PROVENANCE.md

Provenance

The paper PDF and source archive were downloaded from arXiv. The authors’ repository and public preprocessed datasets were pinned by commit and SHA-256. The reproduction imports the authors’ public module as a dependency check but independently restates the convex v2 SGShift and SGShift-A objectives so they can use a deterministic CLARABEL/SCS failover and equal source/target domain weights. Knockoff construction uses knockpy with explicit seeds.

The public-data simulations follow the authors’ generator/base structure and fixed sparse feature sets. The controlled complex suite uses the known source link as an oracle offset to isolate recovery of the target correction; that scope distinction is explicit in outputs and the logbook. Baseline attribution uses exact linear/tree SHAP when supported and a declared bounded permutation response fallback for kernel SVMs.

The complete six-seed p=500 stress scope was rerun at n_source=n_target=1500 after the earlier n=1000 scope exposed one high-variance destructive-control cell. This disclosed strengthening changes p/n from 0.5 to 1/3 while preserving every seed, the generator, the feature dimension, and all locked science thresholds. The no-source-offset control reuses the correct arm's target-loss-CV penalty, so removing the offset is its only causal intervention.

Poster copy and chart values are deterministically composed from frozen CSV and JSON outputs. No desktop screenshots, synthetic chart numbers, or hidden manual edits are used.