{"as_of":"2026-08-20T14:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:fa5807342ab67f5e42ac9eed41ede30439025de5dd8038041ddefa9ed2e76f79","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":"2026-08-20T06:33:59.587034+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":"2026-06-28T03:10:42.043883Z","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":"2026-07-02T11:36:55.604654Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2309.16223","last_updated":"2023-11-05T16:34:36Z","snapshot_observed_at":"2026-08-16T14:56:57.568638Z","submitted_at":"2023-09-28T07:56:10Z","title":"GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations","version":2},"cited_work":{"arxiv_id":"2309.16223","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2309.16223","snapshot_observed_at":"2026-07-02T11:36:55.604654Z","title":null,"venue":null,"work_id":"580372d4-f9c7-46e7-b0aa-194e0540a169","year":2023},"citing_paper":{"arxiv_id":"2606.05756","last_updated":"2026-06-04T06:32:02Z","snapshot_observed_at":"2026-08-13T08:21:46.287304Z","submitted_at":"2026-06-04T06:32:02Z","title":"Beyond Soft Masks: Hard-Perturbation Mixup Explainer for Robust GNN Explainability","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-06-28T03:10:42.043883Z"},"links":{"cited_paper":"/paper/2309.16223","citing_paper":"/paper/2606.05756"},"observation_digest":"sha256:e6a20873ccd86d29081e0e6f5bf0848b3e66707767b64ff4c5abea59e36e2abb","observation_id":"0c856b7d-5433-40f4-992a-282672d6aa35","resolution":{"observed_at":"2026-07-02T11:36:55.606090Z","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/2309.16223/citation-record","integrity":"/paper/2309.16223/integrity","json":"/paper/2309.16223/citation-record.json","paper":"/paper/2309.16223"},"outbound":[],"paper":{"arxiv_id":"2309.16223","last_updated":"2023-11-05T16:34:36Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-16T14:56:57.568638Z","submitted_at":"2023-09-28T07:56:10Z","title":"GInX-Eval: Towards In-Distribution Evaluation of Graph Neural Network Explanations"},"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 1 inbound Pith citation observation for arXiv:2309.16223."}