{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QHMOOM62G6MSCNGPAQMJ4IWPXM","short_pith_number":"pith:QHMOOM62","schema_version":"1.0","canonical_sha256":"81d8e733da37992134cf04189e22cfbb1240d3877b7572aeb11d1f9612b73a42","source":{"kind":"arxiv","id":"2502.15772","version":2},"attestation_state":"computed","paper":{"title":"Rashomon perspective for measuring uncertainty in the survival predictive maintenance models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.AP","authors_text":"Mustafa Cavus, Yigitcan Yardimci","submitted_at":"2025-02-16T13:36:56Z","abstract_excerpt":"The prediction of the Remaining Useful Life of aircraft engines is a critical area in high-reliability sectors such as aerospace and defense. Early failure predictions help ensure operational continuity, reduce maintenance costs, and prevent unexpected failures. Traditional regression models struggle with censored data, which can lead to biased predictions. Survival models, on the other hand, effectively handle censored data, improving predictive accuracy in maintenance processes. This paper introduces a novel approach based on the Rashomon perspective, which considers multiple models that ach"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2502.15772","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.AP","submitted_at":"2025-02-16T13:36:56Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c685aa59cda4af3107849d39031750fcb98454bad633b2f49b64031dafa33f8d","abstract_canon_sha256":"328a9c63070c4d1d38b200d87601ad73cfb9e0ebc47938bfd9e0e7ec1e4d6db3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:54:44.477963Z","signature_b64":"AVQoLfZvMauhVaoy6Vu6LI/Rn81MZ+y7q3p+b8dBrUpDp+F9jdZPEaYweAg/uGBoJd6BFsObkSPWVD4jtaWYBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81d8e733da37992134cf04189e22cfbb1240d3877b7572aeb11d1f9612b73a42","last_reissued_at":"2026-07-05T11:54:44.477493Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:54:44.477493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rashomon perspective for measuring uncertainty in the survival predictive maintenance models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.AP","authors_text":"Mustafa Cavus, Yigitcan Yardimci","submitted_at":"2025-02-16T13:36:56Z","abstract_excerpt":"The prediction of the Remaining Useful Life of aircraft engines is a critical area in high-reliability sectors such as aerospace and defense. Early failure predictions help ensure operational continuity, reduce maintenance costs, and prevent unexpected failures. Traditional regression models struggle with censored data, which can lead to biased predictions. Survival models, on the other hand, effectively handle censored data, improving predictive accuracy in maintenance processes. This paper introduces a novel approach based on the Rashomon perspective, which considers multiple models that ach"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.15772","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.15772/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2502.15772","created_at":"2026-07-05T11:54:44.477555+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.15772v2","created_at":"2026-07-05T11:54:44.477555+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.15772","created_at":"2026-07-05T11:54:44.477555+00:00"},{"alias_kind":"pith_short_12","alias_value":"QHMOOM62G6MS","created_at":"2026-07-05T11:54:44.477555+00:00"},{"alias_kind":"pith_short_16","alias_value":"QHMOOM62G6MSCNGP","created_at":"2026-07-05T11:54:44.477555+00:00"},{"alias_kind":"pith_short_8","alias_value":"QHMOOM62","created_at":"2026-07-05T11:54:44.477555+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2504.12156","citing_title":"Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications","ref_index":44,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QHMOOM62G6MSCNGPAQMJ4IWPXM","json":"https://pith.science/pith/QHMOOM62G6MSCNGPAQMJ4IWPXM.json","graph_json":"https://pith.science/api/pith-number/QHMOOM62G6MSCNGPAQMJ4IWPXM/graph.json","events_json":"https://pith.science/api/pith-number/QHMOOM62G6MSCNGPAQMJ4IWPXM/events.json","paper":"https://pith.science/paper/QHMOOM62"},"agent_actions":{"view_html":"https://pith.science/pith/QHMOOM62G6MSCNGPAQMJ4IWPXM","download_json":"https://pith.science/pith/QHMOOM62G6MSCNGPAQMJ4IWPXM.json","view_paper":"https://pith.science/paper/QHMOOM62","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.15772&json=true","fetch_graph":"https://pith.science/api/pith-number/QHMOOM62G6MSCNGPAQMJ4IWPXM/graph.json","fetch_events":"https://pith.science/api/pith-number/QHMOOM62G6MSCNGPAQMJ4IWPXM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QHMOOM62G6MSCNGPAQMJ4IWPXM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QHMOOM62G6MSCNGPAQMJ4IWPXM/action/storage_attestation","attest_author":"https://pith.science/pith/QHMOOM62G6MSCNGPAQMJ4IWPXM/action/author_attestation","sign_citation":"https://pith.science/pith/QHMOOM62G6MSCNGPAQMJ4IWPXM/action/citation_signature","submit_replication":"https://pith.science/pith/QHMOOM62G6MSCNGPAQMJ4IWPXM/action/replication_record"}},"created_at":"2026-07-05T11:54:44.477555+00:00","updated_at":"2026-07-05T11:54:44.477555+00:00"}