{"id":"W4313280119","doi":"10.1038/s41598-022-25104-6","title":"Machine learning and hypothesis driven optimization of bull semen cryopreservation media","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Sperm and Testicular Function","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Extender; Cryopreservation; Machine learning; Cryoprotectant; Artificial intelligence; Semen cryopreservation; Semen; Computer science; Sperm; Sperm motility; Biology; Chemistry; Anatomy; Botany","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007002981,0.00005713622,0.0001194611,0.0001556267,0.0002429668,0.00002405477,0.00002239778,0.00002025613,0.0009585157],"category_scores_gemma":[0.0007921436,0.0000534786,0.00003251814,0.0003432519,0.00006433183,0.00006581504,0.00006560228,0.00008979005,0.000001001356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003484625,"about_ca_system_score_gemma":0.0000545347,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003745842,"about_ca_topic_score_gemma":0.000003561649,"domain_scores_codex":[0.9989023,0.00004400073,0.0002440183,0.0002767674,0.0004423312,0.00009057687],"domain_scores_gemma":[0.9993778,0.00005339412,0.0002000758,0.0002005452,0.000116411,0.00005171895],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009579831,0.0002613306,0.7842968,0.0001232022,0.00007213171,0.0003532751,0.001584428,0.1155337,0.08637205,0.0000637761,0.003829904,0.007413597],"study_design_scores_gemma":[0.00207964,0.0007866633,0.1510833,0.0001430285,0.0006445326,0.002976531,0.00161771,0.6935433,0.03185951,0.003827628,0.1108887,0.0005495036],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940616,0.0003340408,0.001732702,0.0002834444,0.001692403,0.0002880247,0.000002642255,0.0000643407,0.001540749],"genre_scores_gemma":[0.994399,0.00001092821,0.003338758,0.00001813806,0.00003364665,0.00001637007,0.0002227481,0.00001067031,0.001949815],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6332135,"threshold_uncertainty_score":0.9999548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01896084275053521,"score_gpt":0.2283964442987649,"score_spread":0.2094356015482297,"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."}}