{"as_of":"2026-08-15T10:25:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:255fbb5883b9761403745906ad6724a75f5f16ba898e9f14c91ad07c49763971","coverage":[{"denominator":26,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":26,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T19:57:12.338110Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T11:20:33.347093Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T11:20:44.023401Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"cited_work":{"arxiv_id":"2508.11462","doi":null,"metadata_source":"pith","pith_arxiv_id":"2508.11462","snapshot_observed_at":"2026-08-05T11:20:44.023401Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","venue":"astro-ph.HE","work_id":"7d8723d9-5288-4d87-abee-09256e3e2072","year":2025},"citing_paper":{"arxiv_id":"2509.03001","last_updated":"2025-09-03T04:23:45Z","snapshot_observed_at":"2026-08-13T05:42:09.292668Z","submitted_at":"2025-09-03T04:23:45Z","title":"Low-frequency observations of low-mass binary systems with neutron star candidates","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-05T11:20:33.347093Z"},"links":{"cited_paper":"/paper/2508.11462","citing_paper":"/paper/2509.03001"},"observation_digest":"sha256:849700ec7655374899794d246ff7831dac3339e2c3c8f08463580fa584caba3b","observation_id":"12fd2b17-d869-4a89-950b-29e2455ac452","resolution":{"observed_at":"2026-08-05T11:20:44.028091Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2508.11462/citation-record","integrity":"/paper/2508.11462/integrity","json":"/paper/2508.11462/citation-record.json","paper":"/paper/2508.11462"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T19:57:12.630361Z","title":"Deep learning–based segmentation of glomerular basement membrane in electron microscopy images,","venue":null,"work_id":"1f0709e9-e92e-43c3-bc7c-d7755e112acd","year":2020},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:11.742417Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:9eaf4c2c91e66fc277635bed94bd8a2259e5d10c762d9eda2f4363816fec80c2","observation_id":"11f953c2-156e-47ff-a888-1b01f6ad2d0c","resolution":{"observed_at":"2026-08-05T19:57:12.634331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.619516Z","title":"Glomerular basement membrane thickness in diabetic nephropathy: a stereo- logical study,","venue":null,"work_id":"edd3321e-5d36-4bfc-b474-7fc305ee05b5","year":1992},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:11.860731Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:4cf6e5838b09bee358b24b711e753eead93ec7e08d694e3b02a4edb11cbe0559","observation_id":"0514c97a-4d77-40ac-a0c1-6cb158df3b54","resolution":{"observed_at":"2026-08-05T19:57:12.623060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.609886Z","title":"The glomerular filtration barrier: ultrastructure and functional implications,","venue":null,"work_id":"dddd4bcf-1892-4fa4-9cea-de6041bbbda3","year":2004},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:11.945223Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:e749ff5ab0955670f09a803e8a0e75a41330f64feab340ef7bc08cbc0eb48f79","observation_id":"58ae861b-5e2d-449f-9fb5-b1c27d396195","resolution":{"observed_at":"2026-08-05T19:57:12.613208Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.600139Z","title":"Alport’s syndrome, goodpasture’s syn- drome, and type iv collagen,","venue":null,"work_id":"d3ca5a22-801f-430c-ba04-bc709d777a77","year":2003},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.044620Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:2e7226debcc58302dd8182b496d45afb323cd7d9421675373a3100ba2ce2b9e5","observation_id":"464be930-9f60-4c92-8636-b6177aff4c94","resolution":{"observed_at":"2026-08-05T19:57:12.603625Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.589601Z","title":"The ultrastructural disruption of the glomerular basement membrane in diabetic nephropathy revealed by “tissue negative staining method","venue":null,"work_id":"7f63a27c-8353-4448-8cf7-932a9cb4ac88","year":1995},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.127196Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:a598c02b5b4f5e5fc34be5e3b9099e11e3830fe1000b6c5249771612778b3ab9","observation_id":"b9a4f407-2dee-4eed-883d-fe055f829cbd","resolution":{"observed_at":"2026-08-05T19:57:12.592979Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.578679Z","title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,","venue":null,"work_id":"ec2cbff9-d080-4ff3-a1e5-d48850161c0b","year":2021},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.134679Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:583f3708c00732c08bd56c722c953eacfd4f04725464b0bb979f8b46046009af","observation_id":"65e3124e-0b62-42ae-aa52-6f7bf575d992","resolution":{"observed_at":"2026-08-05T19:57:12.582394Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.568561Z","title":"Deep learning-based morphological feature extraction for kidney pathology,","venue":null,"work_id":"c1cdb3e5-c783-43bb-bfeb-04a2a8f58a29","year":2020},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.261651Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:f4a5d85d4cead12859a1747c7b9389b4452c7d44a94333512be193fe59c322bf","observation_id":"e35c8d42-61b3-42af-888b-7d791120451e","resolution":{"observed_at":"2026-08-05T19:57:12.571934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.558074Z","title":"A