{"as_of":"2026-08-08T02:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8294e1a0feef7aee234a0a4fe6a855ade03635afe03da120de183c57e44bd7a5","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":"2026-08-07T06:34:17.273281+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":"2026-08-06T18:35:10.985537Z","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":"2026-08-06T18:35:11.317944Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2205.11913","last_updated":"2022-06-01T04:08:27Z","snapshot_observed_at":"2026-08-04T02:00:52.084165Z","submitted_at":"2022-05-24T09:18:06Z","title":"Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision","version":3},"cited_work":{"arxiv_id":"2205.11913","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.11913","snapshot_observed_at":"2026-08-06T18:35:11.317944Z","title":"Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision","venue":"cs.DC","work_id":"1dd559a2-5876-4d82-8125-d1a2b483805f","year":2022},"citing_paper":{"arxiv_id":"2507.07932","last_updated":"2025-07-10T17:10:51Z","snapshot_observed_at":"2026-08-06T18:26:41.491952Z","submitted_at":"2025-07-10T17:10:51Z","title":"KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T18:35:10.985537Z"},"links":{"cited_paper":"/paper/2205.11913","citing_paper":"/paper/2507.07932"},"observation_digest":"sha256:63b599e4dd5c115972280154bb984a60dbeca07eaeed08f9aa29c37f89b9e903","observation_id":"8d2faf1c-7b5b-410e-a0d9-2757d24dd339","resolution":{"observed_at":"2026-08-06T18:35:11.331805Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2205.11913/citation-record","integrity":"/paper/2205.11913/integrity","json":"/paper/2205.11913/citation-record.json","paper":"/paper/2205.11913"},"outbound":[],"paper":{"arxiv_id":"2205.11913","last_updated":"2022-06-01T04:08:27Z","latest_version":3,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-04T02:00:52.084165Z","submitted_at":"2022-05-24T09:18:06Z","title":"Deep Learning Workload Scheduling in GPU Datacenters: Taxonomy, Challenges and Vision"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2205.11913."}