{"as_of":"2026-08-11T12:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:40896fdc2a57baa5ea5e3fe3c259c1bfcfcc2239ef997764cc0fef81c35f5c0c","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T13:55:31.399633Z","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":"2026-06-29T14:03:29.695545Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2201.06390","last_updated":"2022-01-17T12:58:45Z","snapshot_observed_at":"2026-08-04T15:18:38.893887Z","submitted_at":"2022-01-17T12:58:45Z","title":"SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window Transformers","version":1},"cited_work":{"arxiv_id":"2201.06390","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2201.06390","snapshot_observed_at":"2026-06-29T14:03:29.695545Z","title":"Journal of Transportation Engineering, Part B: Pavements 152, 04026010","venue":null,"work_id":"e341ca6a-3628-4fec-b964-9f04042c6bd4","year":2016},"citing_paper":{"arxiv_id":"2605.27884","last_updated":"2026-05-27T03:07:53Z","snapshot_observed_at":"2026-08-09T23:15:12.318251Z","submitted_at":"2026-05-27T03:07:53Z","title":"A Road-Conditioned Traffic Movie Prediction Network with Spatiotemporal and Structure-Consistent Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-29T13:55:31.399633Z"},"links":{"cited_paper":"/paper/2201.06390","citing_paper":"/paper/2605.27884"},"observation_digest":"sha256:6406e7451a7d68840d674a18b8459284faec37cb909eb502fa5be181b7ae6e8e","observation_id":"b3f7b413-8650-4c0c-8961-3a6dc30a3c91","resolution":{"observed_at":"2026-06-29T14:03:29.697223Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2201.06390/citation-record","integrity":"/paper/2201.06390/integrity","json":"/paper/2201.06390/citation-record.json","paper":"/paper/2201.06390"},"outbound":[],"paper":{"arxiv_id":"2201.06390","last_updated":"2022-01-17T12:58:45Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-04T15:18:38.893887Z","submitted_at":"2022-01-17T12:58:45Z","title":"SwinUNet3D -- A Hierarchical Architecture for Deep Traffic Prediction using Shifted Window Transformers"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2201.06390."}