{"schema":"https://pith.science/schemas/pith-integrity/v1.json","pith_number":"2605.01810","arxiv_id":"2605.01810","integrity":{"available":true,"endpoint":"/pith/2605.01810/integrity.json","summary":{"critical":3,"advisory":1,"informational":0,"by_detector":{"doi_compliance":{"total":4,"critical":3,"advisory":1,"informational":0}}},"clean":false,"detectors_run":[{"name":"ai_meta_artifact","version":"1.0.0","status":"completed","ran_at":"2026-05-20T17:35:46.438678Z","findings_count":0},{"name":"doi_title_agreement","version":"1.0.0","status":"completed","ran_at":"2026-05-20T05:01:22.623274Z","findings_count":0},{"name":"doi_compliance","version":"1.0.0","status":"completed","ran_at":"2026-05-19T16:57:59.720599Z","findings_count":4}],"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/JBHI.2026.3001234.26) was visible in the surrounding text but could not be confirmed against doi.org as printed.","detected_doi":"10.1109/JBHI.2026.3001234.26","detected_arxiv_id":null,"ref_index":18,"audited_at":"2026-05-19T16:57:59.720599Z"},{"detector":"doi_compliance","finding_type":"unresolvable_identifier","severity":"critical","verdict_class":"cross_source","note":"Identifier '10.1186/s12916-024-03101-9' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","detected_doi":"10.1186/s12916-024-03101-9","detected_arxiv_id":null,"ref_index":16,"audited_at":"2026-05-19T16:57:59.720599Z"},{"detector":"doi_compliance","finding_type":"unresolvable_identifier","severity":"critical","verdict_class":"cross_source","note":"Identifier '10.1016/j.media.2025.102989' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","detected_doi":"10.1016/j.media.2025.102989","detected_arxiv_id":null,"ref_index":30,"audited_at":"2026-05-19T16:57:59.720599Z"},{"detector":"doi_compliance","finding_type":"unresolvable_identifier","severity":"critical","verdict_class":"cross_source","note":"Identifier '10.1109/tnnls.2025.3001234' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","detected_doi":"10.1109/tnnls.2025.3001234","detected_arxiv_id":null,"ref_index":31,"audited_at":"2026-05-19T16:57:59.720599Z"}],"snapshot_sha256":"7ed6fda4949155a8af502a3f2e4da2358add4caa2c80d38926ab2743556cb04e"},"events":[{"event_id":2180,"event_type":"pith.integrity.v1","payload_sha256":"41051e32b38897f07041a15214f328317cc7ae00e42c10fe0bb728aee84d4b32","signature_b64":"hQgHSS/6P+blHD0ka1CnBqU7YfY7CWJSI27M3bieYE5DRy2+F7Vqp5Js1G4sWTpI/21D4NgimUSK+R4yB5pmCw==","signing_key_id":"pith-v1-2026-05","created_at":"2026-05-19T17:02:15.069306+00:00","payload":{"note":"Identifier '10.1109/tnnls.2025.3001234' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"W. Zhang, et al., Class-conditional weighting for federated semi- supervised learning, IEEE Transactions on Neural Networks and Learn- ing Systems 36 (2025) 1–12.doi:10.1109/TNNLS.2025.3001234","arxiv_id":"2605.01810","detector":"doi_compliance","evidence":{"doi":"10.1109/tnnls.2025.3001234","arxiv_id":null,"ref_index":31,"raw_excerpt":"W. Zhang, et al., Class-conditional weighting for federated semi- supervised learning, IEEE Transactions on Neural Networks and Learn- ing Systems 36 (2025) 1–12.doi:10.1109/TNNLS.2025.3001234","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":31,"audited_at":"2026-05-19T16:57:59.720599Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/tnnls.2025.3001234","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"ed8a1b4cab715848430ca59532b882a2f0067fa0d925012a9d415658138d7883","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null}},{"event_id":2179,"event_type":"pith.integrity.v1","payload_sha256":"de292535f33b41b7eb66a83da7a5c4ed28620948237c4177143241193c1f13a8","signature_b64":"PAZ8zT4BalfvMmorosn+lSIVcGHBpeFq5f9HUjMDYQomonL3S73Zg4naymt1u8dgAXOzQDvufHFLZHjscCVPCw==","signing_key_id":"pith-v1-2026-05","created_at":"2026-05-19T17:02:15.068135+00:00","payload":{"note":"Identifier '10.1016/j.media.2025.102989' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"Y. Wang, et al., Federated prototypical