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  "Description": "Implements the split-fit-evaluate-assess workflow from\nHastie, Tibshirani, and Friedman (2009, ISBN:978-0-387-84857-0)\n\"The Elements of Statistical Learning\", Chapter 7. Provides\nthree-way data splitting with automatic stratification,\nmandatory seeds for reproducibility, automatic data type\nhandling, and 10 algorithms out of the box. Uses 'Rust' backend\nfor cross-language deterministic splitting. Designed for\ntabular supervised learning with minimal ceremony. Polyglot\nparity with the 'Python' 'mlw' package on 'PyPI'.",
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    "message": "papers: wire Zenodo DOIs, flatten data, fix Dockerfile\n\n- Grammar refs.bib: landscape DOI 10.5281/zenodo.19406148\n- Landscape refs.bib: grammar DOI 10.5281/zenodo.19406355\n- Recompiled both PDFs with DOI cross-references\n- Flattened data/v3/ → data/ (verify scripts updated)\n- Removed grammar Dockerfile (no experiments to run)\n- Landscape Dockerfile: experiment runner only\n",
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