{"as_of":"2026-08-16T15:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d92c6cec887e407317e690814e3dd63e53a6d4ff7ea02b1d1d11ac3470fb9d59","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:18:51.573875Z","state":"measured"},{"denominator":37,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":37,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/1908.01428/citation-record","integrity":"/paper/1908.01428/integrity","json":"/paper/1908.01428/citation-record.json","paper":"/paper/1908.01428"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"pubmed/2910679","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:18:51.741119Z","title":null,"venue":null,"work_id":"132145b3-6b07-4cec-b76e-2b4110201d6d","year":null},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:50.957508Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:43eb52edba7ddf68a45716850375075bb61f89ba6af583e8bbf6027d031b3d4e","observation_id":"ecb76a60-e291-47b0-9885-9928a6f12c6c","resolution":{"observed_at":"2026-08-14T15:18:51.748608Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.148166Z","title":null,"venue":null,"work_id":"8267d448-350f-487e-ace8-5ec626d148de","year":2012},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.034763Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:5372f062d72f76321d89aa02115db44406f0bc53758bb6c6b65151a64e2b5fa9","observation_id":"0235c4d2-3e05-49bb-9aff-64a3d527549b","resolution":{"observed_at":"2026-08-14T15:18:52.152460Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.135466Z","title":null,"venue":null,"work_id":"2484e73b-6158-4e90-833e-901b8eab286a","year":2013},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.039466Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:af9f0a8f7ea5e3effcd4c8d9b1e20920ce13379b3c5b68d1afffc7fd5036805b","observation_id":"45f7ec88-84f7-4bc4-a609-976f9d83a296","resolution":{"observed_at":"2026-08-14T15:18:52.139918Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.123506Z","title":"Peracha, B","venue":null,"work_id":"57f1f9e5-0c86-4eb5-8249-f3118ed6cec5","year":2013},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.044161Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:1339cdc69406f3f4b82d7ceae282ab2bae96700cbb88ca410a5f2ae02370ce0b","observation_id":"ff39251d-d700-4c5e-bb95-d9d2e40847e7","resolution":{"observed_at":"2026-08-14T15:18:52.127953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.111930Z","title":"Longmuir, K","venue":null,"work_id":"ce3a3e7c-672b-4cb1-865d-b1b3224cd929","year":2009},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.049291Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:54d142bea31ebcbbf390af30964d1e07b41824499cef7267a09d1e00a3244a36","observation_id":"c389ab07-98e0-4680-8617-90872728d046","resolution":{"observed_at":"2026-08-14T15:18:52.116140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.100707Z","title":"Viswanathan, D","venue":null,"work_id":"42d9a607-62ca-4bea-933f-df604bd054c1","year":2003},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.054284Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:db301fde717844e816117788e2a6a7e0c1181f78a9e956ba0d3b3b9451686468","observation_id":"9cf0d068-68f1-4ad6-85bc-2049396a64b3","resolution":{"observed_at":"2026-08-14T15:18:52.104497Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.089017Z","title":"Huang, E","venue":null,"work_id":"3f2cf1f2-af89-4ad7-a67a-267de2cb7e42","year":1991},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.058636Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:42ff00a878d4d7febb3dbf71857eb9284be82c6040805b312fcd6477fadd2c5c","observation_id":"ed388b75-eca9-4b97-b50d-7c5933681b56","resolution":{"observed_at":"2026-08-14T15:18:52.093025Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.076467Z","title":null,"venue":null,"work_id":"0a58c22d-be8f-4dba-8af8-73440b633017","year":2009},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.063246Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:03821e516871daf1a50651ba5b3792a8d54f4bcdf11232dc62e47fd0f9bae2a9","observation_id":"9c8a772c-bb1f-4f29-8b96-fb24f44eb9eb","resolution":{"observed_at":"2026-08-14T15:18:52.080817Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.064540Z","title":null,"venue":null,"work_id":"16e1f3da-45ce-43ed-8af1-ec40bdab7f37","year":2016},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.164187Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:0b61437f5c9be9d62cd55ae2568e10c35b8513819e5609fc692d7bde7475b4de","observation_id":"3c366deb-ee82-4be8-b8e1-f9ab60e8d5f0","resolution":{"observed_at":"2026-08-14T15:18:52.068410Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.052174Z","title":null,"venue":null,"work_id":"aa7cac39-4173-