{"as_of":"2026-08-19T20:42:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c3e8451b4ad166e9c6f7fda3f43082af91d65944ec6ee148945dc0530823d60b","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T00:06:14.912348Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2412.19696/citation-record","integrity":"/paper/2412.19696/integrity","json":"/paper/2412.19696/citation-record.json","paper":"/paper/2412.19696"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:07:01.204262Z","title":null,"venue":null,"work_id":"6b0e36ac-e026-4d99-9029-f1f5def86891","year":2022},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.646536Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:9e38a380657402f57aac8d2cebc2eb835f75d9b6e8747e332783830d80b0e086","observation_id":"ec9768ea-64f9-4c15-8c09-9de45b4d8a79","resolution":{"observed_at":"2026-08-11T00:07:01.209516Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s12958-024-01253-3","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:15.546028Z","title":null,"venue":null,"work_id":"ae3a1fd3-6a81-4432-867a-a117c1609e0a","year":2024},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.655916Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:4eaf540691e40832cb7fe114434c5c5b61f9f128585faa5928d8e2d62027e803","observation_id":"a7e78145-6e65-44e4-911d-8a7355930c44","resolution":{"observed_at":"2026-08-11T00:06:15.556547Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2147/ahct.s71272","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:15.496621Z","title":null,"venue":null,"work_id":"92acfa60-0eb7-43b8-926d-32458e0a2b5e","year":2015},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.661220Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:5d32f30cd7c40e4b2e5519b665a6a37b4165b984e2bc607c696c5fcc14328cef","observation_id":"8a91935d-6a2c-4326-aa2d-5deaee9cb506","resolution":{"observed_at":"2026-08-11T00:06:15.511941Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41746-024-01006-x","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:15.470823Z","title":"C., Voliotis, M., Tsaneva -Atanasova, K., Kelsey, T","venue":null,"work_id":"f2c052c9-9d03-42db-9c53-ed4d5007e94f","year":2024},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.668857Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:c909bdade3abd2d55ce095c947d32deb0a8d181491e5d1784b1aac22178d4081","observation_id":"3da602e9-4610-4b67-9927-77a3a23b8be2","resolution":{"observed_at":"2026-08-11T00:06:15.479498Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.7759/cureus.64725","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:15.411379Z","title":"J., Chaudhari, K., Acharya, N., Shrivastava, D., & Muneeba, S","venue":null,"work_id":"325c5b7c-46bd-4311-ab34-007057c01a7a","year":2024},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.675494Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:4b6fc6bbf272c8afe05a17df4aa21d2c51ff132a9c21c9d407572c5df2cca1b4","observation_id":"0a75512d-d923-4d1c-a991-15bc0810e57a","resolution":{"observed_at":"2026-08-11T00:06:15.427941Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-319-67925-9_1","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-13T11:03:48.094449Z","title":null,"venue":"Lecture notes on data engineering and communications technologies","work_id":"5936bc7c-2322-4a7c-b31a-94ee8d342d0b","year":2018},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.684198Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:5a236e4691ac497f21e36ba7f7c91b9cd29deeb60370506052903ab8d6f74e68","observation_id":"e9a48bb1-2abc-4b0e-b0f5-a0e9b6b87250","resolution":{"observed_at":"2026-08-11T00:06:15.394025Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10815-022-02562-5","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-19T11:03:45.297681Z","title":null,"venue":"Journal of Assisted Reproduction and