novel approach to the classification of glomerular diseases: the nephrotic syndrome study network,","venue":null,"work_id":"f5db89e8-2079-439b-9771-cf8fa16c93bb","year":2020},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.278949Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:b5a519f207c0bcdb36f0c977c5a123b9a80e65db71ce67a3270a8cc7c21aefe8","observation_id":"264ea701-e9d9-42a9-a730-140988bd0d63","resolution":{"observed_at":"2026-08-05T19:57:12.561731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.548477Z","title":"A survey on deep learning in medical image analysis,","venue":null,"work_id":"46806f51-cf09-42af-8a3f-ea53740033d2","year":2017},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.282528Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:3977835b7f92580feefc91a849b91a69782bf267ebc118e69a937bfac927c20e","observation_id":"41c559e7-7e44-4bcc-a0ac-e8f03d337420","resolution":{"observed_at":"2026-08-05T19:57:12.551870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.538225Z","title":"Prototypical networks for few-shot learning,","venue":null,"work_id":"0c1724e5-8fb8-461a-b73b-45e66b6c5257","year":2017},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.285707Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:bf9bebc54a326b97a84feae4c1cb4d49ca91399788296a5f440f2f495deceae6","observation_id":"99563864-951e-417e-98c8-4adf6b1b5626","resolution":{"observed_at":"2026-08-05T19:57:12.541825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.528755Z","title":"One-shot learning for semantic segmentation,","venue":null,"work_id":"a1e2cb99-f7dd-415b-ba87-1a05dc6cd7d4","year":2017},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.288893Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:9e896b52f69ea72876705b36ec2123ae933c5de82301470694cf6c2ae37ecdd0","observation_id":"e87273e3-c3a4-49ac-8757-46531e420144","resolution":{"observed_at":"2026-08-05T19:57:12.531804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.518877Z","title":"Generalizing from a few examples: A survey on few-shot learning,","venue":null,"work_id":"97ade7b9-a63a-48d1-be41-9af0d64d7f0f","year":2020},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.291838Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:547620c0aa25ebb27ad6ae3c4aea3aabca29edfdcc096a25ec0403ace7f855c7","observation_id":"8cd9ad61-64dc-4cad-8728-6541459603bc","resolution":{"observed_at":"2026-08-05T19:57:12.522599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.508781Z","title":"Segment anything model 2 (sam-2): Scaling up zero-shot image segmentation,","venue":null,"work_id":"465d383f-7c3c-4b4e-b95a-d54e0e377362","year":2025},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.294716Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:1f6210bae96cb5e5e0bb0ef999e3112070fccc664050a213e4fced2a76f1fb35","observation_id":"17117637-d6a9-4d55-a620-2371be92a988","resolution":{"observed_at":"2026-08-05T19:57:12.512063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.497288Z","title":"All-in- sam: from weak annotation to pixel-wise nuclei segmentation with prompt-based finetuning,","venue":null,"work_id":"1b826031-a863-4f99-9de3-d2e4122a5a6a","year":2024},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.297699Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:8d5e21d58b571f8b154a42118a3f714ac0b277497f4b60da3a904379cc4218bf","observation_id":"f8f6234b-54cd-4e73-9e9a-3ddf79610cc4","resolution":{"observed_at":"2026-08-05T19:57:12.502272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.487708Z","title":"Sam-med2d: Segment anything model for medical image segmentation,","venue":null,"work_id":"3d1e0902-2049-4002-a771-543706baec4a","year":2024},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.301320Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:c6b5e8499fa594cd5623b7956c43de5e56aff1210398a8f1c6bd96ec9b8bca3e","observation_id":"f9689225-88aa-4cb6-b963-30716c273333","resolution":{"observed_at":"2026-08-05T19:57:12.490963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.477423Z","title":"Leverage weekly annotation to pixel-wise annotation via zero-shot segment anything model for molecular-empowered learning,","venue":null,"work_id":"18a71801-c449-4218-92c2-f964f0535ea3","year":2024},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.304132Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:eac83ea151a53829ce51c23af2fb1efac60644fde86cfc6e00dfbeb9e66291dc","observation_id":"793aaf7e-de31-4ca2-9130-d36646b32019","resolution":{"observed_at":"2026-08-05T19:57:12.480981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.467144Z","title":"Can sam segment medical images? an extensive benchmark study on 12 datasets,","venue":null,"work_id":"f25c1a45-3f8d-4aa2-a941-722ca99071e1","year":2023},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.307131Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:989f2c4c888c92cde070dc9c642f87929bca597e6ae96e94e37b0673cc50ed93","observation_id":"a460a475-0739-4779-a40f-b97d96f69df2","resolution":{"observed_at":"2026-08-05T19:57:12.470499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.457095Z","title":"Prompt, segment, and