{"as_of":"2026-08-16T10:22:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c6c14db35be63e60a0add58c5bb78c521cc540b90ed98f2e2f6fedbba4dbaad7","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:51:10.836654Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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":"2026-08-07T04:39:33.052150Z","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":"2026-08-07T04:39:39.674211Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"cited_work":{"arxiv_id":"1908.02337","doi":null,"metadata_source":"pith","pith_arxiv_id":"1908.02337","snapshot_observed_at":"2026-08-07T04:39:39.674211Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","venue":"stat.ML","work_id":"bb1847a0-3a23-4241-817f-dc5e0bf4a0b9","year":2019},"citing_paper":{"arxiv_id":"2506.10140","last_updated":"2025-06-11T19:40:09Z","snapshot_observed_at":"2026-08-08T00:50:10.030317Z","submitted_at":"2025-06-11T19:40:09Z","title":"Survival Analysis as Imprecise Classification with Trainable Kernels","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T04:39:33.052150Z"},"links":{"cited_paper":"/paper/1908.02337","citing_paper":"/paper/2506.10140"},"observation_digest":"sha256:59b2b10454969c1574385a334a2af57e58721bae863cb3bafde6444e411063fb","observation_id":"43705155-8e0b-4929-b50f-c78c78cb1c85","resolution":{"observed_at":"2026-08-07T04:39:39.774237Z","resolver_source":"local_arxiv","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"}}],"links":{"evidence":"/evidence","html":"/paper/1908.02337/citation-record","integrity":"/paper/1908.02337/integrity","json":"/paper/1908.02337/citation-record.json","paper":"/paper/1908.02337"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:51:11.269186Z","title":"Martinsson","venue":null,"work_id":"81557fb6-e54d-481c-8dee-b406b08cb508","year":2016},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.713070Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:48933132186da56f650eaca35de5f8e57b5d6b5af85d8d662fed35920b3866db","observation_id":"1bef9945-f5b3-4cc5-82b5-2ed39046cdfb","resolution":{"observed_at":"2026-08-14T14:51:11.273744Z","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":"2026-08-14T14:51:11.255093Z","title":null,"venue":null,"work_id":"fafcc887-7515-4436-b476-200add65a2c5","year":2018},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.718122Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:532434c5b5c15d6f6a3b411878ad3ae3c5ab4fb8d5a27195719025ca83948a73","observation_id":"06390822-b028-4175-a21b-783488de144d","resolution":{"observed_at":"2026-08-14T14:51:11.259591Z","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":"2026-08-14T14:51:11.241084Z","title":"Ching, X","venue":null,"work_id":"e5d5fb55-07d2-4d1d-bbe1-77090849c8f5","year":2018},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.722848Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:9c97bf376d940df37930f41c71f5f912a924ef882409d167d721d05c7ba97f4a","observation_id":"03450ab7-a253-403a-989a-b4380c00e0c3","resolution":{"observed_at":"2026-08-14T14:51:11.245671Z","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":"2026-08-14T14:51:11.226304Z","title":"Deep convolutional neural network for survival analysis with pathological images","venue":null,"work_id":"50a5f219-53aa-4634-996b-65465ab171cb","year":2016},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.727511Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:f2edf4d5e758724beec0664273a2aaf68b4028ebc52e6d217999df3d558ba77e","observation_id":"733d0c98-e99b-47b0-a1c2-5e8d0b72513d","resolution":{"observed_at":"2026-08-14T14:51:11.231212Z","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":"2026-08-14T14:51:11.212460Z","title":"Gensheimer and Balasubramanian Narasimhan","venue":null,"work_id":"75422dd2-04f8-4cf1-ae7a-4ffc0b35c119","year":2019},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.731734Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:5cad47d7b90f1f3e62bfb3bbb502b2ff28ed35e466392b2a6947c7d7a2f01ae6","observation_id":"bd88e05b-8fb4-47ae-a72f-896e3eff106d","resolution":{"observed_at":"2026-08-14T14:51:11.216871Z","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":"2026-08-14T14:51:11.198256Z","title":"Deep