{"as_of":"2026-08-10T14:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:cc5a42ce35e1ac34a93569087ba71d6e5efdd9b7570e1b0e2b27d928a7b57a47","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:24:12.932909Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-04T02:59:26.813266Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2203.04600","last_updated":"2022-03-09T09:33:59Z","snapshot_observed_at":"2026-08-10T02:15:04.633402Z","submitted_at":"2022-03-09T09:33:59Z","title":"Domain Generalization using Pretrained Models without Fine-tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.04600","snapshot_observed_at":"2026-08-07T00:24:12.932909Z","title":"Domain generalization using pretrained models without fine-tuning.arXiv preprint arXiv:2203.04600,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2506.14146","last_updated":"2025-06-17T03:20:41Z","snapshot_observed_at":"2026-08-08T11:58:36.379408Z","submitted_at":"2025-06-17T03:20:41Z","title":"Collaborative Editable Model","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T00:24:12.932909Z"},"links":{"cited_paper":"/paper/2203.04600","citing_paper":"/paper/2506.14146"},"observation_digest":"sha256:23dcbce2b619d79fa389e4c737e12fd35c1f51d028ad3f0fbdc5ec732c1fa90b","observation_id":"814bb228-c1ca-452d-8f66-1a1340005871","resolution":{"observed_at":"2026-08-07T00:24:12.932909Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.04600","last_updated":"2022-03-09T09:33:59Z","snapshot_observed_at":"2026-08-10T02:15:04.633402Z","submitted_at":"2022-03-09T09:33:59Z","title":"Domain Generalization using Pretrained Models without Fine-tuning","version":1},"cited_work":{"arxiv_id":"2203.04600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.04600","snapshot_observed_at":"2026-07-04T02:59:26.813266Z","title":"arXiv preprint arXiv:2203.04600 (2022) 5","venue":null,"work_id":"f9485034-961e-4cfe-94f2-b71e218b588d","year":2022},"citing_paper":{"arxiv_id":"2605.01667","last_updated":"2026-05-03T01:38:37Z","snapshot_observed_at":"2026-08-02T13:32:28.104876Z","submitted_at":"2026-05-03T01:38:37Z","title":"Deep neural networks with Fisher vector encoding for medical image classification","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T16:22:00.015928Z"},"links":{"cited_paper":"/paper/2203.04600","citing_paper":"/paper/2605.01667"},"observation_digest":"sha256:393f6cbad9d6f6d4420d13bd02a85fbd52f14ebbd64fc28c44a7012dd3387261","observation_id":"093ed1a4-4ab0-47e8-8db3-979337861cb7","resolution":{"observed_at":"2026-05-11T09:00:58.919775Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2203.04600","last_updated":"2022-03-09T09:33:59Z","snapshot_observed_at":"2026-08-10T02:15:04.633402Z","submitted_at":"2022-03-09T09:33:59Z","title":"Domain Generalization using Pretrained Models without Fine-tuning","version":1},"cited_work":{"arxiv_id":"2203.04600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.04600","snapshot_observed_at":"2026-07-04T02:59:26.813266Z","title":"arXiv preprint arXiv:2203.04600 (2022) 5","venue":null,"work_id":"f9485034-961e-4cfe-94f2-b71e218b588d","year":2022},"citing_paper":{"arxiv_id":"2606.20110","last_updated":"2026-06-18T11:34:26Z","snapshot_observed_at":"2026-08-01T19:05:47.270913Z","submitted_at":"2026-06-18T11:34:26Z","title":"FrozenDrive: Zero-Shot Text-Guided Driving Scene Generation and Data Augmentation with Parameter-Free Frozen Diffusion Model","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-26T18:34:56.810124Z"},"links":{"cited_paper":"/paper/2203.04600","citing_paper":"/paper/2606.20110"},"observation_digest":"sha256:eb0b52e35a75b1e8fafd61a570635254fffee09ef790d049b36ed7919a1039e7","observation_id":"d117682a-406b-4142-9dff-7c69e0ebc2dd","resolution":{"observed_at":"2026-07-04T02:59:26.815201Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2203.04600","last_updated":"2022-03-09T09:33:59Z","snapshot_observed_at":"2026-08-10T02:15:04.633402Z","submitted_at":"2022-03-09T09:33:59Z","title":"Domain Generalization using Pretrained Models without Fine-tuning","version":1},"cited_work":{"arxiv_id":"2203.04600","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.04600","snapshot_observed_at":"2026-07-04T02:59:26.813266Z","title":"arXiv preprint arXiv:2203.04600 (2022) 5","venue":null,"work_id":"f9485034-961e-4cfe-94f2-b71e218b588d","year":2022},"citing_paper":{"arxiv_id":"2607.01657","last_updated":"2026-07-02T03:31:34Z","snapshot_observed_at":"2026-07-07T00:07:13.555571Z","submitted_at":"2026-07-02T03:31:34Z","title":"Domain Generalization via Text-Anchored Information Bottleneck","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-03T16:44:58.332510Z"},"links":{"cited_paper":"/paper/2203.04600","citing_paper":"/paper/2607.01657"},"observation_digest":"sha256:01a3ac45fdf21bd796401d18f2721de6d58e633b82f19a2e74b5422d74d82147","observation_id":"25e08dc3-094f-4c84-89d7-126b8d8c67af","resolution":{"observed_at":"2026-07-03T16:48:39.343076Z","resolver_source":"arxiv_id","status":"verified_exact"},"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":"2203.04600","last_updated":"2022-03-09T09:33:59Z","snapshot_observed_at":"2026-08-10T02:15:04.633402Z","submitted_at":"2022-03-09T09:33:59Z","title":"Domain Generalization using Pretrained Models without Fine-tuning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.04600","snapshot_observed_at":"2026-08-01T07:28:34.909431Z","title":"arXiv preprint arXiv:2203.04600 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.21453","last_updated":"2026-07-24T02:14:44Z","snapshot_observed_at":"2026-08-03T14:01:50.133228Z","submitted_at":"2026-07-23T15:55:29Z","title":"Test-Time Scaling via Error Localization","version":2},"reference_index":163,"source":"arxiv_source","source_observed_at":"2026-08-01T07:28:34.909431Z"},"links":{"cited_paper":"/paper/2203.04600","citing_paper":"/paper/2607.21453"},"observation_digest":"sha256:f2161f65e138522bfa88b67c77a1e71f4e2b1af907b5fe9f9ca6bcf5fbe3b91a","observation_id":"543e3f04-0256-4729-a738-556d30607139","resolution":{"observed_at":"2026-08-01T07:28:34.909431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2203.04600/citation-record","integrity":"/paper/2203.04600/integrity","json":"/paper/2203.04600/citation-record.json","paper":"/paper/2203.04600"},"outbound":[],"paper":{"arxiv_id":"2203.04600","last_updated":"2022-03-09T09:33:59Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-10T02:15:04.633402Z","submitted_at":"2022-03-09T09:33:59Z","title":"Domain Generalization using Pretrained Models without Fine-tuning"},"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 5 inbound Pith citation observations for arXiv:2203.04600."}