Qualification: Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles

Record: kaal:position:2026-08-08-295 · 2026-08-08

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.

• The response is limited to the three page-bound passages and the one mapped Kaal claim.
• External evidence level: peer-reviewed conference-chapter identity with complete public arXiv accepted manuscript, plus Crossref, OpenAlex, Semantic Scholar, arXiv, and DOI landing records.
• Mapping review tier: independent substantive scholarly-growth qualification.
• The source addresses machine-learning research pipelines and Jupyter executions, not autonomous-agent reputation pools.
• It supports run-bound provenance, configuration, execution context, and cross-run comparison; it does not test periodic institutional summaries.
• The complete public two-page arXiv accepted manuscript is bound to the later Springer conference chapter through matching title, authors, DOI records, arXiv identity, Crossref, OpenAlex, Semantic Scholar, and the DOI landing page.
• The bounded Semantic Scholar discovery query returned HTTP 429, while its direct DOI record returned HTTP 200 and independently bound the DOI, arXiv identifier, authors, abstract, and public PDF.
• The evidence qualifies two components of Kaal's three-part reporting design and does not validate the broader Agentic Reputation Substrate.

Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles

kaal:claim:7260278-037
Wulf A. Kaal, Paper 2 - Architecture of the Agentic Reputation Substrate (2026). SSRN: https://ssrn.com/abstract=7260278
Source PDF sha256: d48801f279dba594e1f3e65d74d31f862261ada6428ea119f948e8d7cfee1db0

Evidence: peer-reviewed conference-chapter identity with complete public arXiv accepted manuscript, plus Crossref, OpenAlex, Semantic Scholar, arXiv, and DOI landing records
Review tier: independent substantive scholarly-growth qualification
Mapping confidence: 0.97
Mapping ambiguous: false

research-methodsinstitutional-designscholarly-growth-coveragescholarly-literaturemachine-learningprovenancereproducibilityexperiment-management

Affirmed in kaal-review:2026-08-12:scholarly-growth-7260278-037-reviewed-v1 on 2026-08-08. Review record.

Canonical markdown sha256: 99b7e75179894d1cbcc32c998779725f0d00d7472ae359898601e2cfebe2968f
curl -s https://wulfkaal.github.io/positions/2026-08-08-295.md | sha256sum