{"as_of":"2026-08-23T15:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:32dbe8f8874d78f6632de35aa019f7261124c1d14c97b5c0c20ecbf609f900df","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":"2026-08-23T06:30:58.430688+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":"2026-08-01T18:19:26.350797Z","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":"2210.16168","last_updated":"2022-10-28T14:43:13Z","snapshot_observed_at":"2026-08-16T16:20:19.174786Z","submitted_at":"2022-10-28T14:43:13Z","title":"Feature Engineering vs BERT on Twitter Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.16168","snapshot_observed_at":"2026-08-01T18:19:26.350797Z","title":"Feature engineering vs bert on twitter data.ArXiv, abs/2210.16168, 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.17353","last_updated":"2026-07-19T17:28:36Z","snapshot_observed_at":"2026-08-21T04:28:56.979052Z","submitted_at":"2026-07-19T17:28:36Z","title":"Measuring and Evaluating the Performance of Generative AI Models for Scam Detection","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T18:19:26.350797Z"},"links":{"cited_paper":"/paper/2210.16168","citing_paper":"/paper/2607.17353"},"observation_digest":"sha256:7bf45ff551c29849c7032251a9404f2f55f7c80bf6e12eb895ddaee205f9a2f9","observation_id":"0e1d3d16-e52e-4f85-a1f2-5e17e6564b91","resolution":{"observed_at":"2026-08-01T18:19:26.350797Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2210.16168/citation-record","integrity":"/paper/2210.16168/integrity","json":"/paper/2210.16168/citation-record.json","paper":"/paper/2210.16168"},"outbound":[],"paper":{"arxiv_id":"2210.16168","last_updated":"2022-10-28T14:43:13Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T16:20:19.174786Z","submitted_at":"2022-10-28T14:43:13Z","title":"Feature Engineering vs BERT on Twitter Data"},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2210.16168."}