{"id":"W4402533459","doi":"10.1093/jas/skae234.037","title":"114 Accuracy of genomic predictions using single and multiple-trait machine learning methods in Canadian beef cattle population","year":2024,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; University of Alberta","funders":"","keywords":"Beef cattle; Trait; Genomic selection; Population; Biology; Statistics; Animal science; Biotechnology; Mathematics; Computer science; Genetics; Genotype; Single-nucleotide polymorphism; Medicine; Gene; Environmental health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00493755,0.0006353638,0.0004726084,0.0008186622,0.0005738066,0.0009214924,0.0010847,0.0004468292,0.001022559],"category_scores_gemma":[0.007618607,0.0002178988,0.0007828339,0.0007045523,0.0004092677,0.0004330565,0.0003916335,0.0005048975,0.0003279177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00227876,"about_ca_system_score_gemma":0.002062984,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5069345,"about_ca_topic_score_gemma":0.5111914,"domain_scores_codex":[0.9986101,0.0005095041,0.00005747225,0.0004323583,0.0002698426,0.0001205758],"domain_scores_gemma":[0.9961904,0.001870478,0.0003873038,0.0003606078,0.001024165,0.0001670027],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004826451,0.00006625882,0.8769603,0.00007308363,0.0007123573,0.000117936,0.0001852741,0.04825586,0.003368136,0.0003229674,0.001202081,0.06825314],"study_design_scores_gemma":[0.00002381685,0.0001158318,0.575497,0.00004396652,0.0002083435,0.0001175987,0.0002336136,0.420823,0.001795012,0.0003173135,0.0007617072,0.00006278788],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.987902,0.0006996293,0.009079046,0.0001859353,0.00001679551,0.00001069867,0.001186029,0.0002139755,0.0007059277],"genre_scores_gemma":[0.9941927,0.0001156888,0.004108294,0.00002601671,0.000005204211,0.000006125932,0.001129418,0.0000206783,0.000395873],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4930655,"threshold_uncertainty_score":0.9919386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02698064989801152,"score_gpt":0.3303617176184755,"score_spread":0.303381067720464,"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."}}