{"as_of":"2026-08-12T19:29:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b9ab29edae30f73f03af17714c05c4081235f9b9d6b5314ab81f72aafa8d2e62","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T23:23:43.122614Z","state":"measured"},{"denominator":30,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":30,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.06660/citation-record","integrity":"/paper/2509.06660/integrity","json":"/paper/2509.06660/citation-record.json","paper":"/paper/2509.06660"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:40.804572Z","title":"Monitoring of benthic reference sites: Using an autonomous underwater vehicle,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:40.804572Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:de0940a4a11c95367bf49a2e30a381388e686a888048da61c861d383ecafb2a1","observation_id":"24788c68-ad5a-4444-8e82-10f490c959d0","resolution":{"observed_at":"2026-08-04T23:23:40.804572Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:46.341407Z","title":"A survey on contrastive self-supervised learning,","venue":null,"work_id":"25d4b9a3-bd65-4a40-a380-095178f025d1","year":2021},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:40.856851Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:555ecf0193eaf961019fb7574c4441f6192c75b9b1c0dd8f8ccaed436800f0b2","observation_id":"15dc3e6c-e54f-41cb-a13e-97b1d7916aa7","resolution":{"observed_at":"2026-08-04T23:23:46.362584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:46.291909Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":"641e9455-de44-4adf-a71d-833406250074","year":2020},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:40.922972Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:e6a7d91f87e51a5730a98ee23dc0e18994070ec16206f297d627216fd27052f4","observation_id":"e60bbb10-b4d3-4a3b-ae25-675682981446","resolution":{"observed_at":"2026-08-04T23:23:46.313063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:46.212044Z","title":"ugel-Bennett, and B. Thornton, “Learning features from georeferenced seafloor imagery with location guided autoen- coders,","venue":null,"work_id":"6fb55aeb-cbb3-4a39-acbe-03d4bebfd312","year":2021},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.025207Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:f9dfc0e00d741fd821b8803083aecfc5019f33a1f651685ce9bdec55786d500b","observation_id":"f5ab8c1b-ddbc-4fb5-8ed3-fe59834cb6f7","resolution":{"observed_at":"2026-08-04T23:23:46.268568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:46.091294Z","title":"ugel-Bennett, S. B. Williams, O. Pizarro, and B. Thornton, “Geoclr: Georeference contrastive learning for efficient seafloor image interpretation,","venue":null,"work_id":"a767188b-4275-4ecf-ae31-eacde2792e72","year":2022},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.124937Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:5633431c92185f772f35f9a02d219d9172a6f6b12e4ae95f94f30c07726d5627","observation_id":"fc6c407e-d819-4170-8297-e0582a85be5a","resolution":{"observed_at":"2026-08-04T23:23:46.166596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:46.000087Z","title":"Momentum contrast for unsupervised visual representation learning,","venue":null,"work_id":"e0ee601f-0ffc-4abf-8aed-64391b34ea7d","year":2020},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.201097Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:5f144c93628f3f98112dd6e297f229cfbbb71a03ba6bf8207726bbd17436e5a6","observation_id":"5e247048-7c4d-47ed-8052-c27b77147a01","resolution":{"observed_at":"2026-08-04T23:23:46.054317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:45.860022Z","title":"Self-supervised learning with data augmentations provably isolates content from style,","venue":null,"work_id":"bfe3036e-433d-401d-a5ed-7d86b2b5728a","year":2021},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.342042Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:0c0dc7ac120619470b0f5e72c164b3b80038cd28b6b386317febe6ae2bc6788e","observation_id":"8bc377fa-151b-487e-a23a-6409a6b25e7b","resolution":{"observed_at":"2026-08-04T23:23:45.912493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:45.771062Z","title":"Emerging properties in self-supervised vision transformers,","venue":null,"work_id":"c61ea1d0-6537-40dd-803e-af1125c98cdc","year":2021},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.428562Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:8bc504127c2bb60116593e669f70101767aa72f545c9b025227e6daa31271865","observation_id":"485aead5-9d41-4f3d-b830-3c09d886eb29","resolution":{"observed_at":"2026-08-04T23:23:45.802857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:45.585510Z","title":"Guiding labelling effort for efficient learn- ing with georeferenced images,","venue":null,"work_id":"5c329b2b-4042-4234-8843-6fab4f4b47e4","year":2023},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.505958Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:533a1d801f404082c8795fd1f6ba8073619ddc2135dab48449144a0960e70af1","observation_id":"790962a4-7850-4819-aaa3-c81448fc1047","resolution":{"observed_at":"2026-08-04T23:23:45.690024Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:41.566403Z","title":"Assessing the repeatability of automated seafloor classification algorithms, with application in marine protected area monitoring,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.566403Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:6ab218274da37b93649befe9d14935bc3cbf7c921e6cc5fc3e6e3853b3d54a4f","observation_id":"9f38eff5-2e80-46e9-9904-46700baf2dbb","resolution":{"observed_at":"2026-08-04T23:23:41.566403Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:45.399070Z","title":"Exploring