{"as_of":"2026-08-08T12:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:05b47577c360b1944b22302070af006317648babc4fd534d5740aa7ec0cdae3a","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":"2026-08-03T10:43:50.086229Z","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":"2406.11162","last_updated":"2024-06-26T01:43:15Z","snapshot_observed_at":"2026-08-03T09:21:32.187178Z","submitted_at":"2024-06-17T03:02:04Z","title":"How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11162","snapshot_observed_at":"2026-08-03T10:43:50.086229Z","title":"How good are llms at relation extraction under low-resource scenario? comprehensive evaluation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.09118","last_updated":"2026-07-16T14:44:01Z","snapshot_observed_at":"2026-08-06T20:05:00.082163Z","submitted_at":"2026-01-14T03:35:09Z","title":"LPCAN: Lightweight Pyramid Cross-Attention Network for Rail Surface Defect Detection Using RGB-D Data","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T10:43:50.086229Z"},"links":{"cited_paper":"/paper/2406.11162","citing_paper":"/paper/2601.09118"},"observation_digest":"sha256:7b6f22e6cf7287110405989ac865a196a7975f030bd4d69c8b2ff3ae1b8491fc","observation_id":"12ed9818-f4b6-4b66-9861-598e78ff140d","resolution":{"observed_at":"2026-08-03T10:43:50.086229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11162","last_updated":"2024-06-26T01:43:15Z","snapshot_observed_at":"2026-08-03T09:21:32.187178Z","submitted_at":"2024-06-17T03:02:04Z","title":"How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11162","snapshot_observed_at":"2026-08-03T10:43:42.292852Z","title":"How good are llms at relation extraction under low-resource scenario? comprehensive evaluation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.09238","last_updated":"2026-07-16T14:45:17Z","snapshot_observed_at":"2026-08-07T05:38:49.409741Z","submitted_at":"2026-01-14T07:21:57Z","title":"Knowledge-Embedded and Hypernetwork-Guided Few-Shot Substation Meter Defect Image Generation Method","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-03T10:43:42.292852Z"},"links":{"cited_paper":"/paper/2406.11162","citing_paper":"/paper/2601.09238"},"observation_digest":"sha256:0c34f37450f4ac62e9cefc5c7f4c5a6f438bc4c359d6b109c44a49082aec6de4","observation_id":"2974870d-8efe-4c39-beea-2a3055d39623","resolution":{"observed_at":"2026-08-03T10:43:42.292852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2406.11162/citation-record","integrity":"/paper/2406.11162/integrity","json":"/paper/2406.11162/citation-record.json","paper":"/paper/2406.11162"},"outbound":[],"paper":{"arxiv_id":"2406.11162","last_updated":"2024-06-26T01:43:15Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-03T09:21:32.187178Z","submitted_at":"2024-06-17T03:02:04Z","title":"How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation"},"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:2406.11162."}