{"as_of":"2026-08-14T15:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f0eef9e0e0df6b46248cc180a863d1f70c33b8388ee6433949abdfb89c488ba3","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":"2026-08-14T06:32:32.682623+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":"2026-07-11T16:17:26.963678Z","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":"2026-05-11T14:06:05.134103Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2603.05238","last_updated":"2026-06-07T15:09:25Z","snapshot_observed_at":"2026-08-11T17:54:06.407255Z","submitted_at":"2026-03-05T14:54:29Z","title":"Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects","version":2},"cited_work":{"arxiv_id":"2603.05238","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2603.05238","snapshot_observed_at":"2026-06-09T02:06:15.299570Z","title":"Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects","venue":null,"work_id":"85e84362-851a-43d9-8ed5-5dfef427014a","year":2026},"citing_paper":{"arxiv_id":"2604.21069","last_updated":"2026-04-22T20:29:41Z","snapshot_observed_at":"2026-08-11T03:23:08.934757Z","submitted_at":"2026-04-22T20:29:41Z","title":"Accelerating point defect simulations using data-driven and machine learning approaches","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-09T23:29:55.933661Z"},"links":{"cited_paper":"/paper/2603.05238","citing_paper":"/paper/2604.21069"},"observation_digest":"sha256:57b0b13d9aca2552c3bfce2a1757dcd0d2c1d4eef5864f0e2496ec1c9fd3cdc4","observation_id":"14479388-5753-44b0-827e-743aef50fd7c","resolution":{"observed_at":"2026-06-09T02:06:15.299570Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2603.05238","last_updated":"2026-06-07T15:09:25Z","snapshot_observed_at":"2026-08-11T17:54:06.407255Z","submitted_at":"2026-03-05T14:54:29Z","title":"Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2603.05238","snapshot_observed_at":"2026-07-11T16:17:26.963678Z","title":"Multi-fidelity","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.04622","last_updated":"2026-07-06T03:04:15Z","snapshot_observed_at":"2026-08-13T18:54:02.598800Z","submitted_at":"2026-07-06T03:04:15Z","title":"VASP Plugins: Linking the Vienna ab-initio Simulation Package with Python","version":1},"reference_index":115,"source":"arxiv_source","source_observed_at":"2026-07-11T16:17:26.963678Z"},"links":{"cited_paper":"/paper/2603.05238","citing_paper":"/paper/2607.04622"},"observation_digest":"sha256:e4b487f0b3c8f7848ff96b9fe6977e45f96989a0eea5aeb92e5c77ad1bb7cda5","observation_id":"59683666-0ee1-485d-a440-8b70fbd0f349","resolution":{"observed_at":"2026-07-11T16:17:26.963678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2603.05238/citation-record","integrity":"/paper/2603.05238/integrity","json":"/paper/2603.05238/citation-record.json","paper":"/paper/2603.05238"},"outbound":[],"paper":{"arxiv_id":"2603.05238","last_updated":"2026-06-07T15:09:25Z","latest_version":2,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-11T17:54:06.407255Z","submitted_at":"2026-03-05T14:54:29Z","title":"Multi-fidelity Machine Learning Interatomic Potentials for Charged Point Defects"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2603.05238."}