simple siamese representation learning,","venue":null,"work_id":"a460fa99-3721-4828-a771-33ced798c852","year":2021},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.620543Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:fbdfb097086a9ab573c6a944cc1759dc0dac527c2eebf4f69d63a28c890275c5","observation_id":"3a795e86-333c-4403-a3bb-0f3062f93ad7","resolution":{"observed_at":"2026-08-04T23:23:45.521055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:45.222945Z","title":"Unsupervised learning of visual features by contrasting cluster assign- ments,","venue":null,"work_id":"b04931ad-7d2d-409e-a6b7-9a2460804009","year":2021},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.708937Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:8117ef41529c5edbc72ce98458931fec551e3e416f8b888ab1587c60a9557e18","observation_id":"b82116a1-06e1-4df5-9991-791b11097b41","resolution":{"observed_at":"2026-08-04T23:23:45.314396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:45.064698Z","title":"Deep clustering for unsupervised learning of visual features,","venue":null,"work_id":"9fc56a52-996f-4bfe-bd50-435a8564c663","year":2018},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.820304Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:c1ba2b26af8d469831222a2eba0b03e9303e81be92dab9492b7184e209e4037c","observation_id":"9cef5bd2-7734-4985-b681-e7982d358155","resolution":{"observed_at":"2026-08-04T23:23:45.150571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:44.917128Z","title":"A survey on self-supervised learning: Algorithms, applications, and future trends,","venue":null,"work_id":"162d8bff-d43f-4773-b906-a5a66b963e39","year":2024},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.880415Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:2b0e3af97852aed36a96aac4cd036729830fd544a51a52fcbe28229cbb908118","observation_id":"81a574a1-73e9-41c6-932e-ae4f3526513b","resolution":{"observed_at":"2026-08-04T23:23:44.968295Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:41.966444Z","title":"BERT: Pre- training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:41.966444Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:4e566b624a403fc996de12f8a677347d31eef0a04eec1bc85e6a6cff313b645c","observation_id":"55d18dad-4817-4225-a259-1478b8846ae7","resolution":{"observed_at":"2026-08-04T23:23:41.966444Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-04T23:23:42.064489Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.064489Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:f1d5d1dc316cb99e4a6b91410c62fc9e7eaa715587d3542b7ccb807bdafd3f90","observation_id":"f2edfa42-0257-4766-a109-11c94e30a569","resolution":{"observed_at":"2026-08-04T23:23:42.064489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:44.714177Z","title":"Self-supervised learning: Generative or contrastive,","venue":null,"work_id":"a631ecbc-1ee3-415b-9179-d14ca12c6d36","year":2023},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.136052Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:5323f63739e71f26fa0008ec6d308e53042b99ad0f5b215caafd578ae4499e62","observation_id":"7fe02e53-4996-4951-888e-ab02ae4f91e6","resolution":{"observed_at":"2026-08-04T23:23:44.747451Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:44.556993Z","title":"Advancing surface defect detection: A review of self- supervised learning approaches,","venue":null,"work_id":"098d32f1-5177-4677-a96f-fd2349d638f7","year":2025},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.216068Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:85dff3703faf83275c43bb09dc7a6cd8abfa8138d4f242e3091f6fc9979ed047","observation_id":"d4219626-6292-4258-bfe2-d5f37f2c473a","resolution":{"observed_at":"2026-08-04T23:23:44.624768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:44.453166Z","title":"Concerning nonnegative matrices and doubly stochastic matrices,","venue":null,"work_id":"425e421a-e068-45c2-a270-c8620b2f2b2e","year":1967},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.269895Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:65bd9ff5030d1842e95676c8c3b80cd3b87c8e88612b26af2313f853da828953","observation_id":"42b9271c-b0b0-4ec6-a4e4-464f3c73874c","resolution":{"observed_at":"2026-08-04T23:23:44.517828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2306.04654","last_updated":"2023-06-06T15:04:45Z","snapshot_observed_at":"2026-08-05T11:48:08.573824Z","submitted_at":"2023-06-06T15:04:45Z","title":"DenseDINO: Boosting Dense Self-Supervised Learning with Token-Based Point-Level Consistency","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.04654","snapshot_observed_at":"2026-08-04T23:23:42.352001Z","title":"Densedino: Boosting dense self- supervised learning with token-based point-level consistency,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.352001Z"},"links":{"cited_paper":"/paper/2306.04654","citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:fe9d37118db9f678fde0124aad47a229c47ae23e77d8f5ef989309fba1637dd1","observation_id":"5709b809-1e17-4271-b264-af5ea756a584","resolution":{"observed_at":"2026-08-04T23:23:42.352001Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:44.345677Z","title":"Bootstrap your own latent: A new approach to self-supervised learning,","venue":null,"work_id":"592e69af-45f4-4dc8-800a-37d3bafc0c4c","year":2020},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.407688Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:d4ecf087630b36e556e22bfb81863460636cfcc08c82f03a2a86311a75b4f58a","observation_id":"18fa7d9c-29f6-4fb5-9a89-53a248e5c5c5","resolution":{"observed_at":"2026-08-04T23:23:44.380655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:44.178259Z","title":"Transformers