{"as_of":"2026-08-08T11:38:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b09f51d09e0c76c7e6e9a63338475ab72a16394825033bf5b0b75e1602edd43f","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T13:26:40.528940Z","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":"2026-08-07T13:26:48.871604Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1902.00927","last_updated":"2019-02-19T18:45:10Z","snapshot_observed_at":"2026-07-06T07:30:50.190635Z","submitted_at":"2019-02-03T16:58:19Z","title":"Depthwise Convolution is All You Need for Learning Multiple Visual Domains","version":2},"cited_work":{"arxiv_id":"1902.00927","doi":null,"metadata_source":"pith","pith_arxiv_id":"1902.00927","snapshot_observed_at":"2026-08-07T13:26:48.871604Z","title":"Depthwise Convolution is All You Need for Learning Multiple Visual Domains","venue":"cs.CV","work_id":"f7b1d7a2-e0bb-4fa0-9905-d78d45a02f8c","year":2019},"citing_paper":{"arxiv_id":"2505.21942","last_updated":"2025-05-28T03:52:34Z","snapshot_observed_at":"2026-08-07T13:16:40.291506Z","submitted_at":"2025-05-28T03:52:34Z","title":"Continual Learning Beyond Experience Rehearsal and Full Model Surrogates","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-07T13:26:40.528940Z"},"links":{"cited_paper":"/paper/1902.00927","citing_paper":"/paper/2505.21942"},"observation_digest":"sha256:db5e8b1cce38fd35d67c42fa60a3e30fd457425be17c296289f6bb6b5e7fdb9a","observation_id":"3e8ee4f8-8319-47ac-bed4-8ee8fedc4717","resolution":{"observed_at":"2026-08-07T13:26:48.929420Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1902.00927/citation-record","integrity":"/paper/1902.00927/integrity","json":"/paper/1902.00927/citation-record.json","paper":"/paper/1902.00927"},"outbound":[],"paper":{"arxiv_id":"1902.00927","last_updated":"2019-02-19T18:45:10Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T07:30:50.190635Z","submitted_at":"2019-02-03T16:58:19Z","title":"Depthwise Convolution is All You Need for Learning Multiple Visual Domains"},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1902.00927."}