{"schema":"https://pith.science/schemas/pith-integrity/v1.json","pith_number":"2607.20046","arxiv_id":"2607.20046","integrity":{"available":true,"endpoint":"/pith/2607.20046/integrity.json","summary":{"critical":0,"advisory":2,"informational":0,"by_detector":{"doi_compliance":{"total":2,"critical":0,"advisory":2,"informational":0}}},"clean":false,"detectors_run":[{"name":"ai_meta_artifact","version":"1.0.0","status":"skipped","ran_at":"2026-08-06T22:10:22.339932Z","findings_count":0},{"name":"doi_title_agreement","version":"1.0.0","status":"completed","ran_at":"2026-08-03T14:08:44.891920Z","findings_count":0},{"name":"doi_compliance","version":"1.1.0","status":"completed","ran_at":"2026-08-03T11:30:30.671939Z","findings_count":2},{"name":"claim_evidence","version":"1.0.0","status":"completed","ran_at":"2026-07-29T20:11:12.546150Z","findings_count":0},{"name":"citation_quote_validity","version":"0.1.0","status":"skipped","ran_at":"2026-07-23T09:57:28.330163Z","findings_count":0},{"name":"cited_work_retraction","version":"1.0.0","status":"completed","ran_at":"2026-07-23T07:52:40.129053Z","findings_count":0}],"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 (10.1109/TSE.2024.3350019) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detected_doi":"10.1109/TSE.2024.3350019","detected_arxiv_id":null,"ref_index":8,"audited_at":"2026-08-01T11:09:35.625974Z"},{"detector":"doi_compliance","finding_type":"recoverable_identifier","severity":"advisory","verdict_class":"incontrovertible","note":"DOI is split by whitespace or line breaks in the printed bibliography. Reconstructed DOI 10.1073/pnas.2015509117 resolves to 'Prevalence of neural collapse during the terminal phase of deep learning training'. A reader following the printed text alone cannot reach it.","detected_doi":"10.1073/pnas.2015509117","detected_arxiv_id":null,"ref_index":42,"audited_at":"2026-08-01T11:09:35.625974Z"}],"snapshot_sha256":"849b35f15734c2bbc8e30fe22f950d5d5b101c31b7101632961fc01b490e25dd"},"events":[{"event_id":15682,"event_type":"pith.integrity.v1","payload_sha256":"3f3bf1289fbb5a8877650a01fb294441a770c9d392f1d48d1eddc33f083daddc","signature_b64":"YfRCyHrCPmOhJM2XW1kEKZEey7tamFM2sv3ifM3oK6bOU8aPktnkWLICQjBmpG2+Puy2LlMlo4zWwAFF5OaFBw==","signing_key_id":"pith-v1-2026-05","created_at":"2026-08-01T11:13:33.888104+00:00","payload":{"note":"DOI is split by whitespace or line breaks in the printed bibliography. Reconstructed DOI 10.1073/pnas.2015509117 resolves to 'Prevalence of neural collapse during the terminal phase of deep learning training'. A reader following the printed text alone cannot reach it.","snippet":"Vardan Papyan, XY Han, and David L Donoho. 2020. Prevalence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences117, 40 (2020), 24652–24663. doi:10.1073/pnas. 2015509117","arxiv_id":"2607.20046","detector":"doi_compliance","evidence":{"ref_index":42,"verdict_class":"incontrovertible","resolved_title":"Prevalence of neural collapse during the terminal phase of deep learning training","printed_excerpt":"10.1073/pnas","reconstructed_doi":"10.1073/pnas.2015509117"},"severity":"advisory","ref_index":42,"audited_at":"2026-08-01T11:09:35.625974Z","event_type":"pith.integrity.v1","detected_doi":"10.1073/pnas.2015509117","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"a2f177c58828d1ee1c68c28dd083525bfa5ee70600db03a93513125b46446e9e","paper_version":1,"verdict_class":"incontrovertible","resolved_title":"Prevalence of neural collapse during the terminal phase of deep learning training","detector_version":"1.1.0","detected_arxiv_id":null}},{"event_id":15681,"event_type":"pith.integrity.v1","payload_sha256":"a41a71fc31f8379d1df7585cb8b82ad4ce4da98e3d6c2a7d7b0465ba2ceb5797","signature_b64":"rBU/QVD/YscnBR78YtTUPO3pMaIYwoMwzQIpGWpzyV9Jy2ByJk1RE/FaZAyqnQ0HYyrIyBHHJ6FkhoTwcs41Cw==","signing_key_id":"pith-v1-2026-05","created_at":"2026-08-01T11:13:33.550620+00:00","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/TSE.2024.3350019) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"Xueqi Dang, Yinghua Li, Mike Papadakis, Jacques Klein, Tegawendé F Bissyandé, and Yves Le Traon. 2024. Test input prioritization for machine learning classifiers.IEEE Transactions on Software Engineering(2024). doi:10.1109/TSE.2024. 3350019","arxiv_id":"2607.20046","detector":"doi_compliance","evidence":{"ref_index":8,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1109/tse.2024","reconstructed_doi":"10.1109/TSE.2024.3350019"},"severity":"advisory","ref_index":8,"audited_at":"2026-08-01T11:09:35.625974Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/TSE.2024.3350019","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"16f8f53c7a00a8887b6eab8e246de8a63e431dea4756b4dc37300e30b345ca73","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null}}],"endpoint_self":"/pith/2607.20046/integrity.json","protocol_url":"https://pith.science/pith-integrity-protocol"}