{"as_of":"2026-08-09T09:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5331af04fbb27451e23619de22b055370bc438a2b7045e4194652be832fd3118","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":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:41:27.249212Z","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":"2026-06-30T12:24:39.129639Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2106.15112","last_updated":"2021-06-29T06:23:30Z","snapshot_observed_at":"2026-07-06T11:24:03.356343Z","submitted_at":"2021-06-29T06:23:30Z","title":"workload forecasting and resource management models based on machine learning for cloud computing environments","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.15112","snapshot_observed_at":"2026-08-06T16:41:27.249212Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.12908","last_updated":"2025-07-17T08:51:28Z","snapshot_observed_at":"2026-08-06T16:32:56.429187Z","submitted_at":"2025-07-17T08:51:28Z","title":"Fremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-06T16:41:27.249212Z"},"links":{"cited_paper":"/paper/2106.15112","citing_paper":"/paper/2507.12908"},"observation_digest":"sha256:28a4e8ffa4c769aa35ff67298af89a99755d0d727bfea702d64189566056869c","observation_id":"f22a66b1-2555-448b-b2f4-753ca057a054","resolution":{"observed_at":"2026-08-06T16:41:27.249212Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.15112","last_updated":"2021-06-29T06:23:30Z","snapshot_observed_at":"2026-07-06T11:24:03.356343Z","submitted_at":"2021-06-29T06:23:30Z","title":"workload forecasting and resource management models based on machine learning for cloud computing environments","version":1},"cited_work":{"arxiv_id":"2106.15112","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2106.15112","snapshot_observed_at":"2026-06-30T12:24:39.129639Z","title":"Workload forecasting and resource management models based on machine learning for cloud computing environments,","venue":null,"work_id":"eaec6907-2f7e-48b6-9bd1-997ab243c348","year":2021},"citing_paper":{"arxiv_id":"2605.24499","last_updated":"2026-05-23T10:14:50Z","snapshot_observed_at":"2026-08-08T22:52:49.747899Z","submitted_at":"2026-05-23T10:14:50Z","title":"Cloud Computing Review: A Decade of Research","version":1},"reference_index":139,"source":"pdf_text","source_observed_at":"2026-06-30T12:24:11.073615Z"},"links":{"cited_paper":"/paper/2106.15112","citing_paper":"/paper/2605.24499"},"observation_digest":"sha256:2fdebdbea8175d196be1df3f5c94646ea4e05477568637f9c13f7a0546ef7055","observation_id":"79b1d8ab-dd81-456e-89b5-3e5dab3cd352","resolution":{"observed_at":"2026-06-30T12:24:39.131789Z","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/2106.15112/citation-record","integrity":"/paper/2106.15112/integrity","json":"/paper/2106.15112/citation-record.json","paper":"/paper/2106.15112"},"outbound":[],"paper":{"arxiv_id":"2106.15112","last_updated":"2021-06-29T06:23:30Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-07-06T11:24:03.356343Z","submitted_at":"2021-06-29T06:23:30Z","title":"workload forecasting and resource management models based on machine learning for cloud computing environments"},"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 2 inbound Pith citation observations for arXiv:2106.15112."}