{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZXLJMAETAQ7WGJUXK5PYQCTW6I","short_pith_number":"pith:ZXLJMAET","schema_version":"1.0","canonical_sha256":"cdd6960093043f632697575f880a76f20c5b73d5dbae3af00ffe6c67d0bae35d","source":{"kind":"arxiv","id":"2412.15532","version":1},"attestation_state":"computed","paper":{"title":"Improved Forecasts of Global Extreme Marine Heatwaves Through a Physics-guided Data-driven Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"physics.ao-ph","authors_text":"Fanghua Xu, Hao Wu, Ruijian Gou, Ruiqi Shu, Xiaomeng Huang, Yuan Gao","submitted_at":"2024-12-20T03:47:56Z","abstract_excerpt":"The unusually warm sea surface temperature events known as marine heatwaves (MHWs) have a profound impact on marine ecosystems. Accurate prediction of extreme MHWs has significant scientific and financial worth. However, existing methods still have certain limitations, especially in the most extreme MHWs. In this study, to address these issues, based on the physical nature of MHWs, we created a novel deep learning neural network that is capable of accurate 10-day MHW forecasting. Our framework significantly improves the forecast ability of extreme MHWs through two specially designed modules in"},"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":"2412.15532","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.ao-ph","submitted_at":"2024-12-20T03:47:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"984029c40bc0aa8d5f10f05780e43889300f50554485974c3c4442828301c260","abstract_canon_sha256":"fd1eb1915a3afa259241bc82116db1885f15fdcec5356b947b2cd470cd00af4c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:52:24.762193Z","signature_b64":"7vGPTF5DnMUeo0LuIRqhZB7h7ruFX2aJISV5+7mbycSDv0g5h97Pzr7+lRykPqR1Xsd7v2vd6Yf5DV66xr1xAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdd6960093043f632697575f880a76f20c5b73d5dbae3af00ffe6c67d0bae35d","last_reissued_at":"2026-07-05T09:52:24.761266Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:52:24.761266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improved Forecasts of Global Extreme Marine Heatwaves Through a Physics-guided Data-driven Approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"physics.ao-ph","authors_text":"Fanghua Xu, Hao Wu, Ruijian Gou, Ruiqi Shu, Xiaomeng Huang, Yuan Gao","submitted_at":"2024-12-20T03:47:56Z","abstract_excerpt":"The unusually warm sea surface temperature events known as marine heatwaves (MHWs) have a profound impact on marine ecosystems. Accurate prediction of extreme MHWs has significant scientific and financial worth. However, existing methods still have certain limitations, especially in the most extreme MHWs. In this study, to address these issues, based on the physical nature of MHWs, we created a novel deep learning neural network that is capable of accurate 10-day MHW forecasting. Our framework significantly improves the forecast ability of extreme MHWs through two specially designed modules in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.15532","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/2412.15532/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":"2412.15532","created_at":"2026-07-05T09:52:24.761654+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.15532v1","created_at":"2026-07-05T09:52:24.761654+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.15532","created_at":"2026-07-05T09:52:24.761654+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZXLJMAETAQ7W","created_at":"2026-07-05T09:52:24.761654+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZXLJMAETAQ7WGJUX","created_at":"2026-07-05T09:52:24.761654+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZXLJMAET","created_at":"2026-07-05T09:52:24.761654+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/ZXLJMAETAQ7WGJUXK5PYQCTW6I","json":"https://pith.science/pith/ZXLJMAETAQ7WGJUXK5PYQCTW6I.json","graph_json":"https://pith.science/api/pith-number/ZXLJMAETAQ7WGJUXK5PYQCTW6I/graph.json","events_json":"https://pith.science/api/pith-number/ZXLJMAETAQ7WGJUXK5PYQCTW6I/events.json","paper":"https://pith.science/paper/ZXLJMAET"},"agent_actions":{"view_html":"https://pith.science/pith/ZXLJMAETAQ7WGJUXK5PYQCTW6I","download_json":"https://pith.science/pith/ZXLJMAETAQ7WGJUXK5PYQCTW6I.json","view_paper":"https://pith.science/paper/ZXLJMAET","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.15532&json=true","fetch_graph":"https://pith.science/api/pith-number/ZXLJMAETAQ7WGJUXK5PYQCTW6I/graph.json","fetch_events":"https://pith.science/api/pith-number/ZXLJMAETAQ7WGJUXK5PYQCTW6I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZXLJMAETAQ7WGJUXK5PYQCTW6I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZXLJMAETAQ7WGJUXK5PYQCTW6I/action/storage_attestation","attest_author":"https://pith.science/pith/ZXLJMAETAQ7WGJUXK5PYQCTW6I/action/author_attestation","sign_citation":"https://pith.science/pith/ZXLJMAETAQ7WGJUXK5PYQCTW6I/action/citation_signature","submit_replication":"https://pith.science/pith/ZXLJMAETAQ7WGJUXK5PYQCTW6I/action/replication_record"}},"created_at":"2026-07-05T09:52:24.761654+00:00","updated_at":"2026-07-05T09:52:24.761654+00:00"}