{"as_of":"2026-08-08T22:20:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:495f680d0bf037b2f7f3cbfcb965afac8b074d32b4305dc9a02883aebcd5e0a9","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":"2026-08-07T14:27:36.103811Z","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":"2026-08-07T14:27:36.978758Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2212.04668","last_updated":"2022-12-09T05:07:43Z","snapshot_observed_at":"2026-08-07T10:29:31.647348Z","submitted_at":"2022-12-09T05:07:43Z","title":"Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds","version":1},"cited_work":{"arxiv_id":"2212.04668","doi":null,"metadata_source":"pith","pith_arxiv_id":"2212.04668","snapshot_observed_at":"2026-08-07T14:27:36.978758Z","title":"Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds","venue":"cs.CV","work_id":"f9167cc1-16ed-4d46-955e-4fe610392d17","year":2022},"citing_paper":{"arxiv_id":"2505.18956","last_updated":"2025-06-10T05:46:10Z","snapshot_observed_at":"2026-08-07T14:20:31.708138Z","submitted_at":"2025-05-25T03:01:28Z","title":"How Do Images Align and Complement LiDAR? Towards a Harmonized Multi-modal 3D Panoptic Segmentation","version":2},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-07T14:27:36.103811Z"},"links":{"cited_paper":"/paper/2212.04668","citing_paper":"/paper/2505.18956"},"observation_digest":"sha256:6e68c35bd880ab3fc5559e65f89028d21a4015eeda8cb072bc1afc8286e98637","observation_id":"a602917e-01fb-4724-8078-aad535591254","resolution":{"observed_at":"2026-08-07T14:27:37.023114Z","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/2212.04668/citation-record","integrity":"/paper/2212.04668/integrity","json":"/paper/2212.04668/citation-record.json","paper":"/paper/2212.04668"},"outbound":[],"paper":{"arxiv_id":"2212.04668","last_updated":"2022-12-09T05:07:43Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T10:29:31.647348Z","submitted_at":"2022-12-09T05:07:43Z","title":"Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds"},"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:2212.04668."}