{"as_of":"2026-08-19T08:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0dfb06ed8cf1848c9d03d2d98892dad8f690c5fba9edf567743b78a13d61f9dd","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":"2026-08-19T06:32:44.657259+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":"2026-08-16T00:45:53.059987Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-10T05:30:23.456663Z","state":"measured"}],"external_citation_measurements":[{"count":1,"observed_at":"2026-08-10T05:30:23.456663Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2306.08106","last_updated":"2023-06-13T19:50:36Z","snapshot_observed_at":"2026-08-18T19:46:14.234926Z","submitted_at":"2023-06-13T19:50:36Z","title":"Applications of Deep Learning to physics workflows","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.08106","snapshot_observed_at":"2026-08-16T00:45:53.059987Z","title":"2023 [arXiv:2306.08106]","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.02773","last_updated":"2025-05-05T16:36:46Z","snapshot_observed_at":"2026-08-18T19:45:02.249803Z","submitted_at":"2025-05-05T16:36:46Z","title":"Can Transformers help us perform parameter estimation of overlapping signals in gravitational wave detectors?","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T00:45:53.059987Z"},"links":{"cited_paper":"/paper/2306.08106","citing_paper":"/paper/2505.02773"},"observation_digest":"sha256:1c6f68a9e6b74cafb1b288883420117e4559bb943f00e0aad1fcb85812444366","observation_id":"cd3618d7-3bf3-4b41-b85e-a16027e879f0","resolution":{"observed_at":"2026-08-16T00:45:53.059987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.08106","last_updated":"2023-06-13T19:50:36Z","snapshot_observed_at":"2026-08-18T19:46:14.234926Z","submitted_at":"2023-06-13T19:50:36Z","title":"Applications of Deep Learning to physics workflows","version":1},"cited_work":{"arxiv_id":"2306.08106","doi":"10.48550/arxiv.2306.08106","metadata_source":"pith","pith_arxiv_id":"2306.08106","snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Applications of Deep Learning to physics workflows","venue":"hep-ex","work_id":"ebbace92-17f2-4984-a6d3-fd21c8123888","year":2023},"citing_paper":{"arxiv_id":"2507.03093","last_updated":"2025-07-03T18:05:40Z","snapshot_observed_at":"2026-08-14T20:43:14.334607Z","submitted_at":"2025-07-03T18:05:40Z","title":"From stellar light to astrophysical insight: automating variable star research with machine learning","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-06T20:22:25.560085Z"},"links":{"cited_paper":"/paper/2306.08106","citing_paper":"/paper/2507.03093"},"observation_digest":"sha256:28100a89b919d833653cdf1af075901d1613993937abd70e52343c752352b953","observation_id":"cfdb863a-78dc-4854-8f39-130e1e7197c9","resolution":{"observed_at":"2026-08-06T20:22:42.955918Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2306.08106/citation-record","integrity":"/paper/2306.08106/integrity","json":"/paper/2306.08106/citation-record.json","paper":"/paper/2306.08106"},"outbound":[],"paper":{"arxiv_id":"2306.08106","last_updated":"2023-06-13T19:50:36Z","latest_version":1,"primary_category":"hep-ex","snapshot_observed_at":"2026-08-18T19:46:14.234926Z","submitted_at":"2023-06-13T19:50:36Z","title":"Applications of Deep Learning to physics workflows"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.08106."}