{"schema":"https://pith.science/schemas/pith-integrity/v1.json","pith_number":"2604.26673","arxiv_id":"2604.26673","integrity":{"available":true,"endpoint":"/pith/2604.26673/integrity.json","summary":{"critical":0,"advisory":1,"informational":0,"by_detector":{"doi_compliance":{"total":1,"critical":0,"advisory":1,"informational":0}}},"clean":false,"detectors_run":[{"name":"ai_meta_artifact","version":"1.0.0","status":"completed","ran_at":"2026-05-20T23:44:52.719626Z","findings_count":0},{"name":"doi_compliance","version":"1.0.0","status":"completed","ran_at":"2026-05-19T19:56:08.365236Z","findings_count":1}],"findings":[{"detector":"doi_compliance","finding_type":"recoverable_identifier","severity":"advisory","verdict_class":"incontrovertible","note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1016/j.patrec.2024.09.011.V) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detected_doi":"10.1016/j.patrec.2024.09.011.V","detected_arxiv_id":null,"ref_index":4,"audited_at":"2026-05-19T19:56:08.365236Z"}],"snapshot_sha256":"f1f300198a079b935eb59a01424410b2e30611c753d64c15f9e22c0a475eb9c5"},"events":[{"event_id":2797,"event_type":"pith.integrity.v1","payload_sha256":"0895d2a385ae5ca4d43b70b014626b0d92ed7c7c7e9d0d952869fcb5f114359d","signature_b64":"HiupEKHp7gZD64lzJuHcJRJ6q5veI+crLGlB9oXWPd3tGjJuOot7a9j6nhh6mVj+uksRKvPzGb8XXlNQzVk+DA==","signing_key_id":"pith-v1-2026-05","created_at":"2026-05-19T19:57:20.547333+00:00","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1016/j.patrec.2024.09.011.V) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"ISSN 0167-8655. doi: https://doi.org/10.1016/j. patrec.2024.09.011. V olodymyr Kuleshov, Nathan Fenner, and Stefano Ermon. Accurate uncertainties for deep learning using calibrated regression. In Jennifer Dy and Andreas Krause, editors, Pro","arxiv_id":"2604.26673","detector":"doi_compliance","evidence":{"ref_index":4,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"ISSN 0167-8655. doi: https://doi.org/10.1016/j. patrec.2024.09.011. V olodymyr Kuleshov, Nathan Fenner, and Stefano Ermon. Accurate uncertainties for deep learning using calibrated regression. In Jennifer Dy and Andreas Krause, editors, Pro","reconstructed_doi":"10.1016/j.patrec.2024.09.011.V"},"severity":"advisory","ref_index":4,"audited_at":"2026-05-19T19:56:08.365236Z","event_type":"pith.integrity.v1","detected_doi":"10.1016/j.patrec.2024.09.011.V","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"c510a03ac6b85d47bffc6e878c8f966d2abc3aa5077a0ea1200ede30fb7f986a","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null}}],"endpoint_self":"/pith/2604.26673/integrity.json","protocol_url":"https://pith.science/pith-integrity-protocol"}