{"id":"W2887177770","doi":"10.1016/j.livsci.2018.08.002","title":"Genetic analysis of morphological and functional traits in Campolina horses using Bayesian multi-trait model","year":2018,"lang":"en","type":"article","venue":"Livestock Science","topic":"Veterinary Equine Medical Research","field":"Veterinary","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; BIO (Canada)","funders":"Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo; Universidade de São Paulo","keywords":"Heritability; Genetic correlation; Trait; Biology; Breed; Selection (genetic algorithm); Rump; Withers; Genetic relationship; Correlation; Animal breeding; Genetic variation; Statistics; Evolutionary biology; Genetics; Animal science; Demography; Mathematics; Population; Body weight; Genetic diversity; 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":[0.003268046,0.0005623585,0.0008280425,0.001772588,0.0008336949,0.000949744,0.001016309,0.0008829546,0.001303395],"category_scores_gemma":[0.004624959,0.0003686564,0.00128,0.001070469,0.000590416,0.0005003726,0.0006639362,0.0007908861,0.0001483471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071965,"about_ca_system_score_gemma":0.0007494704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01354602,"about_ca_topic_score_gemma":0.01700409,"domain_scores_codex":[0.9986106,0.0006863267,0.00004401428,0.0004355574,0.0001061324,0.0001171836],"domain_scores_gemma":[0.9972634,0.002050929,0.0002835763,0.0001350402,0.0001513027,0.0001157584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003099,0.001265429,0.4786003,0.0002209606,0.003208641,0.001394453,0.001325694,0.358523,0.06483341,0.01070681,0.0009209987,0.07590131],"study_design_scores_gemma":[0.00004473434,0.0001696386,0.173718,0.00003395063,0.0003233649,0.0001918642,0.0001689653,0.8208666,0.0009744493,0.003028276,0.0003913129,0.00008884685],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9829689,0.00009545575,0.01642193,0.00007685652,0.000005504411,0.0000128765,0.0001391939,0.0000473283,0.0002320238],"genre_scores_gemma":[0.9887931,0.00004357717,0.01038157,0.0000176959,0.000004020128,0.00002386243,0.0003480126,0.00002672842,0.0003615047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01354602,"threshold_uncertainty_score":0.02693433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1852698905592635,"score_gpt":0.4071702600284955,"score_spread":0.221900369469232,"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."}}