Genetics","work_id":"0713f9b5-db4d-409b-b59b-2b5f10f7a101","year":2022},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.692704Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:dec9bf7b9699037142529e83988a9c4398ea8fbfb0ae17d33dca9f99cab90b89","observation_id":"11683be1-e526-4532-99d1-6a89d4e65ade","resolution":{"observed_at":"2026-08-11T00:06:15.370269Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[{"edge_observation":{"observed_at":"2026-08-11T00:18:03.520017+00:00","source":"paper_reference_links","state":"open"},"event_date":"2022-08-31","event_type":"correction","notice_doi":"10.1007/s10815-022-02605-x","provenance":{"observed_at":"2026-07-11T02:56:29.024948+00:00","source":"crossref","source_record_id":"10.1007/s10815-022-02605-x->10.1007/s10815-022-02562-5:correction"}}],"reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T00:07:01.169850Z","title":null,"venue":null,"work_id":"7cd83f65-8026-4c8c-b825-2f02a364fb80","year":2024},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.701361Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:d981ca69d43194626a6c551cdd7df3c98f3d04f4e0edd3b807c71b5fff23edce","observation_id":"124bd1c4-7cc7-468c-ad44-24c8e81e9322","resolution":{"observed_at":"2026-08-11T00:07:01.182894Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/humrep/dead034","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:15.329998Z","title":"K., Barrie, A., … Coomarasamy, A","venue":null,"work_id":"d5c0adad-7486-4a26-a811-b6899f1e386b","year":2023},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.726450Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:18bff7be393adb2ae99260275635bebec9a46af4e938dae1a8cd96764f0f968b","observation_id":"e6510d70-e500-4ca3-b26e-b838c4e4fea8","resolution":{"observed_at":"2026-08-11T00:06:15.336135Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1177/0272989x14535984","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:15.307521Z","title":null,"venue":null,"work_id":"fd293069-6775-476c-9633-eb5cdd4f4b91","year":2015},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.738925Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:7885960d02d1d4287be68540205aad1a3160c5af244c5512314778bff3b00703","observation_id":"544ba41c-3e04-44b8-8d13-08803c69b8f5","resolution":{"observed_at":"2026-08-11T00:06:15.312899Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"6368.2020","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:07:00.992491Z","title":null,"venue":null,"work_id":"9225ea6e-aa88-4acd-8721-8dc16ee3e7a8","year":2021},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.746442Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:07a843d7ac4fd396d40fd370d6059b8711f06b688a692927e94f93d8301892bf","observation_id":"6267b01c-eebe-4605-9976-528901bcf426","resolution":{"observed_at":"2026-08-11T00:07:01.009193Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s12884-021-04373-5","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:15.285208Z","title":null,"venue":null,"work_id":"4066bcff-c3d3-435f-8829-c74ff94dcf20","year":2022},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.760741Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:ddd87579c291d7e5cafcc855799de88956073033d76b7bcf1a674f2bca7f92e6","observation_id":"fc79e660-67af-4d1e-9c75-1323cb6867b6","resolution":{"observed_at":"2026-08-11T00:06:15.292011Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10815-022-02707-6","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-19T11:03:45.297681Z","title":"S., Kandula, H., Thirumalaraju, P., Kanakasabapathy, M","venue":"Journal of Assisted Reproduction and Genetics","work_id":"e6b8e1a8-fca9-4184-95ca-ca88a20ca7ef","year":2023},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.765935Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:9687919cf4909825bd28538bd77858511a033a2fe8a2bdfcd1924444d4fae0ee","observation_id":"f41061cc-e251-4dd2-9c53-2032b9366ba8","resolution":{"observed_at":"2026-08-11T00:06:15.270575Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10815-021-02254-6","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-19T11:03:45.297681Z","title":"F., & Karstoft, H","venue":"Journal of Assisted Reproduction and Genetics","work_id":"22ac93cc-d501-4168-abcb-bc676581e7e5","year":2021},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.776001Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:5459aef9f5f8acff558228f2c64094ef224feffef923fa29fa3f8e9dbae78152","observation_id":"e0324222-54f0-4b3b-90ce-c040c3d01f7d","resolution":{"observed_at":"2026-08-11T00:06:15.252128Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.83808","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:45.888810Z","title":null,"venue":null,"work_id":"4ad5a9d3-0ce4-4b83-9346-00340333a3cb","year":2022},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.789805Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:2c7c4f5256f080f2f8544794b5cab3fc6054b58249d9f79738ae7518149244c2","observation_id":"06cd4a30-4d4a-4f24-8019-ac5cd26b022f","resolution":{"observed_at":"2026-08-11T00:06:45.900195Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T00:06:14.797363Z","title":"J., Steyerberg, E","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.797363Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:c1a1062833653c99a3bea1ae156c25ef4b75785953dbcfeed9c98524db37fab4","observation_id":"b03e23f5-aa51-4f59-9f68-e04b2984f6f7","resolution":{"observed_at":"2026-08-11T00:06:14.797363Z","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":"2026-08-11T00:07:01.144235Z","title":"A., Christensen, A","venue":null,"work_id":"08fc7171-b658-4a37-9edb-7e28b44d654a","year":2011},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.803210Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:f3830bc17967bd1fffe8b33797a52b8a16909c43f98d3da048c0b4e7a2057173","observation_id":"54e8a5b7-c4f3-4fd7-bed6-da280696a51e","resolution":{"observed_at":"2026-08-11T00:07:01.149304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10815-021-02349-0","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-19T11:03:45.297681Z","title":"D., Silvestri, G., Gordon, T., & Griffin, D","venue":"Journal of Assisted Reproduction and Genetics","work_id":"608b86fc-d7b5-4aee-bdeb-89a271342e28","year":2021},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.811451Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:eed47b1836445c2003fea11f0e33ad2b2d3de81be2ef1857107e957bf03d4152","observation_id":"bf40305b-f7b0-43fa-ad6e-a64ab5346525","resolution":{"observed_at":"2026-08-11T00:06:15.202951Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00521-018-3693-9","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-18T11:03:46.223893Z","title":"R., Al -Insaif, S., Hossain, M","venue":"Neural Computing and Applications","work_id":"5f919355-1ff4-4ba9-be36-26be0211b20c","year":2020},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.816684Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:0057d331795e2206d187ee96c8c5b86ab41e17eabb4c1a61d4fc3b01ba41a552","observation_id":"9eb1776b-484d-4d26-bc2b-6f51214bde4e","resolution":{"observed_at":"2026-08-11T00:06:15.183499Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T00:07:01.103414Z","title":null,"venue":null,"work_id":"0f1701e6-995c-4eaf-a12b-33948b65d327","year":2010},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.823585Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:488f2a504c9f9c10b4691effd4ab02472bbac4b29621fd73e667a42ef1147767","observation_id":"68d3e4f2-7a0c-4f50-9d24-b492ee59f685","resolution":{"observed_at":"2026-08-11T00:07:01.109429Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.13926","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:30.809159Z","title":"A., Khanjani, S., Javanmardi, S., Bayat, B., Naderi, Z., & Hajiyavand, A","venue":null,"work_id":"5176bb0e-c805-49f9-acdd-bf0a916124ad","year":2024},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.829548Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:498b6ce4945fd7a614841b7d895b9b464724fb7dac821b36debf995a0d7a7322","observation_id":"b12782d4-94a9-498b-858a-031e4e944de7","resolution":{"observed_at":"2026-08-11T00:06:30.820339Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T00:07:01.074926Z","title":null,"venue":null,"work_id":"32953037-4bb9-4d82-8517-ff37f693a1d9","year":2024},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.835102Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:350db66c41e46cd9e7179703e8d5b81c5f742a3d2d9d66f1b5a7ff2ffc7db345","observation_id":"108ee86a-3f59-4d90-a1f1-eca732f3fd12","resolution":{"observed_at":"2026-08-11T00:07:01.083974Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s00521-022-07950-7","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-18T11:03:46.223893Z","title":null,"venue":"Neural Computing and Applications","work_id":"e2ee7795-88c6-42f0-8e6f-d24d65341239","year":2023},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.841163Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:294003dbc175a210d2c1fe9e5450fd048b658b7acf48e6d0f9a3c9b0befb781a","observation_id":"1d879d9d-2be1-4ac5-b9d1-adb1a8e3d91e","resolution":{"observed_at":"2026-08-11T00:06:15.159253Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/su16135644","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:15.114953Z","title":"S., Alaraidh, I","venue":null,"work_id":"0eefd83a-5029-46d9-be59-78e199380121","year":2024},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.846590Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:0b2