{"as_of":"2026-08-07T00:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:78f15d16ab75a79b66ae57afb3de6c6ec3f898aeb1f32c65589ece1f2f825151","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":"2026-08-06T06:34:29.942622+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":"2026-05-23T02:54:08.887874Z","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":"2026-05-23T02:55:19.680741Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2002.02271","last_updated":"2020-02-06T14:25:08Z","snapshot_observed_at":"2026-07-06T08:55:18.040630Z","submitted_at":"2020-02-06T14:25:08Z","title":"Using generative adversarial networks to synthesize artificial financial datasets","version":1},"cited_work":{"arxiv_id":"2002.02271","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2002.02271","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a4a1dc08-d306-4314-bb47-1382d6af8fc6","year":2020},"citing_paper":{"arxiv_id":"2502.17011","last_updated":"2026-04-24T06:19:27Z","snapshot_observed_at":"2026-07-06T20:41:33.347753Z","submitted_at":"2025-02-24T09:46:37Z","title":"Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation","version":2},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-23T02:54:08.887874Z"},"links":{"cited_paper":"/paper/2002.02271","citing_paper":"/paper/2502.17011"},"observation_digest":"sha256:21400e9f3bb2d08d950dc4094af9f50aeffc5866f31928d214f59062a9147387","observation_id":"e2153349-5b0a-40d2-a76c-d86e35934992","resolution":{"observed_at":"2026-05-23T02:55:19.683292Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2002.02271/citation-record","integrity":"/paper/2002.02271/integrity","json":"/paper/2002.02271/citation-record.json","paper":"/paper/2002.02271"},"outbound":[],"paper":{"arxiv_id":"2002.02271","last_updated":"2020-02-06T14:25:08Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T08:55:18.040630Z","submitted_at":"2020-02-06T14:25:08Z","title":"Using generative adversarial networks to synthesize artificial financial datasets"},"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":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+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:2002.02271."}