{"id":"W3160238730","doi":"10.1038/s41585-021-00465-1","title":"Machine learning for sperm selection","year":2021,"lang":"en","type":"review","venue":"Nature Reviews Urology","topic":"Reproductive Biology and Fertility","field":"Medicine","cited_by":97,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Medicine; Selection (genetic algorithm); Sperm Retrieval; Sperm; Artificial intelligence; Machine learning; Andrology; Male infertility; Infertility; Pregnancy; Genetics; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.001340402,0.0004698963,0.004148324,0.000151169,0.000111328,0.000005891975,0.000137571,0.002505414,0.0002988505],"category_scores_gemma":[0.004230923,0.0003087001,0.001320305,0.0004086306,0.00008975068,0.00002317674,0.00005798202,0.003796594,0.00009365054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001056637,"about_ca_system_score_gemma":0.0002737262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001315614,"about_ca_topic_score_gemma":0.000003428098,"domain_scores_codex":[0.9966316,0.001096784,0.0007840253,0.00105202,0.00008182298,0.0003537904],"domain_scores_gemma":[0.9983943,0.0003106821,0.0004831012,0.0005133354,0.0002083527,0.00009024886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001426022,0.00006788021,0.00006924404,0.01702604,0.0002520403,0.00001088763,0.000004575772,4.996317e-8,0.000002532204,0.0001718978,0.002257109,0.9799951],"study_design_scores_gemma":[0.0002238899,0.0003949963,0.00003486567,0.001810117,0.002807223,0.001368583,3.162868e-7,0.00000524707,0.000001302245,0.0000314011,0.9931164,0.0002056765],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[5.184561e-7,0.9948857,0.0002024921,0.0003140945,0.000862807,0.002854732,0.00001212734,0.00005940248,0.0008081486],"genre_scores_gemma":[0.000004691973,0.9933738,0.0006215376,0.0006199047,0.001470903,0.0004138905,0.001277671,0.00005150136,0.002166152],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9908593,"threshold_uncertainty_score":0.9999365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05692645138005442,"score_gpt":0.4000260246444141,"score_spread":0.3430995732643596,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}