{"id":"W4390953265","doi":"10.20944/preprints202401.1117.v1","title":"Improving Genomic Predictions in Multi-Breed Cattle Populations: A Comparative Analysis of BayesR and GBLUP Models","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Genomic selection; Best linear unbiased prediction; Breed; Selection (genetic algorithm); Beef cattle; Biology; Population; Livestock; Statistics; Biotechnology; Genetics; Computational biology; Mathematics; Single-nucleotide polymorphism; Computer science; Genotype; Machine learning; Gene; Demography; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01709611,0.00120535,0.001501997,0.001939589,0.0005930397,0.00154358,0.001623275,0.001154529,0.0009780229],"category_scores_gemma":[0.02168586,0.000491072,0.001263372,0.001005959,0.0006190125,0.001563462,0.001279,0.001344828,0.0003328044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001172013,"about_ca_system_score_gemma":0.001375569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01686541,"about_ca_topic_score_gemma":0.009114834,"domain_scores_codex":[0.9965549,0.002038187,0.0001576725,0.0005512441,0.0005396929,0.0001583926],"domain_scores_gemma":[0.9802747,0.01698028,0.0007761709,0.0005830229,0.001191025,0.0001947322],"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":[0.0004786268,0.0001675266,0.05060111,0.0002192304,0.0007070705,0.0002137549,0.0003307139,0.8225423,0.002003445,0.005130432,0.001123056,0.1164827],"study_design_scores_gemma":[0.00001399828,0.00006408509,0.002838003,0.00002302031,0.00004920919,0.00003471487,0.00003560015,0.9948193,0.0003338939,0.001545786,0.0002276946,0.00001457362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6322392,0.003093498,0.3570696,0.001249388,0.00007597831,0.0001791015,0.0005810237,0.00158733,0.003924975],"genre_scores_gemma":[0.9054048,0.0006162102,0.09161114,0.0002501538,0.00004600399,0.0001438465,0.0009272439,0.000139393,0.0008613475],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01709611,"threshold_uncertainty_score":0.09041393,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1471618685723383,"score_gpt":0.3586855549770128,"score_spread":0.2115236864046746,"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."}}