{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:GFBHOB5IB27HR6CZIERMLBU7SL","short_pith_number":"pith:GFBHOB5I","schema_version":"1.0","canonical_sha256":"31427707a80ebe78f8594122c5869f92ef37f2f7e5f1810d73b0ecdc6a820e50","source":{"kind":"arxiv","id":"2211.09967","version":1},"attestation_state":"computed","paper":{"title":"Learning on Health Fairness and Environmental Justice via Interactive Visualization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.HC","authors_text":"Abdullah-Al-Raihan Nayeem, Dongyun Han, Huikyo Lee, Ignacio Segovia-Dominguez, Isaac Cho, Yulia Gel, Yuzhou Chen, Zhiwei Zhen","submitted_at":"2022-11-18T01:21:29Z","abstract_excerpt":"This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioeconomic factors on COVID-19 clinical severity by employing multiple Recurrent Graph Neural Networks. We designed and implemented a visualization interface that leverages coordinated multi-views to support exploratory and predictive analysis of hospitalizations and other socio-geographic variables at multiple dimensions, simultaneously. By harnessing the strength of geometric deep learning, we build a consensus machin"},"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":"2211.09967","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2022-11-18T01:21:29Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"68e283f4cd784e8275653709485e66fbac920074c89f1ef8e8c622fbb1ee9fac","abstract_canon_sha256":"07f002b635c8b95e7d97b616bd824cf00a8aed599ff9042b78106f44da57f8df"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:17:16.358031Z","signature_b64":"qzZpWG17OhQyGqq+NXfWYA1ve2MWCnkO2Qjkildxm7XreSqDWgt+HUWYyv89Lnc0WPXlM+pTkkLSWCjfUiODBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"31427707a80ebe78f8594122c5869f92ef37f2f7e5f1810d73b0ecdc6a820e50","last_reissued_at":"2026-07-05T05:17:16.357623Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:17:16.357623Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning on Health Fairness and Environmental Justice via Interactive Visualization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.HC","authors_text":"Abdullah-Al-Raihan Nayeem, Dongyun Han, Huikyo Lee, Ignacio Segovia-Dominguez, Isaac Cho, Yulia Gel, Yuzhou Chen, Zhiwei Zhen","submitted_at":"2022-11-18T01:21:29Z","abstract_excerpt":"This paper introduces an interactive visualization interface with a machine learning consensus analysis that enables the researchers to explore the impact of atmospheric and socioeconomic factors on COVID-19 clinical severity by employing multiple Recurrent Graph Neural Networks. We designed and implemented a visualization interface that leverages coordinated multi-views to support exploratory and predictive analysis of hospitalizations and other socio-geographic variables at multiple dimensions, simultaneously. By harnessing the strength of geometric deep learning, we build a consensus machin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.09967","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/2211.09967/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":"2211.09967","created_at":"2026-07-05T05:17:16.357681+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.09967v1","created_at":"2026-07-05T05:17:16.357681+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.09967","created_at":"2026-07-05T05:17:16.357681+00:00"},{"alias_kind":"pith_short_12","alias_value":"GFBHOB5IB27H","created_at":"2026-07-05T05:17:16.357681+00:00"},{"alias_kind":"pith_short_16","alias_value":"GFBHOB5IB27HR6CZ","created_at":"2026-07-05T05:17:16.357681+00:00"},{"alias_kind":"pith_short_8","alias_value":"GFBHOB5I","created_at":"2026-07-05T05:17:16.357681+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/GFBHOB5IB27HR6CZIERMLBU7SL","json":"https://pith.science/pith/GFBHOB5IB27HR6CZIERMLBU7SL.json","graph_json":"https://pith.science/api/pith-number/GFBHOB5IB27HR6CZIERMLBU7SL/graph.json","events_json":"https://pith.science/api/pith-number/GFBHOB5IB27HR6CZIERMLBU7SL/events.json","paper":"https://pith.science/paper/GFBHOB5I"},"agent_actions":{"view_html":"https://pith.science/pith/GFBHOB5IB27HR6CZIERMLBU7SL","download_json":"https://pith.science/pith/GFBHOB5IB27HR6CZIERMLBU7SL.json","view_paper":"https://pith.science/paper/GFBHOB5I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.09967&json=true","fetch_graph":"https://pith.science/api/pith-number/GFBHOB5IB27HR6CZIERMLBU7SL/graph.json","fetch_events":"https://pith.science/api/pith-number/GFBHOB5IB27HR6CZIERMLBU7SL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GFBHOB5IB27HR6CZIERMLBU7SL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GFBHOB5IB27HR6CZIERMLBU7SL/action/storage_attestation","attest_author":"https://pith.science/pith/GFBHOB5IB27HR6CZIERMLBU7SL/action/author_attestation","sign_citation":"https://pith.science/pith/GFBHOB5IB27HR6CZIERMLBU7SL/action/citation_signature","submit_replication":"https://pith.science/pith/GFBHOB5IB27HR6CZIERMLBU7SL/action/replication_record"}},"created_at":"2026-07-05T05:17:16.357681+00:00","updated_at":"2026-07-05T05:17:16.357681+00:00"}