{"as_of":"2026-08-21T17:00:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7c6c1c1c6fa42452a2c7d1df319c6785052255fab2e598f4fbb3d6bcde840154","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":"2026-08-21T06:32:19.484+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":"2026-08-03T16:43:19.498896Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1905.04753","last_updated":"2020-06-30T00:45:59Z","snapshot_observed_at":"2026-08-17T12:56:49.306795Z","submitted_at":"2019-05-12T17:49:49Z","title":"Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.04753","snapshot_observed_at":"2026-08-03T16:43:19.498896Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2512.12816","last_updated":"2026-07-31T00:19:12Z","snapshot_observed_at":"2026-08-16T07:26:46.965988Z","submitted_at":"2025-12-14T19:42:04Z","title":"Optimal Resource Allocation for ML Model Training and Deployment under Concept Drift","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T16:43:19.498896Z"},"links":{"cited_paper":"/paper/1905.04753","citing_paper":"/paper/2512.12816"},"observation_digest":"sha256:d449626ad83bb6695c81c7918722076869b3a889f4c7baf89cdd0326e295e416","observation_id":"b3f8d026-a075-440a-bc1f-8c18a66ce82d","resolution":{"observed_at":"2026-08-03T16:43:19.498896Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1905.04753/citation-record","integrity":"/paper/1905.04753/integrity","json":"/paper/1905.04753/citation-record.json","paper":"/paper/1905.04753"},"outbound":[],"paper":{"arxiv_id":"1905.04753","last_updated":"2020-06-30T00:45:59Z","latest_version":4,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-17T12:56:49.306795Z","submitted_at":"2019-05-12T17:49:49Z","title":"Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints"},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1905.04753."}