{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:OLFKFG2IXE7CW4UML5DRUQO6XA","short_pith_number":"pith:OLFKFG2I","schema_version":"1.0","canonical_sha256":"72caa29b48b93e2b728c5f471a41deb808c985ef2296daa2c3391dea1b3b16fb","source":{"kind":"arxiv","id":"2001.11957","version":1},"attestation_state":"computed","paper":{"title":"Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Jacqueline CK Lam, Qi Zhang, Victor OK Li, Yang Han","submitted_at":"2020-01-29T14:05:51Z","abstract_excerpt":"Poor air quality has become an increasingly critical challenge for many metropolitan cities, which carries many catastrophicphysical and mental consequences on human health and quality of life. However, accurately monitoring and forecasting air qualityremains a highly challenging endeavour. Limited by geographically sparse data, traditional statistical models and newly emergingdata-driven methods of air quality forecasting mainly focused on the temporal correlation between the historical temporal datasets of airpollutants. However, in reality, both distribution and dispersion of air pollutants"},"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":"2001.11957","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SP","submitted_at":"2020-01-29T14:05:51Z","cross_cats_sorted":[],"title_canon_sha256":"cb30ab59f0ce31320bc8d4f3145812b393dd0adea0eea978897d72696e404643","abstract_canon_sha256":"4aeaa554c431c48f6470f6575b8cb678a6846de21ac329bc2f7f24afe78c2384"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:37:35.316409Z","signature_b64":"Ai+2jBcCkngXZWBnKoS4m+rqN5bS0d3u+Pi0ahsb17x035YE/WdHDA4lfv7tooWY8G59WgrcPaIqPba+Xj35CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72caa29b48b93e2b728c5f471a41deb808c985ef2296daa2c3391dea1b3b16fb","last_reissued_at":"2026-07-05T00:37:35.315998Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:37:35.315998Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep-AIR: A Hybrid CNN-LSTM Framework forFine-Grained Air Pollution Forecast","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Jacqueline CK Lam, Qi Zhang, Victor OK Li, Yang Han","submitted_at":"2020-01-29T14:05:51Z","abstract_excerpt":"Poor air quality has become an increasingly critical challenge for many metropolitan cities, which carries many catastrophicphysical and mental consequences on human health and quality of life. However, accurately monitoring and forecasting air qualityremains a highly challenging endeavour. Limited by geographically sparse data, traditional statistical models and newly emergingdata-driven methods of air quality forecasting mainly focused on the temporal correlation between the historical temporal datasets of airpollutants. However, in reality, both distribution and dispersion of air pollutants"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.11957","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/2001.11957/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":"2001.11957","created_at":"2026-07-05T00:37:35.316069+00:00"},{"alias_kind":"arxiv_version","alias_value":"2001.11957v1","created_at":"2026-07-05T00:37:35.316069+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.11957","created_at":"2026-07-05T00:37:35.316069+00:00"},{"alias_kind":"pith_short_12","alias_value":"OLFKFG2IXE7C","created_at":"2026-07-05T00:37:35.316069+00:00"},{"alias_kind":"pith_short_16","alias_value":"OLFKFG2IXE7CW4UM","created_at":"2026-07-05T00:37:35.316069+00:00"},{"alias_kind":"pith_short_8","alias_value":"OLFKFG2I","created_at":"2026-07-05T00:37:35.316069+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/OLFKFG2IXE7CW4UML5DRUQO6XA","json":"https://pith.science/pith/OLFKFG2IXE7CW4UML5DRUQO6XA.json","graph_json":"https://pith.science/api/pith-number/OLFKFG2IXE7CW4UML5DRUQO6XA/graph.json","events_json":"https://pith.science/api/pith-number/OLFKFG2IXE7CW4UML5DRUQO6XA/events.json","paper":"https://pith.science/paper/OLFKFG2I"},"agent_actions":{"view_html":"https://pith.science/pith/OLFKFG2IXE7CW4UML5DRUQO6XA","download_json":"https://pith.science/pith/OLFKFG2IXE7CW4UML5DRUQO6XA.json","view_paper":"https://pith.science/paper/OLFKFG2I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2001.11957&json=true","fetch_graph":"https://pith.science/api/pith-number/OLFKFG2IXE7CW4UML5DRUQO6XA/graph.json","fetch_events":"https://pith.science/api/pith-number/OLFKFG2IXE7CW4UML5DRUQO6XA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OLFKFG2IXE7CW4UML5DRUQO6XA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OLFKFG2IXE7CW4UML5DRUQO6XA/action/storage_attestation","attest_author":"https://pith.science/pith/OLFKFG2IXE7CW4UML5DRUQO6XA/action/author_attestation","sign_citation":"https://pith.science/pith/OLFKFG2IXE7CW4UML5DRUQO6XA/action/citation_signature","submit_replication":"https://pith.science/pith/OLFKFG2IXE7CW4UML5DRUQO6XA/action/replication_record"}},"created_at":"2026-07-05T00:37:35.316069+00:00","updated_at":"2026-07-05T00:37:35.316069+00:00"}