{"as_of":"2026-08-08T15:01:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ff698636d622a92a7571b0b33458079df98a90ba9554fc400b3354791b9b3840","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T13:31:09.017810Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T13:31:11.482438Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.12438","last_updated":"2024-01-23T02:14:05Z","snapshot_observed_at":"2026-07-06T17:19:08.136109Z","submitted_at":"2024-01-23T02:14:05Z","title":"Secure Federated Learning Approaches to Diagnosing COVID-19","version":1},"cited_work":{"arxiv_id":"2401.12438","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.12438","snapshot_observed_at":"2026-08-07T13:31:11.482438Z","title":"Secure Federated Learning Approaches to Diagnosing COVID-19","venue":"eess.IV","work_id":"10fb1aa2-dbd3-41ac-85a8-895f40387ea1","year":2024},"citing_paper":{"arxiv_id":"2505.21715","last_updated":"2025-05-27T20:01:12Z","snapshot_observed_at":"2026-08-07T13:22:13.302968Z","submitted_at":"2025-05-27T20:01:12Z","title":"Privacy-Preserving Chest X-ray Report Generation via Multimodal Federated Learning with ViT and GPT-2","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T13:31:09.017810Z"},"links":{"cited_paper":"/paper/2401.12438","citing_paper":"/paper/2505.21715"},"observation_digest":"sha256:ccc6d85ea2ebeaf36d025d4c5103af89b3eeee8e37982621955f3f12fae1e3da","observation_id":"17e88905-3c0a-4df4-adc5-c764111a433c","resolution":{"observed_at":"2026-08-07T13:31:11.560777Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2401.12438/citation-record","integrity":"/paper/2401.12438/integrity","json":"/paper/2401.12438/citation-record.json","paper":"/paper/2401.12438"},"outbound":[],"paper":{"arxiv_id":"2401.12438","last_updated":"2024-01-23T02:14:05Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-07-06T17:19:08.136109Z","submitted_at":"2024-01-23T02:14:05Z","title":"Secure Federated Learning Approaches to Diagnosing COVID-19"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2401.12438."}