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 "text": "Samuel, Löffler, and König-Ries provide independent support for the run-level part of Kaal's reporting-layer design. Their study identifies code, data, parameters, package versions, preprocessing, and execution context as information needed to reproduce machine-learning experiments. ProvBook captures, stores, describes, and compares provenance across Jupyter executions. It also binds each model run to its execution environment. This supports preserving run evidence and controls and comparing results across runs. The comparison is bounded. The source concerns machine-learning research pipelines, not autonomous-agent reputation pools, and it does not test periodic institutional summaries. It therefore qualifies the run-level and cross-run provenance components of Kaal's three-part reporting design rather than validating the complete architecture.",
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