{"id":"W2946675921","doi":"10.1016/j.livsci.2019.05.013","title":"Time class for racing performance of the Quarter Horse: Genetic parameters and trends using Bayesian and multivariate threshold models","year":2019,"lang":"en","type":"article","venue":"Livestock Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Heritability; Statistics; Breed; Multivariate statistics; Demography; Trait; Random effects model; Genetic correlation; Threshold model; Quarter (Canadian coin); Race (biology); Bayesian probability; Mathematics; Biology; Animal science; Veterinary medicine; Geography; Genetic variation; Medicine; Evolutionary biology; Computer science; Population","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008955421,0.0004107056,0.0006937893,0.001379363,0.0003796025,0.001210375,0.001431328,0.001375909,0.004486203],"category_scores_gemma":[0.01770471,0.0003026356,0.001205203,0.001211729,0.0008075078,0.001053973,0.0006198708,0.001668767,0.0006260996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006448747,"about_ca_system_score_gemma":0.0005574952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01836613,"about_ca_topic_score_gemma":0.01241129,"domain_scores_codex":[0.9985758,0.0005168592,0.00005102545,0.000516086,0.0001405586,0.0001996444],"domain_scores_gemma":[0.9835764,0.01082803,0.00194929,0.002093971,0.0008147813,0.0007376174],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002515362,0.0003121687,0.8809844,0.00005063498,0.0008533277,0.0001174039,0.00107057,0.06834331,0.003367887,0.004863571,0.001516396,0.03600483],"study_design_scores_gemma":[0.00004542218,0.0002286635,0.5450247,0.00004087476,0.0002141472,0.0001728011,0.0003039333,0.4473643,0.0005242028,0.005571347,0.0004479158,0.00006175214],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834396,0.0001185021,0.01525104,0.0001498662,0.00001678201,0.00001212203,0.0006390394,0.00009327041,0.0002798191],"genre_scores_gemma":[0.9957857,0.00005576121,0.002095469,0.0000180258,0.00001044695,0.00002040725,0.000877983,0.00005889355,0.001077307],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01836613,"threshold_uncertainty_score":0.04736137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01302286816153086,"score_gpt":0.2375730582600333,"score_spread":0.2245501900985024,"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."}}