4eac-85cf-4876569e08fa","year":2006},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.225719Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:2037b47b3a46ded6173880e3608c576a5e2c72d79325230546b8968bd8951a96","observation_id":"8607e855-4627-4aa6-9bb1-f7315e9dd241","resolution":{"observed_at":"2026-08-14T15:18:52.056487Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.040873Z","title":null,"venue":null,"work_id":"a9b0d398-a8f6-4e84-8757-5ad97ff76e5c","year":2012},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.230654Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:6ba2b3e0c931d47587988b1f7f68cc2fade2dabed0c26ff72416fe4ac5ae2b9e","observation_id":"e8dff446-a508-4fd2-b2bf-b53f7b812ce6","resolution":{"observed_at":"2026-08-14T15:18:52.044217Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.028867Z","title":null,"venue":null,"work_id":"ccd94589-1344-40c7-9a6b-2ecf14250c69","year":2011},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.234883Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:62bb10b71f59229e99eaa43370be81d726217b00bfb8692246478158a2b1aa50","observation_id":"3dc46967-001e-41f6-8da3-c93cf552a142","resolution":{"observed_at":"2026-08-14T15:18:52.033440Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.014490Z","title":null,"venue":null,"work_id":"6a3bfa0a-2532-4055-8fed-c621824eec55","year":2011},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.239074Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:8b33d286fbad368de5e6420821cec38448e0fda289b8cb870f0c90849e60296a","observation_id":"eed47500-9913-4976-b3c3-77186082a646","resolution":{"observed_at":"2026-08-14T15:18:52.018910Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:52.002179Z","title":"Sugimoto, H","venue":null,"work_id":"88b48327-ae00-4ad0-beef-3e6006853205","year":2013},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.242654Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:126efb4eaf2d15f94a36475d2e5defbf0c05d06e0e7f5176e8a9a5ef19641e32","observation_id":"6e81496e-9c40-4412-9a33-e5e420701ad2","resolution":{"observed_at":"2026-08-14T15:18:52.006668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.988353Z","title":"Bogunovi´ c, Y","venue":null,"work_id":"c93c7034-39f5-47c7-a9ee-086078fb443e","year":2015},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.246468Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:41b83ee39227b3c7c76f372a2885c32aa3124b183d887b9275bbe963ce933a1d","observation_id":"fc72093a-9d55-4970-ac77-4d4ba8267229","resolution":{"observed_at":"2026-08-14T15:18:51.992999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.974944Z","title":null,"venue":null,"work_id":"744fba4e-9aa8-4cc9-a9f5-45cfea5e8eab","year":2015},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.250492Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:ac28975b16eef31cf5490dca2887b48c56a60a38e7a010a9fc665ff803ae9374","observation_id":"4a1d6242-e972-49d9-80e7-d1158643863a","resolution":{"observed_at":"2026-08-14T15:18:51.979795Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.962380Z","title":null,"venue":null,"work_id":"82988a13-4ef3-4e7e-bf53-d4b3d2b537f9","year":2017},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.255036Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:16c7669d4e84b4207351bf9c6b79faa6ed20b1874a1789682016af855d45e6c9","observation_id":"8c51ea49-f13f-44d2-b809-768300cadf3d","resolution":{"observed_at":"2026-08-14T15:18:51.966521Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.333091Z","title":"Krizhevsky, I","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.333091Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:bd602174f971a6353c3a20bc5d1a1a60a516db7d723ad4deb23780bddac17265","observation_id":"ce996616-2a44-45a1-bae9-c25f7dd12c5a","resolution":{"observed_at":"2026-08-14T15:18:51.333091Z","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":"2026-08-14T15:18:51.941031Z","title":"LeCun, Y","venue":null,"work_id":"fe15cf84-158b-4660-93d5-e289232fcba8","year":2015},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.379157Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:dc47f81b0acd85f255b9a5f090299cb90fd106f5dc702223dc6515c942f98c2b","observation_id":"29009ff2-d13f-4407-a1ed-641fff99e811","resolution":{"observed_at":"2026-08-14T15:18:51.945466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.928990Z","title":null,"venue":null,"work_id":"5b4b17a6-4481-4133-97eb-c9ef7a639b2b","year":2010},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.384957Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:eee481b6bb42bd4a499aa7f73fb3ca64d519ddb1d4c8a869677b064963b8625f","observation_id":"f88d79ca-7fea-4133-9cf6-db6dac2e3a25","resolution":{"observed_at":"