{"as_of":"2026-08-07T18:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:12b578cc09d5445e11a7355cd9cf74ce81363634cc9738dac2d9bb758a938718","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":"2026-08-07T06:34:17.273281+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":"2026-08-06T15:03:56.644997Z","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":"2026-05-19T04:12:02.604111Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2105.06868","last_updated":"2022-03-18T15:17:30Z","snapshot_observed_at":"2026-08-07T09:14:13.387916Z","submitted_at":"2021-05-14T14:53:30Z","title":"Priors in Bayesian Deep Learning: A Review","version":3},"cited_work":{"arxiv_id":"2105.06868","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2105.06868","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Priors in bayesian deep learning: A review","venue":null,"work_id":"3136c775-4629-467a-bb45-b51af3acc4bf","year":2021},"citing_paper":{"arxiv_id":"2507.16344","last_updated":"2026-05-12T15:22:48Z","snapshot_observed_at":"2026-07-06T22:00:52.749944Z","submitted_at":"2025-07-22T08:24:22Z","title":"Diff-ANO: Towards Fast High-Resolution Ultrasound Computed Tomography via Conditional Consistency Models and Adjoint Neural Operators","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-19T04:07:42.061343Z"},"links":{"cited_paper":"/paper/2105.06868","citing_paper":"/paper/2507.16344"},"observation_digest":"sha256:0627114d6823655ed18387908d1ec41d6a141e68cc836f684a622b883c0ff054","observation_id":"6e5ecaa3-6e78-484d-a872-cf4ac309d415","resolution":{"observed_at":"2026-05-19T04:12:02.606333Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.06868","last_updated":"2022-03-18T15:17:30Z","snapshot_observed_at":"2026-08-07T09:14:13.387916Z","submitted_at":"2021-05-14T14:53:30Z","title":"Priors in Bayesian Deep Learning: A Review","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.06868","snapshot_observed_at":"2026-08-06T15:03:56.644997Z","title":"Hobbhahn, M., Kristiadi, A., and Hennig, P","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.17013","last_updated":"2025-07-22T20:49:30Z","snapshot_observed_at":"2026-08-06T14:56:09.144793Z","submitted_at":"2025-07-22T20:49:30Z","title":"laplax -- Laplace Approximations with JAX","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-06T15:03:56.644997Z"},"links":{"cited_paper":"/paper/2105.06868","citing_paper":"/paper/2507.17013"},"observation_digest":"sha256:0fa758411236096fac543e8885bfb3e31f70df7055ec7139df135e1b9af04516","observation_id":"65b48e54-33f9-43ee-b701-81165ecda8a8","resolution":{"observed_at":"2026-08-06T15:03:56.644997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2105.06868/citation-record","integrity":"/paper/2105.06868/integrity","json":"/paper/2105.06868/citation-record.json","paper":"/paper/2105.06868"},"outbound":[],"paper":{"arxiv_id":"2105.06868","last_updated":"2022-03-18T15:17:30Z","latest_version":3,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-07T09:14:13.387916Z","submitted_at":"2021-05-14T14:53:30Z","title":"Priors in Bayesian Deep Learning: A Review"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2105.06868."}