neural networks for survival analysis based on a multi-task framework","venue":null,"work_id":"e919e2ec-6f69-423b-b66c-7e1c861177da","year":2018},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.736491Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:52949101b5fc9d1f7567c8c0f82f6bd929a37220f3c84c09d9ff08a66ac87152","observation_id":"e2a9334b-18f6-49fe-aa5e-55cdbd0c43aa","resolution":{"observed_at":"2026-08-14T14:51:11.203084Z","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":"2026-08-14T14:51:11.183851Z","title":null,"venue":null,"work_id":"3bb50ecc-87df-45cd-bb7b-71a2ecd08c82","year":2018},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.741566Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:9b406a12f5b4e69ebc49b5430442de05749d276517a343c9d5fcf666a259aef1","observation_id":"54ebee21-71c0-4639-8631-a7018faf0b61","resolution":{"observed_at":"2026-08-14T14:51:11.188425Z","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":"2026-08-14T14:51:11.169497Z","title":null,"venue":null,"work_id":"6dec731c-3888-4888-a22c-3eeab84bc913","year":2017},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.745841Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:3e982a0523b0f9dece4db5fe5bf7d6fd981ae8ed24c54fa815fe2969b4569621","observation_id":"dee80d2d-38c9-40b0-b725-43ef5e028ddd","resolution":{"observed_at":"2026-08-14T14:51:11.173980Z","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":"2026-08-14T14:51:11.154641Z","title":"RNN-SURV: A deep recurrent model for survival analysis","venue":null,"work_id":"8528cc33-cbb8-44bf-904e-b6fd9f6124ff","year":2018},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.749975Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:145da0a231cebe6d19b006e8dec8c4d9185b3dbf772960117667b4e8d4d027cb","observation_id":"5e5f5dc3-d6ef-4798-aafe-542733250ed5","resolution":{"observed_at":"2026-08-14T14:51:11.159153Z","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":"2026-08-14T14:51:11.139477Z","title":null,"venue":null,"work_id":"364c891b-bf0b-464d-8b82-2c39427f369f","year":2003},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.754170Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:5a4e26d7cd603ffdc045fd310c5338db4381aaec30e3071123d37e50b5245e76","observation_id":"3c488a62-9381-42cc-854c-d62166bdda42","resolution":{"observed_at":"2026-08-14T14:51:11.144509Z","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":"2026-08-14T14:51:11.123831Z","title":null,"venue":null,"work_id":"acec1b67-828f-4d2c-98ec-e28733db5694","year":2005},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.758441Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:1ec2e9c29a669182f7d998c26f4cfb84dbe2cdd972501cb0701c167a11932e48","observation_id":"24b6c65b-f318-4b63-ab54-4732708229a3","resolution":{"observed_at":"2026-08-14T14:51:11.128702Z","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":"2026-08-14T14:51:11.109395Z","title":"K Andersen and J","venue":null,"work_id":"abf61c87-2d3d-4c6e-83c8-e277a9cc9147","year":2007},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.762362Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:6d31d576f8207754f5471c0df73494b06c435e50ad89f2aaea14adddc275ac82","observation_id":"f1c3400a-cfa8-49f8-9f13-39235afc05b3","resolution":{"observed_at":"2026-08-14T14:51:11.113911Z","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":"2026-08-14T14:51:11.094984Z","title":"K Andersen and M","venue":null,"work_id":"ea372212-2d16-48f2-9ed0-ba387f73491c","year":2010},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.766342Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:dece8040e2bf8e784216f20a1cea5e98350a7cd38b13f8daa93ceb3ca605d9ea","observation_id":"591da071-74b7-4938-843b-30d7ed9302b2","resolution":{"observed_at":"2026-08-14T14:51:11.099726Z","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":"2026-08-14T14:51:11.080805Z","title":null,"venue":null,"work_id":"5d205e1f-d0e8-44b2-921f-cff56b5fa601","year":1993},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.770547Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:1549fcfa521b7bb78e9de22ed0d34a55d9615a83e83650471c27c1ae65bb605c","observation