{"as_of":"2026-08-07T22:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d13ed017862224303d5d34dc7a62d2270f34bbabff75256b91964a95bec64a97","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":"2026-08-07T06:34:17.273281+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":"2026-08-04T14:56:47.942774Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2309.00483","last_updated":"2023-09-01T14:20:48Z","snapshot_observed_at":"2026-08-04T00:06:23.053195Z","submitted_at":"2023-09-01T14:20:48Z","title":"Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.00483","snapshot_observed_at":"2026-08-04T14:56:47.942774Z","title":"Geometry-aware line graph transformer pre-training for molecular property prediction, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.22468","last_updated":"2026-06-02T10:51:32Z","snapshot_observed_at":"2026-08-06T03:52:18.851570Z","submitted_at":"2025-09-26T15:16:20Z","title":"Learning the Neighborhood: Contrast-Free Multimodal Self-Supervised Molecular Graph Pretraining","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-04T14:56:47.942774Z"},"links":{"cited_paper":"/paper/2309.00483","citing_paper":"/paper/2509.22468"},"observation_digest":"sha256:265d956ffad9c7d4b1624532012320ee3e588c4e5a76389edc4bc2a6389dbd80","observation_id":"78452e43-d602-4ae1-a60f-c3ebbf6bbd8e","resolution":{"observed_at":"2026-08-04T14:56:47.942774Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2309.00483/citation-record","integrity":"/paper/2309.00483/integrity","json":"/paper/2309.00483/citation-record.json","paper":"/paper/2309.00483"},"outbound":[],"paper":{"arxiv_id":"2309.00483","last_updated":"2023-09-01T14:20:48Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T00:06:23.053195Z","submitted_at":"2023-09-01T14:20:48Z","title":"Geometry-aware Line Graph Transformer Pre-training for Molecular Property Prediction"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2309.00483."}