{"as_of":"2026-08-10T21:59:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ea61426b1de92680db2689b035a79fb4bebb8f10ee9f70a3e4e63421eacc1065","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":"2026-08-10T06:31:04.303077+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":"2026-08-08T13:51:43.191185Z","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":"2026-05-11T15:16:10.858214Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2206.08744","last_updated":"2022-06-17T12:59:44Z","snapshot_observed_at":"2026-08-04T15:41:42.442457Z","submitted_at":"2022-06-17T12:59:44Z","title":"Improved uncertainty quantification for Gaussian process regression based interatomic potentials","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.08744","snapshot_observed_at":"2026-08-08T13:51:43.191185Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.07104","last_updated":"2025-03-03T10:36:32Z","snapshot_observed_at":"2026-08-09T06:40:40.707707Z","submitted_at":"2025-02-10T22:54:25Z","title":"Uncertainty Quantification for Misspecified Machine Learned Interatomic Potentials","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T13:51:43.191185Z"},"links":{"cited_paper":"/paper/2206.08744","citing_paper":"/paper/2502.07104"},"observation_digest":"sha256:c2becadbe30dd73c8eb1ab75d78ce625003c889a3e2f578495c5938828f30d15","observation_id":"9e050eec-9e31-46a5-b6e2-34b97952325a","resolution":{"observed_at":"2026-08-08T13:51:43.191185Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.08744","last_updated":"2022-06-17T12:59:44Z","snapshot_observed_at":"2026-08-04T15:41:42.442457Z","submitted_at":"2022-06-17T12:59:44Z","title":"Improved uncertainty quantification for Gaussian process regression based interatomic potentials","version":1},"cited_work":{"arxiv_id":"2206.08744","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2206.08744","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"P., Kermode, J.et al.Improved uncertainty quantification for gaussian process regression based interatomic potentials.arXiv preprint arXiv:2206.08744 (2022)","venue":null,"work_id":"653c0fe5-06cd-43bb-9f64-dfd961ad021f","year":2022},"citing_paper":{"arxiv_id":"2605.00640","last_updated":"2026-05-01T13:21:56Z","snapshot_observed_at":"2026-07-06T23:14:01.852096Z","submitted_at":"2026-05-01T13:21:56Z","title":"Knowing when to trust machine-learned interatomic potentials","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-09T20:20:22.056820Z"},"links":{"cited_paper":"/paper/2206.08744","citing_paper":"/paper/2605.00640"},"observation_digest":"sha256:e662e5e25b0d6899b2bec4db19ec5511f61a5184e990e67cf5e84df5d47bf218","observation_id":"abb3c4a8-b72d-4e95-b3af-652899fc8452","resolution":{"observed_at":"2026-05-11T15:16:10.862457Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2206.08744","last_updated":"2022-06-17T12:59:44Z","snapshot_observed_at":"2026-08-04T15:41:42.442457Z","submitted_at":"2022-06-17T12:59:44Z","title":"Improved uncertainty quantification for Gaussian process regression based interatomic potentials","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.08744","snapshot_observed_at":"2026-08-01T05:08:51.854655Z","title":"P.; Kermode, J","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.22338","last_updated":"2026-07-24T14:14:42Z","snapshot_observed_at":"2026-08-07T21:38:22.638268Z","submitted_at":"2026-07-24T14:14:42Z","title":"Uncertainty Quantification for Free Energy Calculations by Generalized Hierarchical Bayesian Inference","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T05:08:51.854655Z"},"links":{"cited_paper":"/paper/2206.08744","citing_paper":"/paper/2607.22338"},"observation_digest":"sha256:5de62a81ed36e0241f177d8ddefe7ab4f307179417bc59320f6b0fa7eca2400b","observation_id":"1e87edfc-a0df-442e-af37-542949807d7a","resolution":{"observed_at":"2026-08-01T05:08:51.854655Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2206.08744/citation-record","integrity":"/paper/2206.08744/integrity","json":"/paper/2206.08744/citation-record.json","paper":"/paper/2206.08744"},"outbound":[],"paper":{"arxiv_id":"2206.08744","last_updated":"2022-06-17T12:59:44Z","latest_version":1,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-04T15:41:42.442457Z","submitted_at":"2022-06-17T12:59:44Z","title":"Improved uncertainty quantification for Gaussian process regression based interatomic potentials"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2206.08744."}