{"as_of":"2026-08-17T03:09:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:af5ed59d3fa2e8b790d54e555d112c3dc89b6a2295a4ed9526c7ce28026d6993","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T11:38:12.784769Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"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/1908.08588/citation-record","integrity":"/paper/1908.08588/integrity","json":"/paper/1908.08588/citation-record.json","paper":"/paper/1908.08588"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T11:38:12.966392Z","title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015","venue":null,"work_id":"43b14492-2539-4b95-a19e-8e37898c4e63","year":2015},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.737708Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:7f7884fb610c6aea3bbc680e5889b2e2081d025a38b360adee249d1d0ea590bc","observation_id":"f60b9c95-d0a3-431c-b22f-86cfc0e80e73","resolution":{"observed_at":"2026-08-14T11:38:12.970986Z","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":"2026-08-14T11:38:12.955287Z","title":"Medical Image Computing and Computer-Assisted Inter- vention MICCAI, pp","venue":null,"work_id":"e878449e-62e3-4619-8d37-c2be30270129","year":2015},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.742356Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:60843d3b1fbbed03d44665b0f3658bb1ad6ebfd101c954097d2508c6a14f8f52","observation_id":"80d94e4d-c741-4b0f-a1e7-b7df0150451f","resolution":{"observed_at":"2026-08-14T11:38:12.959199Z","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":"2026-08-14T11:38:12.944888Z","title":"IEEE Transactions on Neural Networks, 20(1):6180, 2009","venue":null,"work_id":"be79f6b5-b37a-4655-b2e9-2c32d0475edd","year":2009},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.746494Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:e1b0f87caf52c37fdf235e8a7deefc1242e03b1726a2c7d1fb8c829553ad80dd","observation_id":"998a5685-732b-4da0-89f0-506a61ff0dd0","resolution":{"observed_at":"2026-08-14T11:38:12.948464Z","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":"2026-08-14T11:38:12.933950Z","title":"and Welling, M.: Semi-supervised classification with graph convolutional networks","venue":null,"work_id":"bcdc487d-15e8-40de-86f0-de07a042e98f","year":2017},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.750451Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:bf5e5750f7dbe1a74c009c35245f0d2ec25848b29fe9fe1d4f147b71d8f85d0b","observation_id":"95d87cd7-13f8-474d-b26f-d3aa9e21fc2d","resolution":{"observed_at":"2026-08-14T11:38:12.937692Z","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":"2026-08-14T11:38:12.922394Z","title":"et al: Extraction of Airways From CT (EXACT09)","venue":null,"work_id":"8867775f-4598-4900-b302-a1db20d071a2","year":2012},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.754881Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:18aa91ade32f6d8c4aa0c877063d0ad91bdef93e4f97283ab6b853112e5ded1d","observation_id":"b75effd6-9185-40a0-8968-20a141de4da7","resolution":{"observed_at":"2026-08-14T11:38:12.926542Z","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":"2026-08-14T11:38:12.910572Z","title":"Medical Image Computing and Computer-Assisted Intervention MICCAI 2017, pp","venue":null,"work_id":"73411e05-d3b8-47b7-9360-46b0cd1449b2","year":2017},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.759416Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:6cba32810b10c3dfdccbbde35737cbec84b0fab65d16272948fe46d3a1f568da","observation_id":"d1b3c2af-26bb-4609-beca-3b8390c22806","resolution":{"observed_at":"2026-08-14T11:38:12.914825Z","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":"2026-08-14T11:38:12.899336Z","title":"Thoracic Image Analysis Workshop, pp 238-250, 2018","venue":null,"work_id":"576f954c-145a-4cfd-a4dc-ab8660f4988c","year":2018},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.763630Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:ce1d34855d6c4632dfa4533a3b1ad7e903823ff633c7560a834da6ff7239f109","observation_id":"78d4bc56-4325-45be-bbb0-0ea3e32cdb0a","resolution":{"observed_at":"2026-08-14T11:38:12.903477Z","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":"2026-08-14T11:38:12.886637Z","title":"1st