{"id":"W4385900010","doi":"10.1002/aro2.13","title":"Multi‐trait genomic predictions using GBLUP and Bayesian mixture prior model in beef cattle","year":2023,"lang":"en","type":"article","venue":"Animal Research and One Health","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Agricultural Science and Technology Innovation Program; University of California, Davis; Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Tropical Agricultural Sciences; Chinese Academy of Tropical Agricultural Sciences; Chinese Academy of Agricultural Sciences; National Natural Science Foundation of China","keywords":"Heritability; Trait; Best linear unbiased prediction; Genomic selection; Genetic correlation; Bayesian probability; Beef cattle; Statistics; Selection (genetic algorithm); Biology; Animal science; Mathematics; Genetic variation; Genetics; Machine learning; Single-nucleotide polymorphism; Computer science; Genotype","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":[0.005253683,0.0009513756,0.001024854,0.001481032,0.0005008825,0.001057776,0.0009313449,0.0009349038,0.001187068],"category_scores_gemma":[0.007504136,0.0005666299,0.001067121,0.0007753345,0.0004380402,0.0008832302,0.0007911774,0.001045461,0.0002367224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008163794,"about_ca_system_score_gemma":0.0009204881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02238157,"about_ca_topic_score_gemma":0.01180756,"domain_scores_codex":[0.998686,0.0007005425,0.00004368326,0.0002891447,0.0001785386,0.0001020121],"domain_scores_gemma":[0.9958918,0.003347244,0.0002076636,0.0001351746,0.0003376403,0.00008042173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006459624,0.000132417,0.05599364,0.00009385901,0.000527858,0.0001505391,0.0001412687,0.8438162,0.00385861,0.002612493,0.0008447344,0.09118248],"study_design_scores_gemma":[0.000009896014,0.00001685753,0.004279231,0.00001005588,0.00002900241,0.00001784204,0.000008712289,0.9943057,0.0002834396,0.0009445568,0.00008286037,0.00001185517],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6335261,0.001165393,0.3628391,0.0004279344,0.00003215493,0.00003856399,0.0003680927,0.0005729599,0.001029521],"genre_scores_gemma":[0.9497401,0.0002217871,0.04829822,0.00009579596,0.00002386884,0.0000450039,0.0006773308,0.00005445836,0.0008433362],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02238157,"threshold_uncertainty_score":0.04450262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0933304898414402,"score_gpt":0.3807345665459088,"score_spread":0.2874040767044686,"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."}}