{"as_of":"2026-08-11T21:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:04add7a61ce469fc31b28157665b4a0d25d44ea1d4165082e1c7b11a1e1adeb4","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T22:57:49.845087Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-16T10:20:50.023954Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2201.02564","last_updated":"2022-09-20T15:51:32Z","snapshot_observed_at":"2026-07-06T12:25:30.741916Z","submitted_at":"2022-01-07T17:48:49Z","title":"Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.02564","snapshot_observed_at":"2026-08-10T22:57:49.845087Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.00374","last_updated":"2024-12-31T09:55:21Z","snapshot_observed_at":"2026-08-10T22:50:26.289188Z","submitted_at":"2024-12-31T09:55:21Z","title":"Deep learning for exploring hadron-hadron interactions","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T22:57:49.845087Z"},"links":{"cited_paper":"/paper/2201.02564","citing_paper":"/paper/2501.00374"},"observation_digest":"sha256:7f2bf278a9fa74933d0c7c503f8fd461029b8a5e6b4ec346cd6d93a5b8908fd8","observation_id":"92c9c3d4-b1eb-452a-bda7-acb81984b08b","resolution":{"observed_at":"2026-08-10T22:57:49.845087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.02564","last_updated":"2022-09-20T15:51:32Z","snapshot_observed_at":"2026-07-06T12:25:30.741916Z","submitted_at":"2022-01-07T17:48:49Z","title":"Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.02564","snapshot_observed_at":"2026-08-10T14:01:08.784703Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.15810","last_updated":"2025-04-30T02:15:27Z","snapshot_observed_at":"2026-08-11T09:25:50.152384Z","submitted_at":"2025-01-27T06:34:32Z","title":"Towards constraining QCD phase transitions in neutron star interiors: Bayesian Inference with TOV linear response analysis","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-10T14:01:08.784703Z"},"links":{"cited_paper":"/paper/2201.02564","citing_paper":"/paper/2501.15810"},"observation_digest":"sha256:784ea558147d27ddc09ccaddff14d09231e4ac745b878532a126be7162e61de6","observation_id":"d618992a-b70e-4d64-9b0f-ba3757ea7e26","resolution":{"observed_at":"2026-08-10T14:01:08.784703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.02564","last_updated":"2022-09-20T15:51:32Z","snapshot_observed_at":"2026-07-06T12:25:30.741916Z","submitted_at":"2022-01-07T17:48:49Z","title":"Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help","version":2},"cited_work":{"arxiv_id":"2201.02564","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2201.02564","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"449b2e64-38e3-4533-a795-b66d69060ee4","year":2023},"citing_paper":{"arxiv_id":"2601.21155","last_updated":"2026-04-08T06:20:16Z","snapshot_observed_at":"2026-07-06T22:43:21.411174Z","submitted_at":"2026-01-29T01:30:05Z","title":"Nucleon axial-vector form factor and radius from radiatively-corrected antineutrino scattering data","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-05-16T10:20:38.279698Z"},"links":{"cited_paper":"/paper/2201.02564","citing_paper":"/paper/2601.21155"},"observation_digest":"sha256:82ed83943b30bbda4f05968259ab0db3d4e107c23bab8ff32a2f804c5f125769","observation_id":"a374dcb7-b59f-4bbe-ba25-05d5b274bd07","resolution":{"observed_at":"2026-05-16T10:20:50.025605Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2201.02564/citation-record","integrity":"/paper/2201.02564/integrity","json":"/paper/2201.02564/citation-record.json","paper":"/paper/2201.02564"},"outbound":[],"paper":{"arxiv_id":"2201.02564","last_updated":"2022-09-20T15:51:32Z","latest_version":2,"primary_category":"hep-ph","snapshot_observed_at":"2026-07-06T12:25:30.741916Z","submitted_at":"2022-01-07T17:48:49Z","title":"Rethinking the ill-posedness of the spectral function reconstruction -- why is it fundamentally hard and how Artificial Neural Networks can help"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2201.02564."}