Conference on Medical Imaging with Deep Learning, Amsterdam, 2018","venue":null,"work_id":"30607712-704e-45c1-9dcf-135b5b53d760","year":2018},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.766958Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:c2b13a803675dd26292ffb3ee9bcd6f937af0888e532d1d201b936c4dda169e8","observation_id":"7ea34b57-6fe6-4294-940b-ccf3d44adca6","resolution":{"observed_at":"2026-08-14T11:38:12.891316Z","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":"1806.02279","last_updated":"2018-06-06T16:16:52Z","snapshot_observed_at":"2026-08-16T12:12:35.951794Z","submitted_at":"2018-06-06T16:16:52Z","title":"Deep Vessel Segmentation By Learning Graphical Connectivity","version":1},"cited_work":{"arxiv_id":"1806.02279","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.02279","snapshot_observed_at":"2026-08-14T11:38:12.817177Z","title":"Deep Vessel Segmentation By Learning Graphical Connectivity","venue":"cs.CV","work_id":"d6a6e738-9152-419f-ab34-3c2094a31575","year":2018},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.770448Z"},"links":{"cited_paper":"/paper/1806.02279","citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:4bb19759c612261fb1ce720cf5fcd3f734139b4e0fee320465402978b0e25d33","observation_id":"1d80f0a3-ff2b-4177-bb18-1f160b848dd4","resolution":{"observed_at":"2026-08-14T11:38:12.823847Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":"2026-08-14T11:38:12.874227Z","title":"Medical image analysis 18, pp","venue":null,"work_id":"caeab30d-1001-4219-b4b5-77e934ab8256","year":2014},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.774446Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:326554f8aeb921ad8a9102f2074561fb18d2c44cc39fcf4d77755f2b73d921c2","observation_id":"a0d3d01c-7d56-4ca5-ad31-507e1444a4c0","resolution":{"observed_at":"2026-08-14T11:38:12.878582Z","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":"2026-08-14T11:38:12.860953Z","title":"Medical image analysis 14.4, pp","venue":null,"work_id":"1682e5c3-8c20-4f88-a967-95727bd408aa","year":2010},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.777809Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:70dbe0ea7a207f71a7971090e5556510c99b9fd9f2f489a97570205ffc55dcd2","observation_id":"a32b7d40-784a-425b-a22f-3fc50ff5379e","resolution":{"observed_at":"2026-08-14T11:38:12.865227Z","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":"2026-08-14T11:38:12.847001Z","title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","venue":null,"work_id":"9ed88f6e-dcb5-431b-abd5-dc22f2ba5619","year":2009},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.781288Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:36f156604b64d5487bf636df7eb4d403717857bc4c996d15038f4b1f6d655bf8","observation_id":"fb53c4ba-6c39-4950-a601-774ceacf7f3a","resolution":{"observed_at":"2026-08-14T11:38:12.851347Z","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":"2026-08-14T11:38:12.833481Z","title":"Journal of Thoracic Oncology 4.5, pp","venue":null,"work_id":"7028f4c5-e0db-48b7-abdd-75a6f793de57","year":2009},"citing_paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T11:38:12.784769Z"},"links":{"citing_paper":"/paper/1908.08588"},"observation_digest":"sha256:7d46c872c76021c2c28cd949eb494d7d81ffb16f225294383e1a3773d2408ce6","observation_id":"775347af-6129-4fac-9115-46a29e3230be","resolution":{"observed_at":"2026-08-14T11:38:12.837898Z","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"}}],"paper":{"arxiv_id":"1908.08588","last_updated":"2019-08-22T20:32:01Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-16T10:27:17.329539Z","submitted_at":"2019-08-22T20:32:01Z","title":"A joint 3D UNet-Graph Neural Network-based method for Airway Segmentation from chest CTs"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":13},"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 17 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:1908.08588."}