{"as_of":"2026-08-20T09:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e933be38de600fdb8ca6409778cd365a14e7dccf4f6f1164eb2d045686df2661","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":"2026-08-20T06:33:59.587034+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":"2026-08-12T20:09:09.862330Z","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":"2026-05-17T20:50:14.941116Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.11896","last_updated":"2024-01-23T16:08:24Z","snapshot_observed_at":"2026-08-16T23:27:06.270025Z","submitted_at":"2024-01-22T12:46:18Z","title":"Comparison of Model Output Statistics and Neural Networks to Postprocess Wind Gusts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.11896","snapshot_observed_at":"2026-08-12T20:09:09.862330Z","title":"Schulz, S","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.10010","last_updated":"2025-06-22T01:09:22Z","snapshot_observed_at":"2026-08-16T15:06:49.541140Z","submitted_at":"2024-11-15T07:42:16Z","title":"DeepMedcast: A Deep Learning Method for Generating Intermediate Weather Forecasts among Multiple NWP Models","version":2},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-12T20:09:09.862330Z"},"links":{"cited_paper":"/paper/2401.11896","citing_paper":"/paper/2411.10010"},"observation_digest":"sha256:5ba6019e7a9dfa7746cfac43c0770fd1ff2209fb9964dbc32b62dc29bb7d18ff","observation_id":"1842e2d8-b53b-41ff-8d7c-1fa7011ff15d","resolution":{"observed_at":"2026-08-12T20:09:09.862330Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.11896","last_updated":"2024-01-23T16:08:24Z","snapshot_observed_at":"2026-08-16T23:27:06.270025Z","submitted_at":"2024-01-22T12:46:18Z","title":"Comparison of Model Output Statistics and Neural Networks to Postprocess Wind Gusts","version":2},"cited_work":{"arxiv_id":"2401.11896","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.11896","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Comparison of Model Output Statistics and Neural Networks to Postprocess Wind Gusts, January 2024","venue":null,"work_id":"8b45b318-6666-4e9f-b9e8-ac2cd3d9ca91","year":2024},"citing_paper":{"arxiv_id":"2511.16164","last_updated":"2026-04-13T08:01:36Z","snapshot_observed_at":"2026-08-15T03:30:49.981265Z","submitted_at":"2025-11-20T09:05:39Z","title":"Achieving Skilled and Reliable Daily Probabilistic Forecasts of Wind Power at Subseasonal-to-Seasonal Timescales over France","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-17T20:48:59.110280Z"},"links":{"cited_paper":"/paper/2401.11896","citing_paper":"/paper/2511.16164"},"observation_digest":"sha256:1cca1af8a43d7d6c41d4426ce88532149cefb712b5ae1021544d704969a0d4d6","observation_id":"632fdb84-1dc0-48dc-aee0-4380cd027fdf","resolution":{"observed_at":"2026-05-17T20:50:14.944036Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2401.11896/citation-record","integrity":"/paper/2401.11896/integrity","json":"/paper/2401.11896/citation-record.json","paper":"/paper/2401.11896"},"outbound":[],"paper":{"arxiv_id":"2401.11896","last_updated":"2024-01-23T16:08:24Z","latest_version":2,"primary_category":"stat.AP","snapshot_observed_at":"2026-08-16T23:27:06.270025Z","submitted_at":"2024-01-22T12:46:18Z","title":"Comparison of Model Output Statistics and Neural Networks to Postprocess Wind Gusts"},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.11896."}