{"as_of":"2026-08-09T10:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:3fcd2d82e557f275e3b0ba105a6985082e9e7d676510cbb2dca903403405e752","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":"2026-08-09T06:31:02.800959+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":"2026-08-07T15:32:15.371424Z","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":"2026-05-21T05:13:58.173979Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.11006","last_updated":"2020-06-19T08:09:07Z","snapshot_observed_at":"2026-08-04T10:44:39.593215Z","submitted_at":"2020-06-19T08:09:07Z","title":"Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.11006","snapshot_observed_at":"2026-08-07T15:32:15.371424Z","title":"C.Statistical and algorithmic insights for semi-supervised learning with self-training.arXiv preprint arXiv:2006.11006(2020)","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.15195","last_updated":"2025-05-21T07:16:44Z","snapshot_observed_at":"2026-08-08T14:52:48.276800Z","submitted_at":"2025-05-21T07:16:44Z","title":"Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T15:32:15.371424Z"},"links":{"cited_paper":"/paper/2006.11006","citing_paper":"/paper/2505.15195"},"observation_digest":"sha256:4a7decfaf385220ab1cfae79bd1f166de15dccca003b5eeaed22143a6f674ba6","observation_id":"66bd5ed1-f4d6-466d-818f-5bc84cd59f6e","resolution":{"observed_at":"2026-08-07T15:32:15.371424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11006","last_updated":"2020-06-19T08:09:07Z","snapshot_observed_at":"2026-08-04T10:44:39.593215Z","submitted_at":"2020-06-19T08:09:07Z","title":"Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training","version":1},"cited_work":{"arxiv_id":"2006.11006","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.11006","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Statistical and algorithmic insights for semi- supervised learning with self-training","venue":null,"work_id":"c5950305-79ef-42ea-9409-59e81f82dbe5","year":2006},"citing_paper":{"arxiv_id":"2605.17778","last_updated":"2026-05-18T02:56:57Z","snapshot_observed_at":"2026-07-06T23:28:45.646975Z","submitted_at":"2026-05-18T02:56:57Z","title":"Self-Distillation is Optimal Among Spectral Shrinkage Estimators in Spiked Covariance Models","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-20T01:03:22.678982Z"},"links":{"cited_paper":"/paper/2006.11006","citing_paper":"/paper/2605.17778"},"observation_digest":"sha256:e20c8c43ae5ea4ac88d9accc5365dced6c850bdbea67d9c2f2763f0dc077ebe6","observation_id":"5b1cfb36-3c8b-4145-9a9c-48a554c42068","resolution":{"observed_at":"2026-05-20T01:07:54.799743Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.11006","last_updated":"2020-06-19T08:09:07Z","snapshot_observed_at":"2026-08-04T10:44:39.593215Z","submitted_at":"2020-06-19T08:09:07Z","title":"Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training","version":1},"cited_work":{"arxiv_id":"2006.11006","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.11006","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Statistical and algorithmic insights for semi- supervised learning with self-training","venue":null,"work_id":"c5950305-79ef-42ea-9409-59e81f82dbe5","year":2006},"citing_paper":{"arxiv_id":"2605.20742","last_updated":"2026-05-20T05:44:52Z","snapshot_observed_at":"2026-08-01T10:07:56.006010Z","submitted_at":"2026-05-20T05:44:52Z","title":"VBFDD-Agent for Electric Vehicle Battery Fault Detection and Diagnosis: Descriptive Text Modeling of Battery Digital Signals","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-21T05:13:19.835635Z"},"links":{"cited_paper":"/paper/2006.11006","citing_paper":"/paper/2605.20742"},"observation_digest":"sha256:4770fda266a75cf1fea02b192aba40045cd689418b5b1a3b9132ac67f8e58525","observation_id":"7955d99b-5f60-4be3-b84e-2130e1914dc2","resolution":{"observed_at":"2026-05-21T05:13:58.176227Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2006.11006/citation-record","integrity":"/paper/2006.11006/integrity","json":"/paper/2006.11006/citation-record.json","paper":"/paper/2006.11006"},"outbound":[],"paper":{"arxiv_id":"2006.11006","last_updated":"2020-06-19T08:09:07Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-04T10:44:39.593215Z","submitted_at":"2020-06-19T08:09:07Z","title":"Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2006.11006."}