d715d8f9153f686f431d490ba3db633899b0b5effa1d186309be118bcf366","observation_id":"85ca5399-1731-466f-9172-54325607a094","resolution":{"observed_at":"2026-08-11T00:06:15.135877Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/1742-6596/2728/1/012056","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:15.077957Z","title":null,"venue":null,"work_id":"1da6efa8-4307-419d-92fc-5d7ef9b98040","year":2024},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.857206Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:ed86b862e5ef71f55fb4618c4a461739b67ba5dc8218395a96e2bf51fbe96be3","observation_id":"ef57db39-bd6b-455a-9de6-9bc2d6af145a","resolution":{"observed_at":"2026-08-11T00:06:15.089496Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T00:06:14.861421Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.861421Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:b57dbc4c68eb4f0eda1f61d97378938eef94d95198d92de08e8723d860b2ca3e","observation_id":"3544f3ce-ba7c-47fe-90d1-55c484b4e20f","resolution":{"observed_at":"2026-08-11T00:06:14.861421Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.38094/jastt20165","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:14.994513Z","title":null,"venue":null,"work_id":"7c2e3274-1c99-4325-9e54-3f5b9e899a68","year":2021},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.866570Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:a1d37c557d6f23a40cfba49b28bbfaf08472c17e9e3b8024055ad3283b1d359c","observation_id":"bdbd7d4a-09ee-4380-849d-671b9c11156e","resolution":{"observed_at":"2026-08-11T00:06:15.005603Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T00:06:14.874966Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.874966Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:024b5ef3dfc00102d0c5d6382a9cff740dc52f3b9b7d22afd9877037d3fe9ec8","observation_id":"db622c38-1603-454c-9abe-0cf388683726","resolution":{"observed_at":"2026-08-11T00:06:14.874966Z","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":"2026-08-11T00:07:01.054574Z","title":"N., … Polosukhin, I","venue":null,"work_id":"59d9aea2-05a0-4fb7-a56e-0c30ad9550ad","year":2017},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.884824Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:2fe4742dc00ff4ed337a9072d4c31333af071a96073d9dad69ef362c3a7059bc","observation_id":"4e399aa0-a6b5-47eb-959f-90cb4b5d63cf","resolution":{"observed_at":"2026-08-11T00:07:01.061421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/electronics11234017","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T00:06:14.957266Z","title":null,"venue":null,"work_id":"30722f1b-d0a2-4ac9-afce-17b5890583c2","year":2022},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.895130Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:6fe041760c5557bae5dd67c9e030d4a588fe79633203d679d5ec65e6de9ac9d1","observation_id":"ec2d3603-ebe1-4c5b-bc1e-01306197cbab","resolution":{"observed_at":"2026-08-11T00:06:14.966422Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T00:07:01.030212Z","title":null,"venue":null,"work_id":"90670223-64b5-4ef5-a5fc-b4fb64e5b5dd","year":2024},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.912348Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:4fb1c8e999310499b8c19e890d09c715a765b82b101d5e912269d086026bc653","observation_id":"31159415-aa9e-430f-aa87-f6d0af710322","resolution":{"observed_at":"2026-08-11T00:07:01.038438Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-11T00:06:14.715112Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models","version":1},"reference_index":2399,"source":"pdf_text","source_observed_at":"2026-08-11T00:06:14.715112Z"},"links":{"citing_paper":"/paper/2412.19696"},"observation_digest":"sha256:fa5f63325a1100a7b1cd06fc653d1be5d3be43cd947bf18a1030276125598cc3","observation_id":"60ec294b-083f-48b7-bc3e-c2a673418126","resolution":{"observed_at":"2026-08-11T00:06:14.715112Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.19696","last_updated":"2024-12-27T15:46:59Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-19T10:55:40.132470Z","submitted_at":"2024-12-27T15:46:59Z","title":"An Integrated Optimization and Deep Learning Pipeline for Predicting Live Birth Success in IVF Using Feature Optimization and Transformer-Based Models"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":9,"verified_exact":19,"verified_fuzzy":2},"total_outbound_references":32},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2412.19696."}