learning for medical image segmentation under label scarcity, Medical Image Analysis 91 (2025) 102989.doi:10.1016/j.media.2025.102989","arxiv_id":"2605.01810","detector":"doi_compliance","evidence":{"doi":"10.1016/j.media.2025.102989","arxiv_id":null,"ref_index":30,"raw_excerpt":"Y. Wang, et al., Federated prototypical learning for medical image segmentation under label scarcity, Medical Image Analysis 91 (2025) 102989.doi:10.1016/j.media.2025.102989","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":30,"audited_at":"2026-05-19T16:57:59.720599Z","event_type":"pith.integrity.v1","detected_doi":"10.1016/j.media.2025.102989","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"d94336080f2abb832597ed2d71b41e242e69162bca0978d7b372dcde2bff80ec","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null}},{"event_id":2178,"event_type":"pith.integrity.v1","payload_sha256":"b63d234a0a1b6dc86ce6e2115c93b658031729a003e67515803d6b019f03c581","signature_b64":"iuXb8SiWZOnmyNcuulprUufttA2fC0sSYtrfRFANi+LoRa5/QnNau2hsxmRhiee5FcYwdk34DQzrho50Szo/AA==","signing_key_id":"pith-v1-2026-05","created_at":"2026-05-19T17:02:15.066959+00:00","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/JBHI.2026.3001234.26) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"J. Chen, L. Wang, et al., FedEnTrust: Federated ensemble learning with trustworthy aggregation for clinical prediction, IEEE Journal of Biomedical and Health Informatics 30 (2026) 1–12.doi:10.1109/JBHI. 2026.3001234. 26","arxiv_id":"2605.01810","detector":"doi_compliance","evidence":{"ref_index":18,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"J. Chen, L. Wang, et al., FedEnTrust: Federated ensemble learning with trustworthy aggregation for clinical prediction, IEEE Journal of Biomedical and Health Informatics 30 (2026) 1–12.doi:10.1109/JBHI. 2026.3001234. 26","reconstructed_doi":"10.1109/JBHI.2026.3001234.26"},"severity":"advisory","ref_index":18,"audited_at":"2026-05-19T16:57:59.720599Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/JBHI.2026.3001234.26","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"f8f073aee050d8951c69ce27e579d4e4d5349dd5569319bc0e6a60745b1c0dc2","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null}},{"event_id":2177,"event_type":"pith.integrity.v1","payload_sha256":"761a6302d1d9d2739d70d21cf27419f27af17921f2845eca79650b85aa8cf116","signature_b64":"wMUv13XNltRPl/zgaaTIuX/K0ISFZWgXd6xN9R/YkcAQuL9C68jXGX6dZQuNXISsZnvZ3ZzS0ymjfE9E+uwiAQ==","signing_key_id":"pith-v1-2026-05","created_at":"2026-05-19T17:02:15.065737+00:00","payload":{"note":"Identifier '10.1186/s12916-024-03101-9' is syntactically valid but the DOI registry (doi.org) returned 404, and Crossref / OpenAlex / internal corpus also have no record. The cited work could not be located through any authoritative source.","snippet":"GDM Prediction Consortium, Machine learning for gestational dia- betes mellitus prediction: A systematic review and meta-analysis, BMC Medicine 22 (2024) 45.doi:10.1186/s12916-024-03101-9","arxiv_id":"2605.01810","detector":"doi_compliance","evidence":{"doi":"10.1186/s12916-024-03101-9","arxiv_id":null,"ref_index":16,"raw_excerpt":"GDM Prediction Consortium, Machine learning for gestational dia- betes mellitus prediction: A systematic review and meta-analysis, BMC Medicine 22 (2024) 45.doi:10.1186/s12916-024-03101-9","verdict_class":"cross_source","checked_sources":["crossref_by_doi","openalex_by_doi","doi_org_head"]},"severity":"critical","ref_index":16,"audited_at":"2026-05-19T16:57:59.720599Z","event_type":"pith.integrity.v1","detected_doi":"10.1186/s12916-024-03101-9","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"unresolvable_identifier","evidence_hash":"f0a921ccaab2b550e427bb81ccf8e113dfd879f03eb901ea5aa801c9df7edad9","paper_version":1,"verdict_class":"cross_source","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null}}],"endpoint_self":"/pith/2605.01810/integrity.json","protocol_url":"https://pith.science/pith-integrity-protocol"}