{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6LO7NGWDKX2M7BJVZTXUOY4WWN","short_pith_number":"pith:6LO7NGWD","schema_version":"1.0","canonical_sha256":"f2ddf69ac355f4cf8535ccef476396b3736702dd479fbefa0d5a3cc513bdf452","source":{"kind":"arxiv","id":"2411.16790","version":1},"attestation_state":"computed","paper":{"title":"Learning Predictive Checklists with Probabilistic Logic Programming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Edward De Brouwer, Rahul G. Krishnan, Yukti Makhija","submitted_at":"2024-11-25T09:07:19Z","abstract_excerpt":"Checklists have been widely recognized as effective tools for completing complex tasks in a systematic manner. Although originally intended for use in procedural tasks, their interpretability and ease of use have led to their adoption for predictive tasks as well, including in clinical settings. However, designing checklists can be challenging, often requiring expert knowledge and manual rule design based on available data. Recent work has attempted to address this issue by using machine learning to automatically generate predictive checklists from data, although these approaches have been lim"},"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":"2411.16790","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-25T09:07:19Z","cross_cats_sorted":[],"title_canon_sha256":"a6d88a7a0a3c85920a20fc67bb0400016e6f28644ae26b1d925e27acfb936fc1","abstract_canon_sha256":"8619af1bc75e0b48b8d51c27f7029673adfbbc6894054a85e214b6d22908bfda"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:40:23.142091Z","signature_b64":"ZaCWTwaGfARKMcLa8LS7l/oENQOWQbPpeuES7P0s+NdeLIfhS/nyCwQWvofHmLerYkOJa7DYrzGyOgU7Lta2Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2ddf69ac355f4cf8535ccef476396b3736702dd479fbefa0d5a3cc513bdf452","last_reissued_at":"2026-07-05T09:40:23.141642Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:40:23.141642Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Predictive Checklists with Probabilistic Logic Programming","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Edward De Brouwer, Rahul G. Krishnan, Yukti Makhija","submitted_at":"2024-11-25T09:07:19Z","abstract_excerpt":"Checklists have been widely recognized as effective tools for completing complex tasks in a systematic manner. Although originally intended for use in procedural tasks, their interpretability and ease of use have led to their adoption for predictive tasks as well, including in clinical settings. However, designing checklists can be challenging, often requiring expert knowledge and manual rule design based on available data. Recent work has attempted to address this issue by using machine learning to automatically generate predictive checklists from data, although these approaches have been lim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.16790","kind":"arxiv","version":1},"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/2411.16790/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":"2411.16790","created_at":"2026-07-05T09:40:23.141700+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.16790v1","created_at":"2026-07-05T09:40:23.141700+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.16790","created_at":"2026-07-05T09:40:23.141700+00:00"},{"alias_kind":"pith_short_12","alias_value":"6LO7NGWDKX2M","created_at":"2026-07-05T09:40:23.141700+00:00"},{"alias_kind":"pith_short_16","alias_value":"6LO7NGWDKX2M7BJV","created_at":"2026-07-05T09:40:23.141700+00:00"},{"alias_kind":"pith_short_8","alias_value":"6LO7NGWD","created_at":"2026-07-05T09:40:23.141700+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6LO7NGWDKX2M7BJVZTXUOY4WWN","json":"https://pith.science/pith/6LO7NGWDKX2M7BJVZTXUOY4WWN.json","graph_json":"https://pith.science/api/pith-number/6LO7NGWDKX2M7BJVZTXUOY4WWN/graph.json","events_json":"https://pith.science/api/pith-number/6LO7NGWDKX2M7BJVZTXUOY4WWN/events.json","paper":"https://pith.science/paper/6LO7NGWD"},"agent_actions":{"view_html":"https://pith.science/pith/6LO7NGWDKX2M7BJVZTXUOY4WWN","download_json":"https://pith.science/pith/6LO7NGWDKX2M7BJVZTXUOY4WWN.json","view_paper":"https://pith.science/paper/6LO7NGWD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.16790&json=true","fetch_graph":"https://pith.science/api/pith-number/6LO7NGWDKX2M7BJVZTXUOY4WWN/graph.json","fetch_events":"https://pith.science/api/pith-number/6LO7NGWDKX2M7BJVZTXUOY4WWN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6LO7NGWDKX2M7BJVZTXUOY4WWN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6LO7NGWDKX2M7BJVZTXUOY4WWN/action/storage_attestation","attest_author":"https://pith.science/pith/6LO7NGWDKX2M7BJVZTXUOY4WWN/action/author_attestation","sign_citation":"https://pith.science/pith/6LO7NGWDKX2M7BJVZTXUOY4WWN/action/citation_signature","submit_replication":"https://pith.science/pith/6LO7NGWDKX2M7BJVZTXUOY4WWN/action/replication_record"}},"created_at":"2026-07-05T09:40:23.141700+00:00","updated_at":"2026-07-05T09:40:23.141700+00:00"}