{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003663816,0.000089614,0.0001162922,0.0001065281,0.0002479203,0.00002188286,0.0000782469,0.00009794057,0.00000357021],"category_scores_gemma":[0.00004214541,0.00008436554,0.00001233648,0.0001720642,0.000164548,0.000003971545,0.0001111783,0.0002174146,0.00000180237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002326023,"about_ca_system_score_gemma":0.000482536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000343124,"about_ca_topic_score_gemma":0.000955488,"domain_scores_codex":[0.9988367,0.00008127358,0.0001371617,0.0003461548,0.0001435348,0.0004551795],"domain_scores_gemma":[0.9995526,0.00002681874,0.00002184248,0.0001354185,0.00004117806,0.0002221407],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002313991,0.0009121574,0.04741118,0.000911291,0.0001230063,0.00001156572,0.004265305,0.006776876,0.8990101,0.01206435,0.002958762,0.02324145],"study_design_scores_gemma":[0.001353545,0.003384275,0.9675434,0.00007597879,0.000005564994,0.00002613628,0.0004809375,0.02296936,0.001627737,0.001788078,0.0005318478,0.0002131735],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935567,0.001128799,0.003787836,0.001026585,0.00001112204,0.0003306817,0.00006345374,0.00001088337,0.00008396729],"genre_scores_gemma":[0.969479,0.000779088,0.02918001,0.0001026906,0.00007390163,0.00002022114,0.00003119548,0.00001828287,0.0003155723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9201322,"threshold_uncertainty_score":0.3440327,"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."}}