{"as_of":"2026-08-09T00:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f95b77f9822f0d2751d42ca4358de93f1368cf2c26bbea12edade2a784f02a39","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":"2026-08-07T14:59:51.684623Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-07T14:59:51.846870Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2404.04074","last_updated":"2025-01-31T16:01:37Z","snapshot_observed_at":"2026-08-07T12:48:41.765177Z","submitted_at":"2024-04-05T13:03:13Z","title":"DGP-LVM: Derivative Gaussian process latent variable models","version":3},"cited_work":{"arxiv_id":"2404.04074","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.04074","snapshot_observed_at":"2026-08-07T14:59:51.846870Z","title":"DGP-LVM: Derivative Gaussian process latent variable models","venue":"stat.ME","work_id":"e1fa61c3-2119-403c-ad2a-bfc96f69642f","year":2024},"citing_paper":{"arxiv_id":"2505.16755","last_updated":"2025-05-22T14:59:21Z","snapshot_observed_at":"2026-08-07T14:53:57.334038Z","submitted_at":"2025-05-22T14:59:21Z","title":"Multi-Output Gaussian Processes for Graph-Structured Data","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T14:59:51.684623Z"},"links":{"cited_paper":"/paper/2404.04074","citing_paper":"/paper/2505.16755"},"observation_digest":"sha256:39e9b1c076a9adf1d6531180f20562b55bf4afc7fc18cb6215d79b99cb03206c","observation_id":"722a7c65-e282-470e-89a0-1145fd270074","resolution":{"observed_at":"2026-08-07T14:59:51.889658Z","resolver_source":"local_arxiv","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"}},{"citation":{"cited_paper":{"arxiv_id":"2404.04074","last_updated":"2025-01-31T16:01:37Z","snapshot_observed_at":"2026-08-07T12:48:41.765177Z","submitted_at":"2024-04-05T13:03:13Z","title":"DGP-LVM: Derivative Gaussian process latent variable models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.04074","snapshot_observed_at":"2026-08-04T00:38:16.917196Z","title":"doi: 10.1007/s11222-025-10644-4","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2511.00366","last_updated":"2026-06-16T18:34:04Z","snapshot_observed_at":"2026-08-06T20:26:41.001100Z","submitted_at":"2025-11-01T02:20:28Z","title":"A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications","version":2},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-04T00:38:16.917196Z"},"links":{"cited_paper":"/paper/2404.04074","citing_paper":"/paper/2511.00366"},"observation_digest":"sha256:08917473dfab432088df04d68392c26f2f1e350f9cbd37c69f04199a11a7de6d","observation_id":"11eb4e8f-0653-47f8-825f-1c7d5fb4d18a","resolution":{"observed_at":"2026-08-04T00:38:16.917196Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2404.04074/citation-record","integrity":"/paper/2404.04074/integrity","json":"/paper/2404.04074/citation-record.json","paper":"/paper/2404.04074"},"outbound":[],"paper":{"arxiv_id":"2404.04074","last_updated":"2025-01-31T16:01:37Z","latest_version":3,"primary_category":"stat.ME","snapshot_observed_at":"2026-08-07T12:48:41.765177Z","submitted_at":"2024-04-05T13:03:13Z","title":"DGP-LVM: Derivative Gaussian process latent variable models"},"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:2404.04074."}