{"id":"W4399682640","doi":"10.1186/s12958-024-01239-1","title":"Emerging trends in sperm selection: enhancing success rates in assisted reproduction","year":2024,"lang":"en","type":"article","venue":"Reproductive Biology and Endocrinology","topic":"Reproductive Biology and Fertility","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Ottawa","funders":"National Institutes of Health; Mitacs","keywords":"Sperm; Assisted reproductive technology; Biology; Intracytoplasmic sperm injection; Semen analysis; In vitro fertilisation; Selection (genetic algorithm); Computer science; Embryo; Infertility; Artificial intelligence; Genetics; Pregnancy","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0008781605,0.0002656733,0.0005863967,0.001009347,0.0000929372,0.00001122323,0.00007189115,0.000281617,0.0001424275],"category_scores_gemma":[0.0005011896,0.0002280439,0.00007731222,0.001170435,0.0005470235,0.0001845581,0.00008225848,0.0007952797,0.00001812242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001815764,"about_ca_system_score_gemma":0.00007727982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003200898,"about_ca_topic_score_gemma":0.0004419209,"domain_scores_codex":[0.9963191,0.000385835,0.000526504,0.002142082,0.00005437014,0.0005721365],"domain_scores_gemma":[0.9991832,0.0001017149,0.00006575953,0.0005014991,0.00009236662,0.00005542978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002307538,0.0002315157,0.6648045,0.0001093259,0.0001447689,0.0002002262,0.0007647853,0.000007914549,0.1855943,0.002365156,0.00008853229,0.1433815],"study_design_scores_gemma":[0.0006558199,0.0003916508,0.9329091,0.00006421229,0.00004087796,0.002090157,0.0002533141,0.00006516373,0.05323782,0.001424602,0.008658986,0.0002082733],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975143,0.01213355,0.0001053046,0.008527079,0.001296989,0.0002854729,0.00000286628,0.0001650148,0.00234079],"genre_scores_gemma":[0.9962863,0.0008274188,0.0002052623,0.00006072864,0.000909462,0.00007871065,0.00005107036,0.00001893577,0.001562158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2681047,"threshold_uncertainty_score":0.9299359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02775838638111041,"score_gpt":0.3525590447580913,"score_spread":0.3248006583769809,"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."}}