{"as_of":"2026-08-09T04:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:34a806fbce1ccf870aa4af69b5742e097e5cb1a011da7e19b867ae25d6a82a31","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":"2026-08-08T06:32:00.761636+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":"2026-08-04T07:36:35.718440Z","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":"2026-05-14T18:29:22.685137Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1607.03188","last_updated":"2018-04-23T13:30:35Z","snapshot_observed_at":"2026-08-04T04:34:36.728470Z","submitted_at":"2016-07-11T22:30:50Z","title":"The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.03188","snapshot_observed_at":"2026-08-04T07:36:35.718440Z","title":"arXiv:1607.03188","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2510.25824","last_updated":"2026-07-27T20:50:55Z","snapshot_observed_at":"2026-08-07T12:03:29.706461Z","submitted_at":"2025-10-29T18:00:00Z","title":"The Ray Tracing Sampler: Bayesian Sampling of Neural Networks for Everyone","version":2},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-04T07:36:35.718440Z"},"links":{"cited_paper":"/paper/1607.03188","citing_paper":"/paper/2510.25824"},"observation_digest":"sha256:6c304effb0e3c273fad3b2a32c91d9ac65fdac5a612ce2f1ff645929c20a1d43","observation_id":"f22571de-83f3-4439-995d-ef98095cada7","resolution":{"observed_at":"2026-08-04T07:36:35.718440Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.03188","last_updated":"2018-04-23T13:30:35Z","snapshot_observed_at":"2026-08-04T04:34:36.728470Z","submitted_at":"2016-07-11T22:30:50Z","title":"The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data","version":2},"cited_work":{"arxiv_id":"1607.03188","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1607.03188","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Fearnhead, P","venue":null,"work_id":"7a3079d9-1a60-4ff4-82d0-ecce797cac5c","year":null},"citing_paper":{"arxiv_id":"2605.13127","last_updated":"2026-05-13T07:54:37Z","snapshot_observed_at":"2026-07-06T23:24:42.799888Z","submitted_at":"2026-05-13T07:54:37Z","title":"State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives","version":1},"reference_index":207,"source":"arxiv_source","source_observed_at":"2026-05-14T18:27:47.980646Z"},"links":{"cited_paper":"/paper/1607.03188","citing_paper":"/paper/2605.13127"},"observation_digest":"sha256:1f5bfeb1fc416ac323f4fdf4b5f7616569be8d05f1e85042c9fba707cd3bb831","observation_id":"f6bbed42-e84b-47a8-90e4-b184b7fb3a27","resolution":{"observed_at":"2026-05-14T18:29:22.688107Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1607.03188/citation-record","integrity":"/paper/1607.03188/integrity","json":"/paper/1607.03188/citation-record.json","paper":"/paper/1607.03188"},"outbound":[],"paper":{"arxiv_id":"1607.03188","last_updated":"2018-04-23T13:30:35Z","latest_version":2,"primary_category":"stat.CO","snapshot_observed_at":"2026-08-04T04:34:36.728470Z","submitted_at":"2016-07-11T22:30:50Z","title":"The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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:1607.03188."}