{"as_of":"2026-08-10T19:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7bf8593a573b528d0b6d8cf057f94a0d9ed7fc5e52a3609090c3cfe9b3567248","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":"2026-08-10T06:31:04.303077+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":"2026-08-07T00:31:46.255816Z","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":"2026-08-07T00:31:46.506434Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2006.04139","last_updated":"2020-06-22T12:19:51Z","snapshot_observed_at":"2026-08-10T17:41:37.278228Z","submitted_at":"2020-06-07T12:55:34Z","title":"Learning Texture Transformer Network for Image Super-Resolution","version":2},"cited_work":{"arxiv_id":"2006.04139","doi":null,"metadata_source":"pith","pith_arxiv_id":"2006.04139","snapshot_observed_at":"2026-08-07T00:31:46.506434Z","title":"Learning Texture Transformer Network for Image Super-Resolution","venue":"cs.CV","work_id":"a959d496-e41b-41e0-a368-1e95e3247abd","year":2020},"citing_paper":{"arxiv_id":"2506.13756","last_updated":"2025-06-16T17:58:29Z","snapshot_observed_at":"2026-08-08T23:29:03.208860Z","submitted_at":"2025-06-16T17:58:29Z","title":"UltraZoom: Generating Gigapixel Images from Regular Photos","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T00:31:46.255816Z"},"links":{"cited_paper":"/paper/2006.04139","citing_paper":"/paper/2506.13756"},"observation_digest":"sha256:34f4089f6cb45dedd29b86e66b6c35705ae75cd67cd69d7c687e694de542552e","observation_id":"173f6350-717e-42e7-bc14-258859395c3b","resolution":{"observed_at":"2026-08-07T00:31:46.511610Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04139","last_updated":"2020-06-22T12:19:51Z","snapshot_observed_at":"2026-08-10T17:41:37.278228Z","submitted_at":"2020-06-07T12:55:34Z","title":"Learning Texture Transformer Network for Image Super-Resolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.04139","snapshot_observed_at":"2026-07-31T06:40:45.607163Z","title":"arXiv:2006.04139 [cs.CV] https://arxiv.org/abs/2006.04139","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2607.24729","last_updated":"2026-07-27T17:57:00Z","snapshot_observed_at":"2026-08-10T00:01:21.884888Z","submitted_at":"2026-07-27T17:57:00Z","title":"MicroZoom: Structure-Preserving Detail Synthesis at Extreme Scale","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-07-31T06:40:45.607163Z"},"links":{"cited_paper":"/paper/2006.04139","citing_paper":"/paper/2607.24729"},"observation_digest":"sha256:8f9ed592af4b7b6a8547c8f0c274bfd4801d23c5582bc326a14d61d30830b7f3","observation_id":"ecca94ac-7dbe-4915-b4bd-069fdd850bb9","resolution":{"observed_at":"2026-07-31T06:40:45.607163Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2006.04139/citation-record","integrity":"/paper/2006.04139/integrity","json":"/paper/2006.04139/citation-record.json","paper":"/paper/2006.04139"},"outbound":[],"paper":{"arxiv_id":"2006.04139","last_updated":"2020-06-22T12:19:51Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T17:41:37.278228Z","submitted_at":"2020-06-07T12:55:34Z","title":"Learning Texture Transformer Network for Image Super-Resolution"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2006.04139."}