{"as_of":"2026-08-16T12:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:843ee845622e2db330024900dabcf577ced2c01a57b1a31759ad0a8d1505607b","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":"2026-08-16T06:30:59.297886+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":"2026-08-14T15:13:01.026813Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-14T15:13:01.246816Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1801.03132","last_updated":"2018-01-09T20:35:31Z","snapshot_observed_at":"2026-08-16T10:32:56.146366Z","submitted_at":"2018-01-09T20:35:31Z","title":"Robust Propensity Score Computation Method based on Machine Learning with Label-corrupted Data","version":1},"cited_work":{"arxiv_id":"1801.03132","doi":null,"metadata_source":"pith","pith_arxiv_id":"1801.03132","snapshot_observed_at":"2026-08-14T15:13:01.246816Z","title":"Robust Propensity Score Computation Method based on Machine Learning with Label-corrupted Data","venue":"stat.ME","work_id":"70572d26-13e1-4065-ab63-7e1a88ee0ecf","year":2018},"citing_paper":{"arxiv_id":"1908.01672","last_updated":"2021-08-22T15:46:39Z","snapshot_observed_at":"2026-08-14T15:04:07.783510Z","submitted_at":"2019-08-05T15:01:28Z","title":"Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:01.026813Z"},"links":{"cited_paper":"/paper/1801.03132","citing_paper":"/paper/1908.01672"},"observation_digest":"sha256:8a5a34a505cf8b4741b9df7ffe3dca1aa5fb2be3355a802ad93565ed1f33d06b","observation_id":"f3ea52c8-df33-4d73-8d85-fedba4de8f61","resolution":{"observed_at":"2026-08-14T15:13:01.254212Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1801.03132/citation-record","integrity":"/paper/1801.03132/integrity","json":"/paper/1801.03132/citation-record.json","paper":"/paper/1801.03132"},"outbound":[],"paper":{"arxiv_id":"1801.03132","last_updated":"2018-01-09T20:35:31Z","latest_version":1,"primary_category":"stat.ME","snapshot_observed_at":"2026-08-16T10:32:56.146366Z","submitted_at":"2018-01-09T20:35:31Z","title":"Robust Propensity Score Computation Method based on Machine Learning with Label-corrupted 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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1801.03132."}