{"as_of":"2026-08-12T03:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9c55f187e577b24804fd1cf60d192d42890c0ed07350743f42fe2c13527fdef4","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T11:51:46.151791Z","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":"2026-07-02T21:57:26.321792Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2202.12780","last_updated":"2023-07-26T14:55:02Z","snapshot_observed_at":"2026-07-06T12:41:44.534788Z","submitted_at":"2022-02-25T15:52:19Z","title":"Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.12780","snapshot_observed_at":"2026-08-11T11:51:46.151791Z","title":"arXiv preprint arXiv:2202.12780 (2023) https://doi.org/10.48550/arXiv.2202.12780","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.14916","last_updated":"2025-04-28T11:50:11Z","snapshot_observed_at":"2026-08-11T11:46:18.558207Z","submitted_at":"2024-12-19T14:50:10Z","title":"From Point to probabilistic gradient boosting for claim frequency and severity prediction","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-11T11:51:46.151791Z"},"links":{"cited_paper":"/paper/2202.12780","citing_paper":"/paper/2412.14916"},"observation_digest":"sha256:722ac12ae76a5834d17af0a310945e20b0097b0e3091c756d3fb07ffb854117d","observation_id":"65ae20bf-4620-45e2-9733-8b9aaa0c0eed","resolution":{"observed_at":"2026-08-11T11:51:46.151791Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2202.12780","last_updated":"2023-07-26T14:55:02Z","snapshot_observed_at":"2026-07-06T12:41:44.534788Z","submitted_at":"2022-02-25T15:52:19Z","title":"Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice","version":3},"cited_work":{"arxiv_id":"2202.12780","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2202.12780","snapshot_observed_at":"2026-07-02T21:57:26.321792Z","title":"Model comparison and calibration assessment: User guide for consistent scoring functions in machine learning and actuarial practice.arXiv preprint arXiv:2202.12780, 2022","venue":null,"work_id":"a4b61af6-33b4-466b-ba5e-bcbeb3a66b9c","year":2022},"citing_paper":{"arxiv_id":"2606.08084","last_updated":"2026-06-06T10:14:36Z","snapshot_observed_at":"2026-08-05T02:57:41.291111Z","submitted_at":"2026-06-06T10:14:36Z","title":"Assessing model calibration with boosting trees","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T19:20:17.687178Z"},"links":{"cited_paper":"/paper/2202.12780","citing_paper":"/paper/2606.08084"},"observation_digest":"sha256:af4be2f2e7caeacc5373291cec45bcf76763f2636cd744c29fb89609c8ca9f68","observation_id":"d0eedd72-6b2e-463a-96a3-2570fe19233a","resolution":{"observed_at":"2026-07-02T21:57:26.323363Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.12780","last_updated":"2023-07-26T14:55:02Z","snapshot_observed_at":"2026-07-06T12:41:44.534788Z","submitted_at":"2022-02-25T15:52:19Z","title":"Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice","version":3},"cited_work":{"arxiv_id":"2202.12780","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2202.12780","snapshot_observed_at":"2026-07-02T21:57:26.321792Z","title":"Model comparison and calibration assessment: User guide for consistent scoring functions in machine learning and actuarial practice.arXiv preprint arXiv:2202.12780, 2022","venue":null,"work_id":"a4b61af6-33b4-466b-ba5e-bcbeb3a66b9c","year":2022},"citing_paper":{"arxiv_id":"2606.30410","last_updated":"2026-06-29T14:55:52Z","snapshot_observed_at":"2026-08-03T09:53:02.738738Z","submitted_at":"2026-06-29T14:55:52Z","title":"Beyond IID: How General Are Tabular Foundation Models, Really?","version":1},"reference_index":211,"source":"pdf_text","source_observed_at":"2026-06-30T06:59:14.626274Z"},"links":{"cited_paper":"/paper/2202.12780","citing_paper":"/paper/2606.30410"},"observation_digest":"sha256:87410e5118a8ce35e558c5c1a92815e7dc647575141c07097edc274676f23c0c","observation_id":"abed3abc-9331-4fdf-9674-9b25c7e4dae5","resolution":{"observed_at":"2026-06-30T07:04:21.354527Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2202.12780/citation-record","integrity":"/paper/2202.12780/integrity","json":"/paper/2202.12780/citation-record.json","paper":"/paper/2202.12780"},"outbound":[],"paper":{"arxiv_id":"2202.12780","last_updated":"2023-07-26T14:55:02Z","latest_version":3,"primary_category":"stat.ML","snapshot_observed_at":"2026-07-06T12:41:44.534788Z","submitted_at":"2022-02-25T15:52:19Z","title":"Model Comparison and Calibration Assessment: User Guide for Consistent Scoring Functions in Machine Learning and Actuarial Practice"},"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":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2202.12780."}