{"id":"W4391933109","doi":"10.3390/genes15020253","title":"Improving Genomic Predictions in Multi-Breed Cattle Populations: A Comparative Analysis of BayesR and GBLUP Models","year":2024,"lang":"en","type":"article","venue":"Genes","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Genomic selection; Best linear unbiased prediction; Breed; Beef cattle; Selection (genetic algorithm); Biology; Linkage disequilibrium; Genetics; Biotechnology; Computational biology; Single-nucleotide polymorphism; Computer science; Genotype; Machine learning; Gene","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":[0.02372089,0.001467452,0.00222375,0.002094086,0.0007508204,0.002074104,0.002233295,0.001470491,0.00110279],"category_scores_gemma":[0.03310166,0.000797333,0.001752695,0.00132684,0.0009556413,0.002538017,0.001711248,0.001756471,0.0004719931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001416374,"about_ca_system_score_gemma":0.00153718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01786512,"about_ca_topic_score_gemma":0.008765853,"domain_scores_codex":[0.9948178,0.003216435,0.0002223031,0.000807107,0.0007423275,0.0001938764],"domain_scores_gemma":[0.9743056,0.02233808,0.0009589473,0.0007789899,0.001395946,0.0002224916],"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":[0.000377963,0.0001093542,0.04212753,0.0002665241,0.000860731,0.0002935383,0.0005849111,0.8042465,0.001601374,0.01598534,0.001426088,0.1321201],"study_design_scores_gemma":[0.00002066064,0.00004833318,0.002579961,0.00003986878,0.00007557717,0.00007002609,0.00004595739,0.9891503,0.0002364079,0.007173102,0.0005333157,0.0000265463],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.248253,0.005807025,0.7384347,0.001395018,0.00009812951,0.0001886276,0.0004715172,0.001494547,0.003857568],"genre_scores_gemma":[0.7870404,0.002444935,0.2058381,0.0006660892,0.0001340235,0.0003737061,0.001290648,0.000355965,0.001856325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02372089,"threshold_uncertainty_score":0.1254495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04923275224564419,"score_gpt":0.2999532526306211,"score_spread":0.250720500384977,"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."}}