{"id":"W2942349979","doi":"10.1139/cjas-2018-0190","title":"Genetic correlations among selected traits in Canadian Holsteins","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Animal Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Dairy Commission; University of Guelph","funders":"","keywords":"Udder; Selection (genetic algorithm); Trait; Biology; Genetic correlation; Gibbs sampling; Markov chain Monte Carlo; Fertility; Statistics; Biotechnology; Genetic variation; Genetics; Demography; Population; Mastitis; Mathematics; Monte Carlo method; Computer science; Bayesian probability","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.001683643,0.0004036757,0.0006625695,0.001869147,0.00167258,0.001011714,0.0008513631,0.0003382608,0.001874086],"category_scores_gemma":[0.002882642,0.0002022603,0.0004879476,0.003987942,0.0008494658,0.0001539499,0.0004769781,0.000469648,0.0001729659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0113032,"about_ca_system_score_gemma":0.008806242,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9457762,"about_ca_topic_score_gemma":0.9744388,"domain_scores_codex":[0.9987237,0.0001169054,0.00004524311,0.0004834225,0.0003914026,0.0002392048],"domain_scores_gemma":[0.9984812,0.0004348152,0.0002363426,0.0001417342,0.000498737,0.0002071825],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002709019,0.00002871743,0.9696001,0.00004968775,0.0005204181,0.0002286528,0.0009233627,0.002573502,0.002641485,0.0007217511,0.001063196,0.02137817],"study_design_scores_gemma":[0.000005072404,0.00001141537,0.9976363,0.000009040658,0.00004261345,0.00003061141,0.0001333141,0.001074973,0.00009165158,0.00008525782,0.0008675266,0.00001211339],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950932,0.0004543054,0.0006933578,0.00006761139,0.000006058141,0.00001087846,0.002752472,0.00002097964,0.0009011342],"genre_scores_gemma":[0.9941134,0.0002772733,0.001214931,0.00003108583,0.00000384849,0.000009370208,0.003323401,0.00001871069,0.001007988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05422384,"threshold_uncertainty_score":0.1090863,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007011958821435648,"score_gpt":0.2100793750445486,"score_spread":0.2030674162231129,"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."}}