{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OREPAVQGOOYNHWBTRZ4FKR4JXT","short_pith_number":"pith:OREPAVQG","schema_version":"1.0","canonical_sha256":"7448f0560673b0d3d8338e78554789bcf735a6e63717bd91048e4632a4b5a2f2","source":{"kind":"arxiv","id":"2407.20073","version":2},"attestation_state":"computed","paper":{"title":"Domain Adaptation Optimized for Robustness in Mixture Populations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Keyao Zhan, Molei Liu, Tianxi Cai, Xin Xiong, Zijian Guo","submitted_at":"2024-07-29T14:59:09Z","abstract_excerpt":"While domain adaptation methods address data shifts, most assume target populations align with at least one source population, neglecting mixtures that combine sources influenced by factors like demographics. Additional challenges in electronic health record (EHR)-based studies include unobserved outcomes and the need to explain population mixtures using broader clinical characteristics than those in standard risk models. To address these challenges under shifts in both covariate distributions and outcome models, we propose a novel framework: Domain Adaptation Optimized for Robustness in Mixtu"},"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":"2407.20073","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2024-07-29T14:59:09Z","cross_cats_sorted":[],"title_canon_sha256":"a9547c496008ad00d4250d618be3c79679dbf52a15c46ff9ce1c3f2ee9b4ce14","abstract_canon_sha256":"5ffa7c0b1789d8880b169df6b59add000423a2557bdd05ad3dd7c81e55e60c9d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:56:33.738072Z","signature_b64":"P1NcTD/5DIeTRf/0ViT7pKlnunu+k084kcpetKIkeT3d2hqAPVOtqmqwPO3NP6M7mDU3/RaclFVsRezi2miEBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7448f0560673b0d3d8338e78554789bcf735a6e63717bd91048e4632a4b5a2f2","last_reissued_at":"2026-07-05T10:56:33.737575Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:56:33.737575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Domain Adaptation Optimized for Robustness in Mixture Populations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Keyao Zhan, Molei Liu, Tianxi Cai, Xin Xiong, Zijian Guo","submitted_at":"2024-07-29T14:59:09Z","abstract_excerpt":"While domain adaptation methods address data shifts, most assume target populations align with at least one source population, neglecting mixtures that combine sources influenced by factors like demographics. Additional challenges in electronic health record (EHR)-based studies include unobserved outcomes and the need to explain population mixtures using broader clinical characteristics than those in standard risk models. To address these challenges under shifts in both covariate distributions and outcome models, we propose a novel framework: Domain Adaptation Optimized for Robustness in Mixtu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.20073","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/2407.20073/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":"2407.20073","created_at":"2026-07-05T10:56:33.737633+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.20073v2","created_at":"2026-07-05T10:56:33.737633+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.20073","created_at":"2026-07-05T10:56:33.737633+00:00"},{"alias_kind":"pith_short_12","alias_value":"OREPAVQGOOYN","created_at":"2026-07-05T10:56:33.737633+00:00"},{"alias_kind":"pith_short_16","alias_value":"OREPAVQGOOYNHWBT","created_at":"2026-07-05T10:56:33.737633+00:00"},{"alias_kind":"pith_short_8","alias_value":"OREPAVQG","created_at":"2026-07-05T10:56:33.737633+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/OREPAVQGOOYNHWBTRZ4FKR4JXT","json":"https://pith.science/pith/OREPAVQGOOYNHWBTRZ4FKR4JXT.json","graph_json":"https://pith.science/api/pith-number/OREPAVQGOOYNHWBTRZ4FKR4JXT/graph.json","events_json":"https://pith.science/api/pith-number/OREPAVQGOOYNHWBTRZ4FKR4JXT/events.json","paper":"https://pith.science/paper/OREPAVQG"},"agent_actions":{"view_html":"https://pith.science/pith/OREPAVQGOOYNHWBTRZ4FKR4JXT","download_json":"https://pith.science/pith/OREPAVQGOOYNHWBTRZ4FKR4JXT.json","view_paper":"https://pith.science/paper/OREPAVQG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.20073&json=true","fetch_graph":"https://pith.science/api/pith-number/OREPAVQGOOYNHWBTRZ4FKR4JXT/graph.json","fetch_events":"https://pith.science/api/pith-number/OREPAVQGOOYNHWBTRZ4FKR4JXT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OREPAVQGOOYNHWBTRZ4FKR4JXT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OREPAVQGOOYNHWBTRZ4FKR4JXT/action/storage_attestation","attest_author":"https://pith.science/pith/OREPAVQGOOYNHWBTRZ4FKR4JXT/action/author_attestation","sign_citation":"https://pith.science/pith/OREPAVQGOOYNHWBTRZ4FKR4JXT/action/citation_signature","submit_replication":"https://pith.science/pith/OREPAVQGOOYNHWBTRZ4FKR4JXT/action/replication_record"}},"created_at":"2026-07-05T10:56:33.737633+00:00","updated_at":"2026-07-05T10:56:33.737633+00:00"}