{"as_of":"2026-08-10T04:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:556a4b14586c7a5984e5a08afea32e56258b225fd6cda4436f7b7aaa7d6bd636","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":"2026-08-09T06:31:02.800959+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":"2026-08-06T16:32:02.082275Z","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":"2026-05-11T12:06:02.131669Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.10837","last_updated":"2025-05-06T07:20:04Z","snapshot_observed_at":"2026-08-07T17:05:48.572375Z","submitted_at":"2025-03-13T19:40:58Z","title":"Lessons from the trenches on evaluating machine-learning systems in materials science","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.10837","snapshot_observed_at":"2026-08-06T16:32:02.082275Z","title":"Alampara, M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.13246","last_updated":"2025-07-17T15:55:02Z","snapshot_observed_at":"2026-08-09T12:42:20.958851Z","submitted_at":"2025-07-17T15:55:02Z","title":"The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics?","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T16:32:02.082275Z"},"links":{"cited_paper":"/paper/2503.10837","citing_paper":"/paper/2507.13246"},"observation_digest":"sha256:dbfeb67a46842f474c2592d600d5036e1e6e3c76b3b5a527a580201b541b628d","observation_id":"e7125f59-67a9-483d-98db-bcfafc7c6619","resolution":{"observed_at":"2026-08-06T16:32:02.082275Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.10837","last_updated":"2025-05-06T07:20:04Z","snapshot_observed_at":"2026-08-07T17:05:48.572375Z","submitted_at":"2025-03-13T19:40:58Z","title":"Lessons from the trenches on evaluating machine-learning systems in materials science","version":2},"cited_work":{"arxiv_id":"2503.10837","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2503.10837","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Lessons from the trenches on evaluating machine-learning systems in materials science","venue":null,"work_id":"7db9c4d7-ec1d-468a-8dd0-5f550c0c9c49","year":2025},"citing_paper":{"arxiv_id":"2604.18086","last_updated":"2026-04-20T11:04:40Z","snapshot_observed_at":"2026-08-02T17:43:22.846452Z","submitted_at":"2026-04-20T11:04:40Z","title":"Materials Informatics Across the Length Scales","version":1},"reference_index":157,"source":"pdf_text","source_observed_at":"2026-05-10T04:13:12.315655Z"},"links":{"cited_paper":"/paper/2503.10837","citing_paper":"/paper/2604.18086"},"observation_digest":"sha256:7057a77a20232ce848c2fe51df7049be0545fb39a34d86b1925d124c966f556e","observation_id":"35a1bc22-238c-4c8b-aa5d-a9acae6c15d1","resolution":{"observed_at":"2026-05-11T12:06:02.138652Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2503.10837/citation-record","integrity":"/paper/2503.10837/integrity","json":"/paper/2503.10837/citation-record.json","paper":"/paper/2503.10837"},"outbound":[],"paper":{"arxiv_id":"2503.10837","last_updated":"2025-05-06T07:20:04Z","latest_version":2,"primary_category":"cond-mat.mtrl-sci","snapshot_observed_at":"2026-08-07T17:05:48.572375Z","submitted_at":"2025-03-13T19:40:58Z","title":"Lessons from the trenches on evaluating machine-learning systems in materials science"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2503.10837."}