{"as_of":"2026-08-18T03:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0ca55052afbd2fa3ebf924f48bba81e91aac1ba34198980388758e8f95ec3c32","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T23:22:46.458202Z","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":"2026-07-01T23:06:20.643231Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.10789","last_updated":"2024-06-16T03:10:16Z","snapshot_observed_at":"2026-08-16T13:42:37.822706Z","submitted_at":"2024-06-16T03:10:16Z","title":"Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10789","snapshot_observed_at":"2026-08-15T23:22:46.458202Z","title":"Learning traffic crashes as language: Datasets, benchmarks, and what-if causal analyses","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.07853","last_updated":"2025-05-08T00:23:18Z","snapshot_observed_at":"2026-08-17T20:37:18.639086Z","submitted_at":"2025-05-08T00:23:18Z","title":"CrashSage: A Large Language Model-Centered Framework for Contextual and Interpretable Traffic Crash Analysis","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T23:22:46.458202Z"},"links":{"cited_paper":"/paper/2406.10789","citing_paper":"/paper/2505.07853"},"observation_digest":"sha256:3fe25bd3308a52734a91c06b9baeca9581a2df28707e97009fde3806181f6b19","observation_id":"e747f55a-3174-4d8c-8590-34e76b368e5a","resolution":{"observed_at":"2026-08-15T23:22:46.458202Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10789","last_updated":"2024-06-16T03:10:16Z","snapshot_observed_at":"2026-08-16T13:42:37.822706Z","submitted_at":"2024-06-16T03:10:16Z","title":"Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10789","snapshot_observed_at":"2026-08-15T21:24:04.272455Z","title":"Eluru, and A","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2505.09949","last_updated":"2025-05-15T04:07:55Z","snapshot_observed_at":"2026-08-18T01:59:31.122479Z","submitted_at":"2025-05-15T04:07:55Z","title":"Advanced Crash Causation Analysis for Freeway Safety: A Large Language Model Approach to Identifying Key Contributing Factors","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T21:24:04.272455Z"},"links":{"cited_paper":"/paper/2406.10789","citing_paper":"/paper/2505.09949"},"observation_digest":"sha256:2732752b263acb763d2a8cda6f19dd43847654ab94cfb9899206bfb4a670ce03","observation_id":"7b6c24ea-bf77-406f-a2c6-af9bcc316956","resolution":{"observed_at":"2026-08-15T21:24:04.272455Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10789","last_updated":"2024-06-16T03:10:16Z","snapshot_observed_at":"2026-08-16T13:42:37.822706Z","submitted_at":"2024-06-16T03:10:16Z","title":"Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.10789","snapshot_observed_at":"2026-08-15T16:12:54.074249Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.07845","last_updated":"2025-09-09T15:22:14Z","snapshot_observed_at":"2026-08-15T16:08:04.922611Z","submitted_at":"2025-09-09T15:22:14Z","title":"Predicting person-level injury severity using crash narratives: A balanced approach with roadway classification and natural language process techniques","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T16:12:54.074249Z"},"links":{"cited_paper":"/paper/2406.10789","citing_paper":"/paper/2509.07845"},"observation_digest":"sha256:8e3d0cc1804c0e763a59fb7c7dbf44790feec9f29bc49a7bb02075c2e4cec540","observation_id":"70f96399-eacc-4013-b114-062c9d1c8db0","resolution":{"observed_at":"2026-08-15T16:12:54.074249Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10789","last_updated":"2024-06-16T03:10:16Z","snapshot_observed_at":"2026-08-16T13:42:37.822706Z","submitted_at":"2024-06-16T03:10:16Z","title":"Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses","version":1},"cited_work":{"arxiv_id":"2406.10789","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10789","snapshot_observed_at":"2026-07-01T23:06:20.643231Z","title":"arXiv preprint arXiv:2406.10789 (2024)","venue":null,"work_id":"0e6abcdd-aa93-47dc-b304-fedbc61f2ae3","year":2024},"citing_paper":{"arxiv_id":"2604.15369","last_updated":"2026-04-15T05:03:20Z","snapshot_observed_at":"2026-08-16T23:38:26.032950Z","submitted_at":"2026-04-15T05:03:20Z","title":"An Agentic Workflow for Detecting Personally Identifiable Information in Crash Narratives","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T13:40:33.026198Z"},"links":{"cited_paper":"/paper/2406.10789","citing_paper":"/paper/2604.15369"},"observation_digest":"sha256:6a49a811b98b73bd448ae21b6c047ae5e44fe40b66bef64ce22b306a1d4f0ce2","observation_id":"beccc242-6eb2-4b94-98ea-a94d1614a42c","resolution":{"observed_at":"2026-05-10T13:45:28.677775Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.10789","last_updated":"2024-06-16T03:10:16Z","snapshot_observed_at":"2026-08-16T13:42:37.822706Z","submitted_at":"2024-06-16T03:10:16Z","title":"Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses","version":1},"cited_work":{"arxiv_id":"2406.10789","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2406.10789","snapshot_observed_at":"2026-07-01T23:06:20.643231Z","title":"arXiv preprint arXiv:2406.10789 (2024)","venue":null,"work_id":"0e6abcdd-aa93-47dc-b304-fedbc61f2ae3","year":2024},"citing_paper":{"arxiv_id":"2606.01737","last_updated":"2026-06-01T06:01:04Z","snapshot_observed_at":"2026-08-16T23:39:07.244661Z","submitted_at":"2026-06-01T06:01:04Z","title":"TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-06-28T14:39:27.242094Z"},"links":{"cited_paper":"/paper/2406.10789","citing_paper":"/paper/2606.01737"},"observation_digest":"sha256:2c99931b8c9a4c3f95c0be3a7c133c98438fed46965458574e9ef340e60fb2cc","observation_id":"6bcae783-0d68-4367-9181-2b28c306a822","resolution":{"observed_at":"2026-07-01T23:06:20.647186Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.10789/citation-record","integrity":"/paper/2406.10789/integrity","json":"/paper/2406.10789/citation-record.json","paper":"/paper/2406.10789"},"outbound":[],"paper":{"arxiv_id":"2406.10789","last_updated":"2024-06-16T03:10:16Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T13:42:37.822706Z","submitted_at":"2024-06-16T03:10:16Z","title":"Learning Traffic Crashes as Language: Datasets, Benchmarks, and What-if Causal Analyses"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.10789."}