{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002312236,0.0002626712,0.0004671432,0.0003167984,0.00004629432,0.00001359245,0.0002716088,0.0003308716,0.00003397753],"category_scores_gemma":[0.00004246795,0.000282875,0.0001976338,0.0002286157,0.0001367625,0.000003509318,0.001471947,0.0003916153,0.000009983954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004085893,"about_ca_system_score_gemma":0.0001592509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001039293,"about_ca_topic_score_gemma":0.001095917,"domain_scores_codex":[0.9981982,0.0001000249,0.0005299704,0.00085407,0.0001177959,0.0001999658],"domain_scores_gemma":[0.9988854,0.00001834613,0.0002083399,0.0007162,0.00009121044,0.00008048372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00005846313,0.0001973119,0.3253138,0.0002031558,0.001370088,4.63691e-7,0.00241646,0.6089439,0.05955399,0.001743244,0.00001332872,0.0001858136],"study_design_scores_gemma":[0.000304845,0.00003578142,0.9396521,0.00006121468,0.000846758,0.00000169894,0.0002252978,0.0496482,0.004320496,0.004602868,0.00002766816,0.0002730714],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9774229,0.001771301,0.01889082,0.00003054418,0.000240423,0.0005206485,0.0002851576,0.00002158259,0.0008165974],"genre_scores_gemma":[0.9885074,0.00007327695,0.01045007,0.0000146291,0.00005524381,0.0001366843,0.0003331141,0.00002117246,0.0004083733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6143383,"threshold_uncertainty_score":0.9999623,"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."}}