{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:KWOZLJ2VM72OTX2IKNGCMDNAFZ","short_pith_number":"pith:KWOZLJ2V","schema_version":"1.0","canonical_sha256":"559d95a75567f4e9df48534c260da02e42a461bdfa8d34d046c6fcd9d16cef60","source":{"kind":"arxiv","id":"2108.00853","version":2},"attestation_state":"computed","paper":{"title":"Sea Ice Forecasting using Attention-based Ensemble LSTM","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"physics.ao-ph","authors_text":"Jianwu Wang, Sahara Ali, Xin Huang, YiYi Huang","submitted_at":"2021-07-27T21:37:29Z","abstract_excerpt":"Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables f"},"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":"2108.00853","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2021-07-27T21:37:29Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"59a5608babf7ecf3fd3fa414b2385270ede88c9e79767a625b960bedd196ff7d","abstract_canon_sha256":"3656e57e40aa38089f2a9081bb83468c6be307396e74bce79c3e956d7ba55305"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:54:58.950084Z","signature_b64":"LNwhXAvrWHM0XK+hzb2oj3WovZ2x8zyjDysuYzIGOhpXXFjJ5fWbke6vOo7rkjay6zT0Kpiq+fSdhlTaSKhvDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"559d95a75567f4e9df48534c260da02e42a461bdfa8d34d046c6fcd9d16cef60","last_reissued_at":"2026-07-05T03:54:58.949502Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:54:58.949502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sea Ice Forecasting using Attention-based Ensemble LSTM","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"physics.ao-ph","authors_text":"Jianwu Wang, Sahara Ali, Xin Huang, YiYi Huang","submitted_at":"2021-07-27T21:37:29Z","abstract_excerpt":"Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.00853","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/2108.00853/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":"2108.00853","created_at":"2026-07-05T03:54:58.949565+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.00853v2","created_at":"2026-07-05T03:54:58.949565+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.00853","created_at":"2026-07-05T03:54:58.949565+00:00"},{"alias_kind":"pith_short_12","alias_value":"KWOZLJ2VM72O","created_at":"2026-07-05T03:54:58.949565+00:00"},{"alias_kind":"pith_short_16","alias_value":"KWOZLJ2VM72OTX2I","created_at":"2026-07-05T03:54:58.949565+00:00"},{"alias_kind":"pith_short_8","alias_value":"KWOZLJ2V","created_at":"2026-07-05T03:54:58.949565+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/KWOZLJ2VM72OTX2IKNGCMDNAFZ","json":"https://pith.science/pith/KWOZLJ2VM72OTX2IKNGCMDNAFZ.json","graph_json":"https://pith.science/api/pith-number/KWOZLJ2VM72OTX2IKNGCMDNAFZ/graph.json","events_json":"https://pith.science/api/pith-number/KWOZLJ2VM72OTX2IKNGCMDNAFZ/events.json","paper":"https://pith.science/paper/KWOZLJ2V"},"agent_actions":{"view_html":"https://pith.science/pith/KWOZLJ2VM72OTX2IKNGCMDNAFZ","download_json":"https://pith.science/pith/KWOZLJ2VM72OTX2IKNGCMDNAFZ.json","view_paper":"https://pith.science/paper/KWOZLJ2V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.00853&json=true","fetch_graph":"https://pith.science/api/pith-number/KWOZLJ2VM72OTX2IKNGCMDNAFZ/graph.json","fetch_events":"https://pith.science/api/pith-number/KWOZLJ2VM72OTX2IKNGCMDNAFZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KWOZLJ2VM72OTX2IKNGCMDNAFZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KWOZLJ2VM72OTX2IKNGCMDNAFZ/action/storage_attestation","attest_author":"https://pith.science/pith/KWOZLJ2VM72OTX2IKNGCMDNAFZ/action/author_attestation","sign_citation":"https://pith.science/pith/KWOZLJ2VM72OTX2IKNGCMDNAFZ/action/citation_signature","submit_replication":"https://pith.science/pith/KWOZLJ2VM72OTX2IKNGCMDNAFZ/action/replication_record"}},"created_at":"2026-07-05T03:54:58.949565+00:00","updated_at":"2026-07-05T03:54:58.949565+00:00"}