{"id":"W2763499525","doi":"10.3168/jds.2017-12732","title":"Genotype imputation in a tropical crossbred dairy cattle population","year":2017,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; BIO (Canada)","funders":"Faculdade de Zootecnia e Engenharia de Alimentos, Universidade de São Paulo; Agricultural Research Service; Universidade de São Paulo; Universidade Estadual Paulista; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Empresa Brasileira de Pesquisa Agropecuária; Fundação de Amparo à Pesquisa do Estado de São Paulo; Zoetis; Iowa State University; U.S. Department of Agriculture","keywords":"Imputation (statistics); Crossbreed; Population; Biology; Dairy cattle; Holstein Cattle; Genotype; Statistics; Animal science; Mathematics; Genetics; Missing data; Demography","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":[0.01169929,0.000457967,0.0007336407,0.0005763695,0.0005025144,0.001076764,0.001016269,0.0005088081,0.0008657876],"category_scores_gemma":[0.01678518,0.0002454649,0.0007151415,0.001290017,0.0006007004,0.0005318781,0.0009319648,0.0006358202,0.0002375422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005007753,"about_ca_system_score_gemma":0.0006296026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006320109,"about_ca_topic_score_gemma":0.008072413,"domain_scores_codex":[0.995233,0.003298782,0.0001716788,0.0008919236,0.0002236595,0.0001810333],"domain_scores_gemma":[0.9942284,0.003616423,0.0006404016,0.0009071879,0.000470268,0.0001372969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001623577,0.0001339671,0.7840092,0.0001641684,0.002197853,0.0009528022,0.002085472,0.1251069,0.009267215,0.002530467,0.001300139,0.07062819],"study_design_scores_gemma":[0.000342022,0.0006318864,0.4497893,0.0001699879,0.001091987,0.001895732,0.001630102,0.5244124,0.005806244,0.008702735,0.00540404,0.0001234986],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9695392,0.0004728832,0.02850439,0.000145957,0.00001000082,0.00003116437,0.0004765061,0.0001031622,0.0007167398],"genre_scores_gemma":[0.9763899,0.0001732638,0.02099266,0.0001205584,0.00000934735,0.00003388986,0.001920957,0.00003313835,0.0003263755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01169929,"threshold_uncertainty_score":0.06187248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01572752216739521,"score_gpt":0.2954073071689755,"score_spread":0.2796797850015803,"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."}}