_id":"f6b61a54-8ab2-4558-978f-b3f50d723e2e","resolution":{"observed_at":"2026-08-14T14:51:11.085217Z","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":"2026-08-14T14:51:11.067120Z","title":null,"venue":null,"work_id":"2806c978-78d0-4732-879d-1bc46618f166","year":2008},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.774581Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:7792a0f2ee4c6d3ab175f676bf8c039b33828773eff034050dd748d030c61eb0","observation_id":"75a087ec-a841-4b68-be51-cede6b762aeb","resolution":{"observed_at":"2026-08-14T14:51:11.071480Z","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":"2026-08-14T14:51:10.778504Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.778504Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:6ad97e67eac9efdf049c6e47558f8eda62c98cf74c723c52823f074626e7e0c5","observation_id":"8761af9e-a477-4f57-b10e-1bd2c0355bae","resolution":{"observed_at":"2026-08-14T14:51:10.778504Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:51:10.782459Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.782459Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:6eae6b5a9177eb4a0fa4271be0108a5d4ccfb39de51f559204aad264478776c6","observation_id":"97f96af4-12bc-447b-8602-f9516c291f7c","resolution":{"observed_at":"2026-08-14T14:51:10.782459Z","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":"2026-08-14T14:51:11.034320Z","title":"Gerds, and Per Kragh Andersen","venue":null,"work_id":"8a59e91a-2b6e-438e-94fd-f18cd3584325","year":2014},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.786555Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:709fa954acc030650a82257bcf4b4d8650439b6d97abec1f8bea77febb6b0908","observation_id":"242f411b-22ab-4bb6-be78-3aa18045983b","resolution":{"observed_at":"2026-08-14T14:51:11.038833Z","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":"2026-08-14T14:51:11.018773Z","title":"Restricted mean models for transplant beneﬁt and urgency","venue":null,"work_id":"977ff216-5fb2-4e56-972a-702a76ec85fd","year":2012},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.790585Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:757c09c56f42d7213c7e6cc9dbf6634c45d4d48eb58853548f08e35e54e0667b","observation_id":"91201f3e-555e-4595-a024-f9e3219d40d1","resolution":{"observed_at":"2026-08-14T14:51:11.024353Z","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":"2026-08-14T14:51:11.003642Z","title":"The accelerated failure time model: a useful alternative to the cox regression model in survival analysis","venue":null,"work_id":"c0bf4fb2-5345-4287-8d12-5efcdf61460d","year":1992},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.794679Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:9a57b1e36e5546d184636a862c2707aaf5b76b78bf8d754cc91f45885202f880","observation_id":"b51fcf0f-6191-4734-a53e-fb6c4c1f7409","resolution":{"observed_at":"2026-08-14T14:51:11.008592Z","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":"2026-08-14T14:51:10.988642Z","title":"Harrell, Kerry L","venue":null,"work_id":"939a87b7-63ae-4d57-9310-83bcd465cce1","year":1996},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.798833Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:16c89b04b352b05c6226eea4afd92d6c0de9a45b83bd109a7be9adc67a2557da","observation_id":"23bd432e-5e5d-4288-9d52-da5af16f514d","resolution":{"observed_at":"2026-08-14T14:51:10.993313Z","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":"2026-08-14T14:51:10.973119Z","title":"K Andersen and M","venue":null,"work_id":"410e7b6c-867e-420f-9c1f-c7d1f51c9264","year":2006},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.803329Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:e02e9b94d31a7e49df064f25fe7a92ca31c15d1ac53dcc68dd3d990534d27e77","observation_id":"790d17e3-3992-4315-9208-e20acefe9060","resolution":{"observed_at":"2026-08-14T14:51:10.977783Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:51:10.807505Z","title":"Random search for hyper-parameter