2026-08-14T15:18:51.933225Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.916433Z","title":"Bengtsson, A","venue":null,"work_id":"6bf2653a-43b6-4108-91bc-2679f305b043","year":2008},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.389600Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:90d41130e67922b525aadcad767d6a20c896f4eaec4f2fc178c28d378607ae49","observation_id":"4cac47d9-36fe-4105-810e-f7e0db9efe6e","resolution":{"observed_at":"2026-08-14T15:18:51.920373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.903542Z","title":"Mansuri, A","venue":null,"work_id":"3dbd4788-3b32-4c93-bd5c-8e223b1270e9","year":2014},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.394075Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:8127c3559a9f85c364253359f8dec6f84cf2c307a64a6bc8d049e2f2dde1d7a9","observation_id":"68931c6b-9c73-4928-a99d-eb596c8911b9","resolution":{"observed_at":"2026-08-14T15:18:51.908037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.887662Z","title":"Yaqub, Visual fields interpretation in glaucoma: a focus on static auto- mated perimetry, Community eye health 25 (79-80) (2012) 1","venue":null,"work_id":"aef88128-8179-4a64-b8d4-aa9b4a2f5df6","year":2012},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.397408Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:14ab556918ea764fab45fd873df0f8e72bd580b529385b346e6c62fde0b7e0fa","observation_id":"9044c79c-7123-4e2f-9cba-afe0d8e2c2ae","resolution":{"observed_at":"2026-08-14T15:18:51.893623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.873243Z","title":null,"venue":null,"work_id":"83633906-7a72-455a-9921-68ea0bb5d4aa","year":2009},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.401481Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:5c747eaa7114b6f7d1bc4b9b9be64d307544a37b685749c1605679a8d91f2a49","observation_id":"79a92c72-0bb5-4462-8520-8f71b4a3f27c","resolution":{"observed_at":"2026-08-14T15:18:51.878733Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.859094Z","title":"Pedregosa, G","venue":null,"work_id":"3f6d8560-d907-456a-8980-1c5766e2ede3","year":2011},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.405773Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:6bfadb959d89b2d64d5deeb90a4d4e80c2d0153600abce9e9b53193503c06eec","observation_id":"6e3f9df1-9189-436f-b587-19e5f3387cfa","resolution":{"observed_at":"2026-08-14T15:18:51.863731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.845487Z","title":"Scholkopf, A","venue":null,"work_id":"22508233-5427-413c-9168-a9211f2db22e","year":2001},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.454807Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:cdbb5b0c328e6e8e21bdf5c0536ec375ab54eaf1dc3cfba1f399376c930b9491","observation_id":"9b321df9-732a-4f2f-9da7-27f5bfd83d02","resolution":{"observed_at":"2026-08-14T15:18:51.849437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.833214Z","title":null,"venue":null,"work_id":"fd7515c2-a59d-44ad-b1e4-5037823d2851","year":1986},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.500561Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:fce60ee852b7cdcc33e9b5fd8deec89040800792432c09ff9799d49d833dbddb","observation_id":"27cb6e56-96a2-44e0-8226-2a592ebaf4fd","resolution":{"observed_at":"2026-08-14T15:18:51.836597Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.505995Z","title":"Breiman, Random forests, Machine learning 45 (1) (2001) 5–32","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.505995Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:372a80e6cfb237a46f99c4af97658f8288b06954b3ee7d6fc9dc0b7e75d7f95d","observation_id":"90e577fc-e0ad-4598-b966-19d3ccb60b52","resolution":{"observed_at":"2026-08-14T15:18:51.505995Z","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":"2026-08-14T15:18:51.812448Z","title":"Natekin, A","venue":null,"work_id":"090c1361-0395-4d8d-9622-13d5f9fea2cb","year":2013},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.510448Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:f1cdafb28e291b4c4e8dea1384511a0ae37b4e80750ada34f5f49ca960ab3f88","observation_id":"ad9b5917-db8d-44b3-9dea-ed9dcbc230ca","resolution":{"observed_at":"2026-08-14T15:18:51.816413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.798990Z","title":"Geurts, D","venue":null,"work_id":"36b09519-bd5b-44cb-947b-73888cfa7b40","year":2006},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.514251Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:c68eb40328adccb3f0eeb25a0d24e990c8356b2151b78c0aafa61e6374726516","observation_id":"6ed5d958-3249-4d9d-b6ce-b5366eae304b","resolution":{"observed_at":"2026-08-14T15:18:51.803628Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.786280Z","title":"Maetschke, B","venue":null,"work_id":"889d5db4-15e8-4cd1-8cff-bfe5dc76b74e","year":2019},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.518304Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:6d08a879a5da0020c88e3a8ca2fc43bf1694ac34eefb284bf1934469c69dd69e","observation_id":"28d0d50e-5fd3-4a67-9162-26b9d421f614","resolution":{"observed_at":"2026-08-14T15:18:51.790098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.771452Z","title":"Tompson, R","venue":null,"work_id":"94a5e47c-6b32-4471-be31-1e4e7f9578df","year":2015},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.522375Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:330bd57bbcd1848334624984d13256ba677c01d72bd61994881d33b2943ca4ec","observation_id":"f1681a79-491a-401b-aedd-d94f941ad3d9","resolution":{"observed_at":"2026-08-14T15:18:51.776586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T15:18:51.758597Z","title":"Chollet, Keras (2017)","venue":null,"work_id":"59c2d7e9-2489-40b0-a739-2f413fa2d5a6","year":2017},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.526582Z"},"links":{"citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:f768ea058bdc4fd2c96fff73929b79084fd89253d653db33fdc661059e2be32b","observation_id":"c2c44f7b-569f-4795-97fc-648c11825ebb","resolution":{"observed_at":"2026-08-14T15:18:51.762959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1512.04150","last_updated":"2015-12-14T01:32:33Z","snapshot_observed_at":"2026-08-14T22:18:44.850358Z","submitted_at":"2015-12-14T01:32:33Z","title":"Learning Deep Features for Discriminative Localization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1512.04150","snapshot_observed_at":"2026-08-14T15:18:51.530431Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.530431Z"},"links":{"cited_paper":"/paper/1512.04150","citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:4d49a8f1959054d3e8623c21f428163aef597213b51f30bd047fb19d43ba9f3e","observation_id":"1e7421fc-9467-449d-8502-5155e6402bd4","resolution":{"observed_at":"2026-08-14T15:18:51.530431Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1603.04467","last_updated":"2016-03-16T16:57:12Z","snapshot_observed_at":"2026-08-14T22:06:04.800050Z","submitted_at":"2016-03-14T20:50:20Z","title":"TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.04467","snapshot_observed_at":"2026-08-14T15:18:51.563254Z","title":"Abadi, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.563254Z"},"links":{"cited_paper":"/paper/1603.04467","citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:41eb3e10e9d05ae9de5b8e920dc3693003b16c0c33c02630dfceb5923875851a","observation_id":"e4146025-3a10-4368-b212-ffdef5c16e15","resolution":{"observed_at":"2026-08-14T15:18:51.563254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.06046","last_updated":"2018-01-10T00:46:13Z","snapshot_observed_at":"2026-08-14T20:40:22.713070Z","submitted_at":"2017-08-21T01:28:37Z","title":"nuts-flow/ml: data pre-processing for deep learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.06046","snapshot_observed_at":"2026-08-14T15:18:51.568772Z","title":"Maetschke, R","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.568772Z"},"links":{"cited_paper":"/paper/1708.06046","citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:73bba96b63d93acc5fb38ea937324614ff5440852f321269b6db643644ab22d9","observation_id":"187a2bbc-1f91-4157-82bc-c3121d16855f","resolution":{"observed_at":"2026-08-14T15:18:51.568772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.09412","last_updated":"2018-04-27T21:39:25Z","snapshot_observed_at":"2026-08-08T10:28:19.597631Z","submitted_at":"2017-10-25T18:30:49Z","title":"mixup: Beyond Empirical Risk Minimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.09412","snapshot_observed_at":"2026-08-14T15:18:51.573875Z","title":"Zhang, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning","version":3},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T15:18:51.573875Z"},"links":{"cited_paper":"/paper/1710.09412","citing_paper":"/paper/1908.01428"},"observation_digest":"sha256:918d1b7a3c9b7370aeb1036ede317dbd73fdebae0f95921491ad3bd78a89f4ca","observation_id":"b987e666-3cd5-4164-b68e-3a963c3a9976","resolution":{"observed_at":"2026-08-14T15:18:51.573875Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.01428","last_updated":"2019-10-10T04:43:27Z","latest_version":3,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-16T06:28:53.670452Z","submitted_at":"2019-08-05T00:35:18Z","title":"Inference of visual field test performance from OCT volumes using deep learning"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":1,"verified_fuzzy":17},"total_outbound_references":37},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:1908.01428."}