{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:4QQFYCLF2DA22SQRSLS3LZU62Q","short_pith_number":"pith:4QQFYCLF","schema_version":"1.0","canonical_sha256":"e4205c0965d0c1ad4a1192e5b5e69ed432f0cc8733145b1621bab4b4ce2f09c6","source":{"kind":"arxiv","id":"2608.01767","version":1},"attestation_state":"computed","paper":{"title":"Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dongqi Wang, Han Zhou, Weihua Zhou, Weiwei Chen","submitted_at":"2026-08-03T06:41:38Z","abstract_excerpt":"Ensuring food safety represents a critical public health challenge, particularly when inspection resources are limited and regional sampling data are sparse. This study proposes a Transformer-based framework capable of forecasting fine-grained, city-level food safety risks by unifying over 11 million inspection records with supplemental demographic, economic, and environmental indicators extracted from the Statistical Yearbook. A three-stage pretraining design leverages partial supervision from the Wilson interval (capturing both safety and risk rankings), together with semi-supervised label r"},"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":"2608.01767","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-08-03T06:41:38Z","cross_cats_sorted":[],"title_canon_sha256":"4bb80be4657103bfcabcc9fd9b0b677d5dc69b231a13e966dacf8da1a408282b","abstract_canon_sha256":"5fffda5235e521e3404c693e8010453ff5a94a630c6696caf293597dc5a93621"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-04T02:06:49.966409Z","signature_b64":"pEKAXbxGnjBeuIox3VlOf4SwfaLO8oVSmuunjzkwT46rmpY4mTQIoPzv8gjH24U0kPg5/wNdl0/oaSfNyIsnAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4205c0965d0c1ad4a1192e5b5e69ed432f0cc8733145b1621bab4b4ce2f09c6","last_reissued_at":"2026-08-04T02:06:49.964827Z","signature_status":"signed_v1","first_computed_at":"2026-08-04T02:06:49.964827Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leveraging AI for fine-grained food safety risk forecasting in sparse data conditions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Dongqi Wang, Han Zhou, Weihua Zhou, Weiwei Chen","submitted_at":"2026-08-03T06:41:38Z","abstract_excerpt":"Ensuring food safety represents a critical public health challenge, particularly when inspection resources are limited and regional sampling data are sparse. This study proposes a Transformer-based framework capable of forecasting fine-grained, city-level food safety risks by unifying over 11 million inspection records with supplemental demographic, economic, and environmental indicators extracted from the Statistical Yearbook. A three-stage pretraining design leverages partial supervision from the Wilson interval (capturing both safety and risk rankings), together with semi-supervised label r"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.01767","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/2608.01767/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":"2608.01767","created_at":"2026-08-04T02:06:49.966273+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.01767v1","created_at":"2026-08-04T02:06:49.966273+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.01767","created_at":"2026-08-04T02:06:49.966273+00:00"},{"alias_kind":"pith_short_12","alias_value":"4QQFYCLF2DA2","created_at":"2026-08-04T02:06:49.966273+00:00"},{"alias_kind":"pith_short_16","alias_value":"4QQFYCLF2DA22SQR","created_at":"2026-08-04T02:06:49.966273+00:00"},{"alias_kind":"pith_short_8","alias_value":"4QQFYCLF","created_at":"2026-08-04T02:06:49.966273+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/4QQFYCLF2DA22SQRSLS3LZU62Q","json":"https://pith.science/pith/4QQFYCLF2DA22SQRSLS3LZU62Q.json","graph_json":"https://pith.science/api/pith-number/4QQFYCLF2DA22SQRSLS3LZU62Q/graph.json","events_json":"https://pith.science/api/pith-number/4QQFYCLF2DA22SQRSLS3LZU62Q/events.json","paper":"https://pith.science/paper/4QQFYCLF"},"agent_actions":{"view_html":"https://pith.science/pith/4QQFYCLF2DA22SQRSLS3LZU62Q","download_json":"https://pith.science/pith/4QQFYCLF2DA22SQRSLS3LZU62Q.json","view_paper":"https://pith.science/paper/4QQFYCLF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.01767&json=true","fetch_graph":"https://pith.science/api/pith-number/4QQFYCLF2DA22SQRSLS3LZU62Q/graph.json","fetch_events":"https://pith.science/api/pith-number/4QQFYCLF2DA22SQRSLS3LZU62Q/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4QQFYCLF2DA22SQRSLS3LZU62Q/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4QQFYCLF2DA22SQRSLS3LZU62Q/action/storage_attestation","attest_author":"https://pith.science/pith/4QQFYCLF2DA22SQRSLS3LZU62Q/action/author_attestation","sign_citation":"https://pith.science/pith/4QQFYCLF2DA22SQRSLS3LZU62Q/action/citation_signature","submit_replication":"https://pith.science/pith/4QQFYCLF2DA22SQRSLS3LZU62Q/action/replication_record"}},"created_at":"2026-08-04T02:06:49.966273+00:00","updated_at":"2026-08-04T02:06:49.966273+00:00"}