{"id":"W2550771350","doi":"10.3168/jds.2016-11491","title":"Modeling genetic and nongenetic variation of feed efficiency and its partial relationships between component traits as a function of management and environmental factors","year":2016,"lang":"en","type":"article","venue":"Journal of Dairy Science","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Institute of Food and Agriculture; Biotechnology and Biological Sciences Research Council; U.S. Department of Agriculture","keywords":"Heritability; Trait; Variance components; Multivariate statistics; Mixed model; Statistics; Biology; Variance (accounting); Econometrics; Animal science; Biotechnology; Mathematics; Computer science; Economics; Evolutionary biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.003817175,0.0005841784,0.0005043179,0.0007416469,0.0003008876,0.000745372,0.0008438982,0.0007176501,0.0006360096],"category_scores_gemma":[0.007987137,0.0005367837,0.001003775,0.00080898,0.0006819417,0.000519286,0.0007093658,0.0005767351,0.00008553944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008131822,"about_ca_system_score_gemma":0.0009077581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03034163,"about_ca_topic_score_gemma":0.0272975,"domain_scores_codex":[0.9986162,0.0008071964,0.00004368426,0.0003608488,0.00006165181,0.0001104048],"domain_scores_gemma":[0.9938462,0.004709053,0.0007400619,0.0004305919,0.0001534086,0.0001205148],"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":[0.0003560011,0.0002034794,0.253968,0.00004100928,0.0009884372,0.0001799605,0.0003180782,0.7101071,0.005669465,0.00751392,0.0001710089,0.02048359],"study_design_scores_gemma":[0.00002248041,0.00009342669,0.05689607,0.00000723841,0.000124811,0.00003702514,0.00005018691,0.9384686,0.0004410209,0.003625973,0.0002110733,0.00002207821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9248819,0.00009074497,0.07438496,0.00008214921,0.000004478008,0.00001981736,0.0002272345,0.00005683686,0.0002518116],"genre_scores_gemma":[0.9863796,0.00004960848,0.01255264,0.00001611799,0.000005380461,0.00004285881,0.0003139464,0.00001162558,0.0006282199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03034163,"threshold_uncertainty_score":0.06033003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0193622786878384,"score_gpt":0.222162368432133,"score_spread":0.2028000897442946,"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."}}