optimization","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.807505Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:dbcc4fcfc40968760e90e3b8a084d921ba71ada5e3ca5db59962cdc044573109","observation_id":"05066b7e-48dc-4d4f-bf6c-1a53c560d3bc","resolution":{"observed_at":"2026-08-14T14:51:10.807505Z","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":"2026-08-14T14:51:10.949495Z","title":"Srivastava, G","venue":null,"work_id":"e0e3db80-90ec-4902-8760-053533709c89","year":1929},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.811510Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:f860e4ab2bd9d40b7293de2ab82af38dc30a51efdfa2f2196555d31cadaf5b13","observation_id":"f3e0e5c9-8cc9-44be-bfbd-2134147d43f7","resolution":{"observed_at":"2026-08-14T14:51:10.954120Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:51:10.815641Z","title":"Greedy function approximation: a gradient boosting machine","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.815641Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:db1ae384ede2ff452a70f814732b2aaf1392254fc556c5c5832486b8bf8eaf28","observation_id":"c64f18ba-59e9-4219-a492-4bc633c3aa61","resolution":{"observed_at":"2026-08-14T14:51:10.815641Z","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":"2026-08-14T14:51:10.923529Z","title":"Individualized treatment effects with censored data via fully nonparametric bayesian accelerated failure time models","venue":null,"work_id":"fb888820-f4c0-45a4-891d-6b5de8f99545","year":2017},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.820065Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:1c582d694f83d6690bf90ec70b8cd57d26ac5beada1907336b1154eb6a17aecf","observation_id":"d98d14ae-965c-4736-aef9-ae2d59511168","resolution":{"observed_at":"2026-08-14T14:51:10.928522Z","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":"2026-08-14T14:51:10.908248Z","title":"Recruitment of adults 65 years and older as participants in the cardiovascular health study","venue":null,"work_id":"27663a77-6cd4-41ec-b8c2-f27d95cba4ee","year":1993},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.824435Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:d8b2e390a602124958ddca7c78ac0d9479f6f3035a505c86acf747a8482522cf","observation_id":"169efb25-1147-4757-aa51-762fe4b93cd5","resolution":{"observed_at":"2026-08-14T14:51:10.913859Z","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":"2026-08-14T14:51:10.894145Z","title":null,"venue":null,"work_id":"0984083a-ba55-4b43-b870-bde310f41b16","year":2002},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.828377Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:bb9aa9bc1a8dfc1a7325d211354be7310019c749f917b4e185753bab36e158f9","observation_id":"3491e091-d5be-4121-bc7d-9da41afedabd","resolution":{"observed_at":"2026-08-14T14:51:10.898486Z","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":"2026-08-14T14:51:10.880806Z","title":"Schaubel","venue":null,"work_id":"ec51acf4-5165-4ec8-b079-0ba8249de455","year":1957},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.832510Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:ad026d05701deb57ecbf28490732d3a882769fcf0d86a5f1a6dff4f967fcb328","observation_id":"4eedf155-353a-4d11-9710-a3dfcfc5e7ad","resolution":{"observed_at":"2026-08-14T14:51:10.884876Z","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":"2026-08-14T14:51:10.865811Z","title":null,"venue":null,"work_id":"c4a09adf-71b0-4d9e-98b9-dbd74253e762","year":1992},"citing_paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T14:51:10.836654Z"},"links":{"citing_paper":"/paper/1908.02337"},"observation_digest":"sha256:1ec42f63c6b08d17e7b25bf8812138d1a26dfc15a6a9b7595b0466d2927beb77","observation_id":"cfdd4eb6-e386-49ec-9442-0bf1af87d7c4","resolution":{"observed_at":"2026-08-14T14:51:10.871264Z","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"}}],"paper":{"arxiv_id":"1908.02337","last_updated":"2020-03-10T18:57:05Z","latest_version":2,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-16T05:48:32.224629Z","submitted_at":"2019-08-06T19:16:58Z","title":"DNNSurv: Deep Neural Networks for Survival Analysis Using Pseudo Values"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":17},"total_outbound_references":30},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+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 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:1908.02337."}