learn: Boosting medical image segmentation with segment anything model,","venue":null,"work_id":"fff9f387-834a-4725-a19a-44c17ffdfafa","year":2023},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.310680Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:f7bb198dd99a70d0a275022a16996a7cb4a85b9ec489eae236e97b2e6de8a40e","observation_id":"b2bfdaa3-d545-48a9-92ba-072df1bcf8cc","resolution":{"observed_at":"2026-08-05T19:57:12.460710Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.446609Z","title":"Prompt-to-prompt image segmentation with foundation models,","venue":null,"work_id":"949f00bf-1c72-4c71-bb7a-404059c192ab","year":2023},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.313827Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:04e001ffd52978c6740078d6853453e0dc8df67d51fe7e61fd80855c10b96eef","observation_id":"c77d7376-74ee-46b2-8fa1-95b33013ac10","resolution":{"observed_at":"2026-08-05T19:57:12.450222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.436443Z","title":"Sam �: Prompt learning for efficient interactive segmentation,","venue":null,"work_id":"a5399b6d-59b0-4608-89f5-2d65cf399896","year":2023},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.317391Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:5ade286992ceb8326dd457916467fc9a5ec6706903cbd116ae83f384c44ac45c","observation_id":"f12ccb7e-d5c6-4166-9f90-0989cabbba4d","resolution":{"observed_at":"2026-08-05T19:57:12.439939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.424666Z","title":"U-net: Convolutional networks for biomedical image segmentation,","venue":null,"work_id":"110195f4-daa4-4955-ae62-eb089684328c","year":2015},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.320515Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:afc23f1adb8637e7a7efa472676304f8b4d998548c36a112e666b4b507dcce9c","observation_id":"3cba44a0-776c-4078-8d17-4090e0307b53","resolution":{"observed_at":"2026-08-05T19:57:12.428657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.413348Z","title":"Swin unetr: Swin trans- formers for semantic segmentation of brain tumors in mri images,","venue":null,"work_id":"6aa291be-9a70-4ac0-9364-594e82a05256","year":2022},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.324276Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:3d3e99c26c47913fec2d69c4d97c99a8f48c2da19fd9b945864e1e90a5f1a8ce","observation_id":"a7444d28-f183-4013-b606-43b140d1dd84","resolution":{"observed_at":"2026-08-05T19:57:12.417042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.402250Z","title":"Rethinking atrous convolution for semantic image segmen- tation,","venue":null,"work_id":"0f41467b-df99-4c26-bcd5-7f962501e41f","year":2017},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.327779Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:948194ddeda274c5df32a7371de5fd77e046961ee4db6ba743b14042dd3f2119","observation_id":"95674f1e-51df-4b8c-a207-00973efd6e0c","resolution":{"observed_at":"2026-08-05T19:57:12.405958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.391340Z","title":"Universeg: Universal medical image segmentation,","venue":null,"work_id":"b908461b-d7fd-410a-9b1e-1bc57b9109be","year":2023},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.331036Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:f1b496b2809ad6f64f7d49d18b4eb30851f471309fa2608d928d4257f828a526","observation_id":"bde306e4-ae0b-4c1e-af0c-f45ea5a62f5e","resolution":{"observed_at":"2026-08-05T19:57:12.394815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.379306Z","title":"Gbmseg: Prompting segment anything for glomerular basement membrane segmentation in em images,","venue":null,"work_id":"36143a34-7402-4f21-a243-b6d52a9b941f","year":2024},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.334333Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:59a3fef34dd6136ad60608755cfc61d5506676acb3e0949e1d410bf5d2caf5ce","observation_id":"c7a9b97f-1038-436e-adbd-c79405f368fd","resolution":{"observed_at":"2026-08-05T19:57:12.383594Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-05T19:57:12.365472Z","title":"Dinov2: Learning robust visual features without supervision,","venue":null,"work_id":"8be163c2-408a-4634-9f38-472359655e88","year":2023},"citing_paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T19:57:12.338110Z"},"links":{"citing_paper":"/paper/2508.11462"},"observation_digest":"sha256:283577a702ddb74eec662c9f3730bee30b8e7b3aa99ae4ea4019a62561c8abcf","observation_id":"37361305-d5e0-487b-9ad1-fb6e79aca97b","resolution":{"observed_at":"2026-08-05T19:57:12.371203Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.11462","last_updated":"2025-08-15T13:24:05Z","latest_version":1,"primary_category":"astro-ph.HE","snapshot_observed_at":"2026-08-08T07:12:24.685281Z","submitted_at":"2025-08-15T13:24:05Z","title":"Search of RRATs on declinations from $+42^{\\circ}$ to $+55^{\\circ}$ with a neural network"},"reference_resolution":{"displayed":26,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":26},"total_outbound_references":26},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2508.11462."}