{"id":"W2111371381","doi":"10.1016/j.livsci.2015.03.030","title":"Comparing multi-trait Poisson and Gaussian Bayesian models for genetic evaluation of litter traits in pigs","year":2015,"lang":"en","type":"article","venue":"Livestock Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Trait; Statistics; Litter; Deviance information criterion; Mixed model; Poisson distribution; Restricted maximum likelihood; Genetic correlation; Best linear unbiased prediction; Goodness of fit; Mathematics; Population; Threshold model; Bayesian probability; Biology; Bayesian inference; Maximum likelihood; Demography; Ecology; Genetic variation; Computer science; Machine learning; Selection (genetic algorithm)","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":[0.04039084,0.0008748615,0.001386471,0.001666157,0.0006267787,0.001653959,0.002408661,0.002062784,0.001294093],"category_scores_gemma":[0.07996621,0.0008717552,0.002277602,0.001031631,0.00127962,0.001910419,0.001576222,0.001485021,0.0002388776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002312588,"about_ca_system_score_gemma":0.001594743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01422322,"about_ca_topic_score_gemma":0.01075132,"domain_scores_codex":[0.9893488,0.008802202,0.0002679932,0.0007240828,0.0004871837,0.0003697654],"domain_scores_gemma":[0.8290529,0.1611076,0.003321536,0.002516633,0.002996183,0.001005258],"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":[0.003825606,0.0004496486,0.03402424,0.0001511546,0.001113815,0.0001277016,0.0005678866,0.8978407,0.001797662,0.01675606,0.0004675036,0.0428781],"study_design_scores_gemma":[0.0001052723,0.0002339317,0.009115045,0.00002391242,0.0001169774,0.00003303198,0.00007085442,0.980921,0.0002794578,0.008945366,0.00008601976,0.00006900403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7424946,0.0004565605,0.255636,0.0003451236,0.00003310358,0.00007410959,0.0002107916,0.0002134693,0.0005362706],"genre_scores_gemma":[0.9500403,0.0001502947,0.04815456,0.00008187968,0.00002246987,0.0001309452,0.0003668613,0.00008388249,0.0009688927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04039084,"threshold_uncertainty_score":0.2136097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07285785494152369,"score_gpt":0.3113809926325696,"score_spread":0.2385231376910459,"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."}}