in vision: A survey,","venue":null,"work_id":"f426bd9a-0ee0-4e3a-ad3e-651b06c49496","year":2022},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.463489Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:c8108525ecece66911563dbbecc083af3e4bc9ea0bd97a5a5f89bda0829c8e86","observation_id":"0b753110-82a9-4d4d-a7ed-132fa4c1764e","resolution":{"observed_at":"2026-08-04T23:23:44.270557Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-11T10:12:11.384939Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-04T23:23:42.575343Z","title":"Dinov2: Learning robust visual features without supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.575343Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:000069149d85d9d873d774c97bb442c9b0f4ab926470d873982049ff44441f6e","observation_id":"652eac85-0e28-47a2-8853-6f814584ed88","resolution":{"observed_at":"2026-08-04T23:23:42.575343Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:44.071715Z","title":"Unsupervised learning of dense visual representations,","venue":null,"work_id":"a915ab98-1988-4f6e-9f51-708b3f3eeef8","year":2020},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.625997Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:8f38be40e3df7d9dfcdff015fd4d1e0cf4441ba945ae69b6a72c031e6a2bd750","observation_id":"e51e84bd-3d7d-4431-861e-d79b5c90009d","resolution":{"observed_at":"2026-08-04T23:23:44.095966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/rob.20324","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Generation and visualization of large-scale three-dimensional reconstructions from underwater robotic surveys,","venue":"Journal of Field Robotics","work_id":"998264fc-18bd-4c99-bd81-dfc1a9f5b56a","year":2010},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.720144Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:3236c4282516bbffc58b41f1fe276c6a0fd24ac7f7d4afb111552b959841beb4","observation_id":"71320ff9-71ff-43b4-9471-7494b471c708","resolution":{"observed_at":"2026-08-04T23:23:43.446120Z","resolver_source":"doi","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:42.800298Z","title":"Self-supervised learning with multimodal remote sensed maps for seafloor visual class inference,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.800298Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:d66e169bf87e3fa252e2c9ce5ae68d9b29a41c1168a27e707d5005740985f0c9","observation_id":"92296d1e-6c86-4fae-923e-4a22a405f9e3","resolution":{"observed_at":"2026-08-04T23:23:42.800298Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1029/2023ea003220","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Leveraging spatial metadata in machine learning for improved objective quantification of geological drill core,","venue":"Earth and Space Science","work_id":"64e72a68-1cdc-47cb-ba5d-93be8a7f8fac","year":2024},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.883660Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:0d9192ab915ff236397c2dd314cd8877ebe6f9c01afd84d93cc2fe24c29c9c42","observation_id":"649e8cfe-7cfc-4e2d-8e1c-3555aa42db10","resolution":{"observed_at":"2026-08-04T23:23:43.286809Z","resolver_source":"doi","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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:43.898809Z","title":"3D CNN-PCA: A deep-learning-based parameterization for complex geomodels,","venue":null,"work_id":"1bcffab3-d9de-45df-857d-412edaf5b1c5","year":2021},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:42.952290Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:6ccb881c770d4deecc7fec9e65429803870da0e36332c6f04face25bb460abf4","observation_id":"a83e598e-122b-4a65-9e9c-a29129562585","resolution":{"observed_at":"2026-08-04T23:23:43.992908Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:43.766940Z","title":"Compact DINO- ViT: Feature reduction for visual transformer,","venue":null,"work_id":"b00baab3-3696-4978-b7cc-08a54597873e","year":2024},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:43.037057Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:eabffc1f2c816b4e0ae35b757cdf0b30ca33d29112cec777fd220c1fe384a0e3","observation_id":"570039a7-6fbc-4494-a836-117dbc87dae0","resolution":{"observed_at":"2026-08-04T23:23:43.853513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T23:23:43.619273Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":"04f1e79f-df2a-4ffc-8dff-c29ca171a67e","year":2019},"citing_paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-04T23:23:43.122614Z"},"links":{"citing_paper":"/paper/2509.06660"},"observation_digest":"sha256:af234abfb5af132901bbeed292cf284b6b0dd44d46afa95b3ce8ffc576e94b95","observation_id":"f2161e69-e356-43b2-8517-40537a896854","resolution":{"observed_at":"2026-08-04T23:23:43.672167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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"}}],"paper":{"arxiv_id":"2509.06660","last_updated":"2025-09-08T13:19:04Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-06T06:54:16.886093Z","submitted_at":"2025-09-08T13:19:04Z","title":"Investigating Location-Regularised Self-Supervised Feature Learning for Seafloor Visual Imagery"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":2,"verified_fuzzy":21},"total_outbound_references":30},"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 12 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 0 inbound Pith citation observations for arXiv:2509.06660."}