{"as_of":"2026-08-19T05:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a00406b0431f061c65582e591de88ba477ce6cdbee5e9096b1903523893798bf","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T15:03:40.138335Z","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":"2026-08-04T20:50:45.112157Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2012.09258","last_updated":"2022-09-06T07:23:55Z","snapshot_observed_at":"2026-08-16T20:37:06.685968Z","submitted_at":"2020-12-16T20:50:12Z","title":"Detection of data drift and outliers affecting machine learning model performance over time","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.09258","snapshot_observed_at":"2026-08-07T15:03:40.138335Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.18770","last_updated":"2025-05-22T09:36:17Z","snapshot_observed_at":"2026-08-14T02:31:16.728536Z","submitted_at":"2025-05-22T09:36:17Z","title":"Importance of User Control in Data-Centric Steering for Healthcare Experts","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-07T15:03:40.138335Z"},"links":{"cited_paper":"/paper/2012.09258","citing_paper":"/paper/2506.18770"},"observation_digest":"sha256:91aad8b13070e0693f734efc02634a6297662ccf59c8af7a722370ba5cb913d4","observation_id":"a155493a-8319-4ed1-af90-4bedc6b4d5d1","resolution":{"observed_at":"2026-08-07T15:03:40.138335Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.09258","last_updated":"2022-09-06T07:23:55Z","snapshot_observed_at":"2026-08-16T20:37:06.685968Z","submitted_at":"2020-12-16T20:50:12Z","title":"Detection of data drift and outliers affecting machine learning model performance over time","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.09258","snapshot_observed_at":"2026-08-06T10:46:01.696517Z","title":"Detection of data drift and outliers affecting machine learning model performance over time,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.00042","last_updated":"2026-07-17T08:54:41Z","snapshot_observed_at":"2026-08-14T04:03:53.299255Z","submitted_at":"2025-07-31T12:31:57Z","title":"Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T10:46:01.696517Z"},"links":{"cited_paper":"/paper/2012.09258","citing_paper":"/paper/2508.00042"},"observation_digest":"sha256:9b18e740a6700ceef04a6f22c7dfe95ed202970030009007baae303baf779f1a","observation_id":"8d1920a6-4cdb-4570-a922-ce0735e00231","resolution":{"observed_at":"2026-08-06T10:46:01.696517Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.09258","last_updated":"2022-09-06T07:23:55Z","snapshot_observed_at":"2026-08-16T20:37:06.685968Z","submitted_at":"2020-12-16T20:50:12Z","title":"Detection of data drift and outliers affecting machine learning model performance over time","version":3},"cited_work":{"arxiv_id":"2012.09258","doi":null,"metadata_source":"pith","pith_arxiv_id":"2012.09258","snapshot_observed_at":"2026-08-04T20:50:45.112157Z","title":"Detection of data drift and outliers affecting machine learning model performance over time","venue":"stat.AP","work_id":"3692c047-41fb-47d2-bb80-14fecfe4a23e","year":2020},"citing_paper":{"arxiv_id":"2509.10560","last_updated":"2025-09-10T06:33:09Z","snapshot_observed_at":"2026-08-11T09:39:20.720949Z","submitted_at":"2025-09-10T06:33:09Z","title":"GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T20:50:44.635986Z"},"links":{"cited_paper":"/paper/2012.09258","citing_paper":"/paper/2509.10560"},"observation_digest":"sha256:7caf4fff312e013b0d48b26c381723304e568135e9fe3521a01517ddc19db5d1","observation_id":"8506f8cb-b3cb-4838-af89-8147ee6a4df0","resolution":{"observed_at":"2026-08-04T20:50:45.117541Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.09258","last_updated":"2022-09-06T07:23:55Z","snapshot_observed_at":"2026-08-16T20:37:06.685968Z","submitted_at":"2020-12-16T20:50:12Z","title":"Detection of data drift and outliers affecting machine learning model performance over time","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.09258","snapshot_observed_at":"2026-08-02T08:50:37.847598Z","title":"Detection of data drift and outliers affecting machine learning model performance over time.arXiv preprint arXiv:2012.09258,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2607.21623","last_updated":"2026-07-04T01:39:59Z","snapshot_observed_at":"2026-08-18T11:12:56.093014Z","submitted_at":"2026-07-04T01:39:59Z","title":"Cloud-Native Evaluation-as-a-Service: A Microservices Architecture for Scalable AI Monitoring with Conformal Guarantees","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-02T08:50:37.847598Z"},"links":{"cited_paper":"/paper/2012.09258","citing_paper":"/paper/2607.21623"},"observation_digest":"sha256:f983642bc1cf1cdc0a11040b05e05740815e9d2bde87f432c0131bbba05544db","observation_id":"1bcb441b-eb5d-4663-93fb-55289108ad32","resolution":{"observed_at":"2026-08-02T08:50:37.847598Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2012.09258/citation-record","integrity":"/paper/2012.09258/integrity","json":"/paper/2012.09258/citation-record.json","paper":"/paper/2012.09258"},"outbound":[],"paper":{"arxiv_id":"2012.09258","last_updated":"2022-09-06T07:23:55Z","latest_version":3,"primary_category":"stat.AP","snapshot_observed_at":"2026-08-16T20:37:06.685968Z","submitted_at":"2020-12-16T20:50:12Z","title":"Detection of data drift and outliers affecting machine learning model performance over time"},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2012.09258."}