{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QMIHRDGNB3F7HIFODELNLM2VJR","short_pith_number":"pith:QMIHRDGN","schema_version":"1.0","canonical_sha256":"8310788ccd0ecbf3a0ae1916d5b3554c6a61af2e9dc9bfbff33e334dc8fe0485","source":{"kind":"arxiv","id":"2505.06271","version":1},"attestation_state":"computed","paper":{"title":"Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SD"],"primary_cat":"cs.LG","authors_text":"Daehwan Hwang, June-Woo Kim, Kyunghoon Kim, Miika Toikkanen, Sanghoon Lee","submitted_at":"2025-05-06T09:25:15Z","abstract_excerpt":"Auscultation remains a cornerstone of clinical practice, essential for both initial evaluation and continuous monitoring. Clinicians listen to the lung sounds and make a diagnosis by combining the patient's medical history and test results. Given this strong association, multitask learning (MTL) can offer a compelling framework to simultaneously model these relationships, integrating respiratory sound patterns with disease manifestations. While MTL has shown considerable promise in medical applications, a significant research gap remains in understanding the complex interplay between respirato"},"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":"2505.06271","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-06T09:25:15Z","cross_cats_sorted":["cs.AI","cs.SD"],"title_canon_sha256":"00c4ebdbdb2376c7a9961015d84ccdca1395cec381e0a580f3f6c86da4d458ca","abstract_canon_sha256":"75957344eb4c1809782957194df2b6e8a3c4ac0e58d2a08c56cc2ff52c201fde"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:06.390724Z","signature_b64":"lGNByzUeFVui8A6SrxK7iVyGH9BFdw/obDVfWI+b4KDOpKIezOu0p8XwRe54MHwCXTjekU8z/HI/eLw/kOOqAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8310788ccd0ecbf3a0ae1916d5b3554c6a61af2e9dc9bfbff33e334dc8fe0485","last_reissued_at":"2026-07-05T11:01:06.390229Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:06.390229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tri-MTL: A Triple Multitask Learning Approach for Respiratory Disease Diagnosis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SD"],"primary_cat":"cs.LG","authors_text":"Daehwan Hwang, June-Woo Kim, Kyunghoon Kim, Miika Toikkanen, Sanghoon Lee","submitted_at":"2025-05-06T09:25:15Z","abstract_excerpt":"Auscultation remains a cornerstone of clinical practice, essential for both initial evaluation and continuous monitoring. Clinicians listen to the lung sounds and make a diagnosis by combining the patient's medical history and test results. Given this strong association, multitask learning (MTL) can offer a compelling framework to simultaneously model these relationships, integrating respiratory sound patterns with disease manifestations. While MTL has shown considerable promise in medical applications, a significant research gap remains in understanding the complex interplay between respirato"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.06271","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/2505.06271/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":"2505.06271","created_at":"2026-07-05T11:01:06.390286+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.06271v1","created_at":"2026-07-05T11:01:06.390286+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.06271","created_at":"2026-07-05T11:01:06.390286+00:00"},{"alias_kind":"pith_short_12","alias_value":"QMIHRDGNB3F7","created_at":"2026-07-05T11:01:06.390286+00:00"},{"alias_kind":"pith_short_16","alias_value":"QMIHRDGNB3F7HIFO","created_at":"2026-07-05T11:01:06.390286+00:00"},{"alias_kind":"pith_short_8","alias_value":"QMIHRDGN","created_at":"2026-07-05T11:01:06.390286+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11915","citing_title":"Quality Adaptive Angular Margin Learning for Respiratory Sound Classification","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QMIHRDGNB3F7HIFODELNLM2VJR","json":"https://pith.science/pith/QMIHRDGNB3F7HIFODELNLM2VJR.json","graph_json":"https://pith.science/api/pith-number/QMIHRDGNB3F7HIFODELNLM2VJR/graph.json","events_json":"https://pith.science/api/pith-number/QMIHRDGNB3F7HIFODELNLM2VJR/events.json","paper":"https://pith.science/paper/QMIHRDGN"},"agent_actions":{"view_html":"https://pith.science/pith/QMIHRDGNB3F7HIFODELNLM2VJR","download_json":"https://pith.science/pith/QMIHRDGNB3F7HIFODELNLM2VJR.json","view_paper":"https://pith.science/paper/QMIHRDGN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.06271&json=true","fetch_graph":"https://pith.science/api/pith-number/QMIHRDGNB3F7HIFODELNLM2VJR/graph.json","fetch_events":"https://pith.science/api/pith-number/QMIHRDGNB3F7HIFODELNLM2VJR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QMIHRDGNB3F7HIFODELNLM2VJR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QMIHRDGNB3F7HIFODELNLM2VJR/action/storage_attestation","attest_author":"https://pith.science/pith/QMIHRDGNB3F7HIFODELNLM2VJR/action/author_attestation","sign_citation":"https://pith.science/pith/QMIHRDGNB3F7HIFODELNLM2VJR/action/citation_signature","submit_replication":"https://pith.science/pith/QMIHRDGNB3F7HIFODELNLM2VJR/action/replication_record"}},"created_at":"2026-07-05T11:01:06.390286+00:00","updated_at":"2026-07-05T11:01:06.390286+00:00"}