{"as_of":"2026-08-23T14:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:719ad6012b6d7ebdbeead12a40a082fc28d4effa423dc4373ff459921358a4e5","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":"2026-08-23T06:30:58.430688+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":"2026-08-04T13:15:32.178662Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2107.00018","last_updated":"2022-02-16T17:53:04Z","snapshot_observed_at":"2026-08-18T22:21:08.949220Z","submitted_at":"2021-06-30T18:00:02Z","title":"Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.00018","snapshot_observed_at":"2026-08-04T13:15:32.178662Z","title":"Prelogović, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2510.01121","last_updated":"2026-06-03T17:40:08Z","snapshot_observed_at":"2026-08-13T02:37:27.272236Z","submitted_at":"2025-10-01T17:07:37Z","title":"CosmoUiT: A Vision Transformer-UNet Hybrid for Fast and Accurate Emulation of 21-cm Maps from the Epoch of Reionization","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-04T13:15:32.178662Z"},"links":{"cited_paper":"/paper/2107.00018","citing_paper":"/paper/2510.01121"},"observation_digest":"sha256:656d9efdd91ea8f1a5f4da4ad51fa311549e47e971312d869ecdeb13daff370f","observation_id":"48bddef6-4c8b-4d7f-b6f7-b237744c6f34","resolution":{"observed_at":"2026-08-04T13:15:32.178662Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.00018","last_updated":"2022-02-16T17:53:04Z","snapshot_observed_at":"2026-08-18T22:21:08.949220Z","submitted_at":"2021-06-30T18:00:02Z","title":"Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.00018","snapshot_observed_at":"2026-08-03T05:10:20.063699Z","title":"Prelogovi´ c, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2602.03313","last_updated":"2026-07-23T06:47:43Z","snapshot_observed_at":"2026-08-18T02:28:12.109554Z","submitted_at":"2026-02-03T09:42:28Z","title":"Seeing Wiggles without Seeing Wiggles: BAO Recovery in 21 cm Intensity Mapping with Deep Learning","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T05:10:20.063699Z"},"links":{"cited_paper":"/paper/2107.00018","citing_paper":"/paper/2602.03313"},"observation_digest":"sha256:e51a85240cec17dd1dea1f4ecc38a51fa9a8cd8e5d713b6a9280a8c31a1fbff4","observation_id":"2ddd1770-3fec-4c31-8834-689be6034608","resolution":{"observed_at":"2026-08-03T05:10:20.063699Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2107.00018/citation-record","integrity":"/paper/2107.00018/integrity","json":"/paper/2107.00018/citation-record.json","paper":"/paper/2107.00018"},"outbound":[],"paper":{"arxiv_id":"2107.00018","last_updated":"2022-02-16T17:53:04Z","latest_version":2,"primary_category":"astro-ph.CO","snapshot_observed_at":"2026-08-18T22:21:08.949220Z","submitted_at":"2021-06-30T18:00:02Z","title":"Machine learning astrophysics from 21 cm lightcones: impact of network architectures and signal contamination"},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2107.00018."}