{"as_of":"2026-08-19T17:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:023a0bca2ead5f2bf436a3fe4f3a16337038b230b5324b9333a74c5c363f7ec5","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":"2026-08-19T06:32:44.657259+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":"2026-08-10T21:41:30.270999Z","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":"2026-08-10T21:41:30.674428Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2205.07493","last_updated":"2023-07-21T06:28:40Z","snapshot_observed_at":"2026-08-16T17:00:07.021223Z","submitted_at":"2022-05-16T07:53:42Z","title":"Multi-scale Attention Flow for Probabilistic Time Series Forecasting","version":3},"cited_work":{"arxiv_id":"2205.07493","doi":null,"metadata_source":"pith","pith_arxiv_id":"2205.07493","snapshot_observed_at":"2026-08-10T21:41:30.674428Z","title":"Multi-scale Attention Flow for Probabilistic Time Series Forecasting","venue":"cs.LG","work_id":"1ce71a46-4e08-4b82-bb07-dfc2ead59dca","year":2022},"citing_paper":{"arxiv_id":"2501.04339","last_updated":"2026-06-09T10:04:44Z","snapshot_observed_at":"2026-08-14T05:24:02.680934Z","submitted_at":"2025-01-08T08:21:58Z","title":"Interpretable deep convolutional model for nonlinear multivariate time series in complex systems","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:41:30.270999Z"},"links":{"cited_paper":"/paper/2205.07493","citing_paper":"/paper/2501.04339"},"observation_digest":"sha256:0d31806b7a2975c8e0263e8558bd954f643329c5dd3571b429163680e281f1d0","observation_id":"49410b71-af94-49cc-9a20-f5e59897d6b5","resolution":{"observed_at":"2026-08-10T21:41:30.680019Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2205.07493/citation-record","integrity":"/paper/2205.07493/integrity","json":"/paper/2205.07493/citation-record.json","paper":"/paper/2205.07493"},"outbound":[],"paper":{"arxiv_id":"2205.07493","last_updated":"2023-07-21T06:28:40Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T17:00:07.021223Z","submitted_at":"2022-05-16T07:53:42Z","title":"Multi-scale Attention Flow for Probabilistic Time Series Forecasting"},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2205.07493."}