{"as_of":"2026-08-08T07:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:505f899804ff3d2d6d7d482dc0d9395c816d1e71186cc58b37c1499a8ba1fa0b","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":"2026-08-08T06:32:00.761636+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":"2026-08-06T00:57:11.271161Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T00:57:13.487930Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2401.10643","last_updated":"2024-01-19T11:45:10Z","snapshot_observed_at":"2026-07-06T17:17:52.174927Z","submitted_at":"2024-01-19T11:45:10Z","title":"A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges","version":1},"cited_work":{"arxiv_id":"2401.10643","doi":null,"metadata_source":"pith","pith_arxiv_id":"2401.10643","snapshot_observed_at":"2026-08-06T00:57:13.487930Z","title":"A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges","venue":"cs.CV","work_id":"2cdf9e1b-bf68-41e8-87c3-ed32e9f5c476","year":2024},"citing_paper":{"arxiv_id":"2508.04120","last_updated":"2025-08-06T06:36:44Z","snapshot_observed_at":"2026-08-06T00:57:10.788001Z","submitted_at":"2025-08-06T06:36:44Z","title":"CLIPVehicle: A Unified Framework for Vision-based Vehicle Search","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T00:57:11.271161Z"},"links":{"cited_paper":"/paper/2401.10643","citing_paper":"/paper/2508.04120"},"observation_digest":"sha256:6cb57a4b2ea71618b696c0bf45cc35abb26af89b20143008babf18fea3c81f76","observation_id":"e54d93aa-003e-43c0-8d9b-5175f3a372ca","resolution":{"observed_at":"2026-08-06T00:57:13.538232Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10643","last_updated":"2024-01-19T11:45:10Z","snapshot_observed_at":"2026-07-06T17:17:52.174927Z","submitted_at":"2024-01-19T11:45:10Z","title":"A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.10643","snapshot_observed_at":"2026-08-01T05:59:03.515543Z","title":"A comprehensive survey on deep-learning-based vehicle re-identification: Models, data sets and challenges,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.22068","last_updated":"2026-07-24T08:10:53Z","snapshot_observed_at":"2026-08-02T23:45:56.297068Z","submitted_at":"2026-07-24T08:10:53Z","title":"Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification in the Foundation-Model Era","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T05:59:03.515543Z"},"links":{"cited_paper":"/paper/2401.10643","citing_paper":"/paper/2607.22068"},"observation_digest":"sha256:a24b821970d46a04cc61747764b558f6c34d01192533fa3097750667250d93e2","observation_id":"bc5b2566-4b29-417d-88ad-59c87a3c3423","resolution":{"observed_at":"2026-08-01T05:59:03.515543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2401.10643/citation-record","integrity":"/paper/2401.10643/integrity","json":"/paper/2401.10643/citation-record.json","paper":"/paper/2401.10643"},"outbound":[],"paper":{"arxiv_id":"2401.10643","last_updated":"2024-01-19T11:45:10Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T17:17:52.174927Z","submitted_at":"2024-01-19T11:45:10Z","title":"A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.10643."}