{"as_of":"2026-08-19T20:24:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8e1bf2763791bda00d1ca5d8303a63c755c0328c00b317e95e1d207e84addebe","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":"2026-08-19T06:32:44.657259+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":"2026-08-16T10:55:05.048071Z","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":"2026-08-10T22:23:53.957128Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1805.00336","last_updated":"2019-01-31T20:31:32Z","snapshot_observed_at":"2026-08-14T19:21:01.258403Z","submitted_at":"2018-04-28T02:51:43Z","title":"Hyperparameter Optimization for Effort Estimation","version":4},"cited_work":{"arxiv_id":"1805.00336","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.00336","snapshot_observed_at":"2026-08-10T22:23:53.957128Z","title":"Hyperparameter Optimization for Effort Estimation","venue":"cs.SE","work_id":"e776f497-a2ae-4cda-b908-d0ecdfd34b13","year":2018},"citing_paper":{"arxiv_id":"2501.01876","last_updated":"2025-01-03T15:57:20Z","snapshot_observed_at":"2026-08-19T14:08:01.670627Z","submitted_at":"2025-01-03T15:57:20Z","title":"Accuracy Can Lie: On the Impact of Surrogate Model in Configuration Tuning","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-10T22:23:53.507503Z"},"links":{"cited_paper":"/paper/1805.00336","citing_paper":"/paper/2501.01876"},"observation_digest":"sha256:e1ccd2a716f621b9876db452d47f29fe9564a4856f50a5da2fd8cb1b60ae8574","observation_id":"424b2f4a-60bb-4c2b-8ca2-b6875ca83c2f","resolution":{"observed_at":"2026-08-10T22:23:53.962730Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.00336","last_updated":"2019-01-31T20:31:32Z","snapshot_observed_at":"2026-08-14T19:21:01.258403Z","submitted_at":"2018-04-28T02:51:43Z","title":"Hyperparameter Optimization for Effort Estimation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.00336","snapshot_observed_at":"2026-08-16T10:55:05.048071Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2504.17066","last_updated":"2025-05-01T07:20:54Z","snapshot_observed_at":"2026-08-16T10:48:11.968594Z","submitted_at":"2025-04-23T19:28:30Z","title":"Whence Is A Model Fair? Fixing Fairness Bugs via Propensity Score Matching","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-16T10:55:05.048071Z"},"links":{"cited_paper":"/paper/1805.00336","citing_paper":"/paper/2504.17066"},"observation_digest":"sha256:8a647b52d6e5b222cc38b94f46e61cc2cd44afdeb67da331ceee1605432dff35","observation_id":"e9f31a41-6076-420d-bc79-958e8793e65a","resolution":{"observed_at":"2026-08-16T10:55:05.048071Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1805.00336/citation-record","integrity":"/paper/1805.00336/integrity","json":"/paper/1805.00336/citation-record.json","paper":"/paper/1805.00336"},"outbound":[],"paper":{"arxiv_id":"1805.00336","last_updated":"2019-01-31T20:31:32Z","latest_version":4,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-14T19:21:01.258403Z","submitted_at":"2018-04-28T02:51:43Z","title":"Hyperparameter Optimization for Effort Estimation"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1805.00336."}