{"as_of":"2026-08-09T20:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:aba2a1332663b16de2c0d2ce3e310a02b5beb6351d737215cc66588ac6fea561","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:26:54.504697Z","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":"2026-08-06T21:51:48.368271Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2109.02543","last_updated":"2021-09-06T15:29:15Z","snapshot_observed_at":"2026-08-09T13:11:53.170431Z","submitted_at":"2021-09-06T15:29:15Z","title":"Generation of Synthetic Electronic Health Records Using a Federated GAN","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2109.02543","snapshot_observed_at":"2026-08-06T23:26:54.504697Z","title":"Generation of syn- thetic electronic health records using a federated GAN","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.18007","last_updated":"2025-06-22T12:09:55Z","snapshot_observed_at":"2026-08-07T04:05:04.207286Z","submitted_at":"2025-06-22T12:09:55Z","title":"Imputation of Longitudinal Data Using GANs: Challenges and Implications for Classification","version":1},"reference_index":204,"source":"pdf_text","source_observed_at":"2026-08-06T23:26:54.504697Z"},"links":{"cited_paper":"/paper/2109.02543","citing_paper":"/paper/2506.18007"},"observation_digest":"sha256:c646856673f1a68f4acb38d538cbf661168eee2b830c3a417f863bde5a711c38","observation_id":"152f2821-00bc-4caa-8064-c245631bc551","resolution":{"observed_at":"2026-08-06T23:26:54.504697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2109.02543","last_updated":"2021-09-06T15:29:15Z","snapshot_observed_at":"2026-08-09T13:11:53.170431Z","submitted_at":"2021-09-06T15:29:15Z","title":"Generation of Synthetic Electronic Health Records Using a Federated GAN","version":1},"cited_work":{"arxiv_id":"2109.02543","doi":null,"metadata_source":"pith","pith_arxiv_id":"2109.02543","snapshot_observed_at":"2026-08-06T21:51:48.368271Z","title":"Generation of Synthetic Electronic Health Records Using a Federated GAN","venue":"cs.LG","work_id":"2c868e42-6e12-4d5d-81ae-460d12713ad7","year":2021},"citing_paper":{"arxiv_id":"2506.23358","last_updated":"2025-06-29T18:29:59Z","snapshot_observed_at":"2026-08-09T03:19:54.664488Z","submitted_at":"2025-06-29T18:29:59Z","title":"Federated Timeline Synthesis: Scalable and Private Methodology For Model Training and Deployment","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T21:51:47.035424Z"},"links":{"cited_paper":"/paper/2109.02543","citing_paper":"/paper/2506.23358"},"observation_digest":"sha256:1063b623caed7fc893e3703ae8aba26eb7fca661556a57682a5b4ed6d8a077ce","observation_id":"d2439cf4-27e2-402b-b489-bb2f912e05d3","resolution":{"observed_at":"2026-08-06T21:51:48.438541Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2109.02543/citation-record","integrity":"/paper/2109.02543/integrity","json":"/paper/2109.02543/citation-record.json","paper":"/paper/2109.02543"},"outbound":[],"paper":{"arxiv_id":"2109.02543","last_updated":"2021-09-06T15:29:15Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-09T13:11:53.170431Z","submitted_at":"2021-09-06T15:29:15Z","title":"Generation of Synthetic Electronic Health Records Using a Federated GAN"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2109.02543."}