{"id":"W4297518140","doi":"10.1101/2022.09.27.509504","title":"Machine learning and hypothesis driven optimization of bull semen cryopreservation media","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Sperm and Testicular Function","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Memorial University of Newfoundland","funders":"Mitacs","keywords":"Extender; Cryopreservation; Cryoprotectant; Machine learning; Semen cryopreservation; Artificial intelligence; Semen; Sperm; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002166799,0.0007841588,0.0005917791,0.0006637499,0.0002218263,0.0007161655,0.0007227496,0.0006790539,0.0006699105],"category_scores_gemma":[0.002943641,0.0003521545,0.0007294995,0.000346434,0.0004365901,0.0004898232,0.0004576532,0.0007218523,0.0001486904],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009556891,"about_ca_system_score_gemma":0.0007313232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003021318,"about_ca_topic_score_gemma":0.002197763,"domain_scores_codex":[0.9996339,0.0001534384,0.00002233916,0.00008707974,0.00006520926,0.00003813284],"domain_scores_gemma":[0.9985548,0.001009707,0.0001469454,0.0000633595,0.0001967579,0.00002854173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001322098,0.0001382992,0.004302226,0.00008494179,0.00008813339,0.00004462981,0.00003019568,0.9539778,0.006947914,0.0006820954,0.0002736777,0.033298],"study_design_scores_gemma":[0.000005136951,0.00007151091,0.0005408155,0.000004081272,0.00001008819,0.000005026086,0.000005386159,0.9955938,0.003448962,0.0002085076,0.0001028177,0.00000373745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7367213,0.001158053,0.2581205,0.0004584,0.00005129721,0.0001089932,0.000433792,0.000831872,0.002115754],"genre_scores_gemma":[0.9214932,0.0002052255,0.07635761,0.00007961012,0.0000142384,0.0001006176,0.0004923954,0.00005070656,0.001206347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003021318,"threshold_uncertainty_score":0.01145923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01796105032118531,"score_gpt":0.2163287064537218,"score_spread":0.1983676561325365,"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."}}