, its separately recorded calibration, and model.json, which was written by the run that fitted the model and records the feature contract, all 174 column names, the target contract, the decision rule and the rule that chose it, the hyperparameter search, and the digests.