{"as_of":"2026-08-13T06:14:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9ff57c2b979db554f7067b9d187e6ee2f2323b72041e37c8951e935539024061","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":"2026-08-12T06:34:41.77262+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":"2026-08-10T20:16:18.360661Z","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":"2026-08-10T20:16:18.789331Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1608.03981","last_updated":"2016-08-13T13:33:28Z","snapshot_observed_at":"2026-08-11T17:11:42.262691Z","submitted_at":"2016-08-13T13:33:28Z","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","version":1},"cited_work":{"arxiv_id":"1608.03981","doi":null,"metadata_source":"pith","pith_arxiv_id":"1608.03981","snapshot_observed_at":"2026-08-10T20:16:18.789331Z","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","venue":"cs.CV","work_id":"fd41ee86-6dbd-4302-9670-5b9c1731307d","year":2016},"citing_paper":{"arxiv_id":"2501.09129","last_updated":"2025-03-17T20:49:43Z","snapshot_observed_at":"2026-08-13T06:03:24.291520Z","submitted_at":"2025-01-15T20:24:18Z","title":"Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-10T20:16:18.360661Z"},"links":{"cited_paper":"/paper/1608.03981","citing_paper":"/paper/2501.09129"},"observation_digest":"sha256:2c714a2028bbf760e9d1bce48a3b72038a3e9c7283cd7e016ea46602fad82754","observation_id":"fbf8e110-dbbd-4166-b83f-e4ff96ea5c9b","resolution":{"observed_at":"2026-08-10T20:16:18.798685Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1608.03981","last_updated":"2016-08-13T13:33:28Z","snapshot_observed_at":"2026-08-11T17:11:42.262691Z","submitted_at":"2016-08-13T13:33:28Z","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1608.03981","snapshot_observed_at":"2026-08-01T05:16:02.524215Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2607.22793","last_updated":"2026-07-24T13:31:22Z","snapshot_observed_at":"2026-08-11T00:55:20.003673Z","submitted_at":"2026-07-24T13:31:22Z","title":"Small, Bias-Free, Blind and Convolutional Denoiser: A compact ConvNeXt U-Net for blind Gaussian color-image denoising","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-01T05:16:02.524215Z"},"links":{"cited_paper":"/paper/1608.03981","citing_paper":"/paper/2607.22793"},"observation_digest":"sha256:a7395748be61c6e57bed8ba5108538e623c1fc5de5de0218df8f90b01d383f0d","observation_id":"8cbf0774-f434-4b62-85c2-688dba7f76c0","resolution":{"observed_at":"2026-08-01T05:16:02.524215Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/1608.03981/citation-record","integrity":"/paper/1608.03981/integrity","json":"/paper/1608.03981/citation-record.json","paper":"/paper/1608.03981"},"outbound":[],"paper":{"arxiv_id":"1608.03981","last_updated":"2016-08-13T13:33:28Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-11T17:11:42.262691Z","submitted_at":"2016-08-13T13:33:28Z","title":"Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1608.03981."}