{"as_of":"2026-08-23T13:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ce835d84f82047bbd39bbc111b724761cb72887492400774e3597924bec02979","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":"2026-08-23T06:30:58.430688+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":"2026-08-11T23:27:39.814859Z","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":"2026-08-11T23:27:39.887822Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2406.19154","last_updated":"2024-06-27T13:14:20Z","snapshot_observed_at":"2026-08-19T02:22:12.103264Z","submitted_at":"2024-06-27T13:14:20Z","title":"Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet)","version":1},"cited_work":{"arxiv_id":"2406.19154","doi":null,"metadata_source":"pith","pith_arxiv_id":"2406.19154","snapshot_observed_at":"2026-08-11T23:27:39.887822Z","title":"Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet)","venue":"cs.LG","work_id":"33475345-a356-4a23-b046-7280fbd0022d","year":2024},"citing_paper":{"arxiv_id":"2412.02498","last_updated":"2024-12-03T15:21:26Z","snapshot_observed_at":"2026-08-14T23:52:43.902189Z","submitted_at":"2024-12-03T15:21:26Z","title":"Advancing global aerosol forecasting with artificial intelligence","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T23:27:39.814859Z"},"links":{"cited_paper":"/paper/2406.19154","citing_paper":"/paper/2412.02498"},"observation_digest":"sha256:66d510adaaf303edae38ed1e1c8926bde39e7c7483126e685940c868ce82a5cf","observation_id":"afa64f0a-9116-4a84-88ab-b0c667f5a2d2","resolution":{"observed_at":"2026-08-11T23:27:39.891656Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2406.19154/citation-record","integrity":"/paper/2406.19154/integrity","json":"/paper/2406.19154/citation-record.json","paper":"/paper/2406.19154"},"outbound":[],"paper":{"arxiv_id":"2406.19154","last_updated":"2024-06-27T13:14:20Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-19T02:22:12.103264Z","submitted_at":"2024-06-27T13:14:20Z","title":"Advancing operational PM2.5 forecasting with dual deep neural networks (D-DNet)"},"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":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2406.19154."}