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Integrity report for Operationalizing Assurance Cases for Data Scientists: A Showcase of Concepts and Tooling in the Context of Test Data Quality for Machine Learning

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2312.04917 · pith:2023:EEBNR6WNKDDTTTUEOXKBFE2Q7A

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Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/EEBNR6WN/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.