{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LEHCP4BMLLG2RNB6PVZAXJR373","short_pith_number":"pith:LEHCP4BM","schema_version":"1.0","canonical_sha256":"590e27f02c5acda8b43e7d720ba63bfec61c72644594bfebf8fda40fb5aa0af8","source":{"kind":"arxiv","id":"2211.05267","version":1},"attestation_state":"computed","paper":{"title":"Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Deep Learning-Based Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Chen Lin, Donghai Liang, Elvis Kahoro, Eugene Agichtein, Jeremy Sarnat, Payam Karisani, Safoora Yousefi","submitted_at":"2022-11-09T23:56:35Z","abstract_excerpt":"Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks (ANNs). Most of the prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone, oxides of nitrogen, and PM2.5. Given that traditional, highly sophisticated air quality monitors are expensive and are not universally available, these models cannot adequately s"},"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":"2211.05267","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-11-09T23:56:35Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"feacbc9033730fe045afd513dd918cf9a5ecc05114343333d4377c1e1dcf4e44","abstract_canon_sha256":"66897123dec08abfc4fd50575367628a1f4d5d19f8e7bd5cfbc1949f575cb2b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:15:02.635665Z","signature_b64":"BuXXWSqKIqw8Uz7LNjo4BCX/RB4os5cloHzAixTmERpxN1hntixLnle29dz84X63TjWYHFmwsTivLD15MCv5Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"590e27f02c5acda8b43e7d720ba63bfec61c72644594bfebf8fda40fb5aa0af8","last_reissued_at":"2026-07-05T05:15:02.635189Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:15:02.635189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Deep Learning-Based Time Series Forecasting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.LG","authors_text":"Chen Lin, Donghai Liang, Elvis Kahoro, Eugene Agichtein, Jeremy Sarnat, Payam Karisani, Safoora Yousefi","submitted_at":"2022-11-09T23:56:35Z","abstract_excerpt":"Real-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks (ANNs). Most of the prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone, oxides of nitrogen, and PM2.5. Given that traditional, highly sophisticated air quality monitors are expensive and are not universally available, these models cannot adequately s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.05267","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/2211.05267/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":"2211.05267","created_at":"2026-07-05T05:15:02.635246+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.05267v1","created_at":"2026-07-05T05:15:02.635246+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.05267","created_at":"2026-07-05T05:15:02.635246+00:00"},{"alias_kind":"pith_short_12","alias_value":"LEHCP4BMLLG2","created_at":"2026-07-05T05:15:02.635246+00:00"},{"alias_kind":"pith_short_16","alias_value":"LEHCP4BMLLG2RNB6","created_at":"2026-07-05T05:15:02.635246+00:00"},{"alias_kind":"pith_short_8","alias_value":"LEHCP4BM","created_at":"2026-07-05T05:15:02.635246+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/LEHCP4BMLLG2RNB6PVZAXJR373","json":"https://pith.science/pith/LEHCP4BMLLG2RNB6PVZAXJR373.json","graph_json":"https://pith.science/api/pith-number/LEHCP4BMLLG2RNB6PVZAXJR373/graph.json","events_json":"https://pith.science/api/pith-number/LEHCP4BMLLG2RNB6PVZAXJR373/events.json","paper":"https://pith.science/paper/LEHCP4BM"},"agent_actions":{"view_html":"https://pith.science/pith/LEHCP4BMLLG2RNB6PVZAXJR373","download_json":"https://pith.science/pith/LEHCP4BMLLG2RNB6PVZAXJR373.json","view_paper":"https://pith.science/paper/LEHCP4BM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.05267&json=true","fetch_graph":"https://pith.science/api/pith-number/LEHCP4BMLLG2RNB6PVZAXJR373/graph.json","fetch_events":"https://pith.science/api/pith-number/LEHCP4BMLLG2RNB6PVZAXJR373/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LEHCP4BMLLG2RNB6PVZAXJR373/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LEHCP4BMLLG2RNB6PVZAXJR373/action/storage_attestation","attest_author":"https://pith.science/pith/LEHCP4BMLLG2RNB6PVZAXJR373/action/author_attestation","sign_citation":"https://pith.science/pith/LEHCP4BMLLG2RNB6PVZAXJR373/action/citation_signature","submit_replication":"https://pith.science/pith/LEHCP4BMLLG2RNB6PVZAXJR373/action/replication_record"}},"created_at":"2026-07-05T05:15:02.635246+00:00","updated_at":"2026-07-05T05:15:02.635246+00:00"}