{"id":"W2996694077","doi":"10.3390/ani10010022","title":"Evaluation of Growth Curve Models for Body Weight in American Mink","year":2019,"lang":"en","type":"article","venue":"Animals","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Mink; Akaike information criterion; Gompertz function; Bayesian information criterion; Growth curve (statistics); Weibull distribution; Statistics; Biology; Mathematics; Model selection; Body weight; Selection (genetic algorithm); Goodness of fit; Growth model; Animal model; Ecology; Computer science; Machine learning","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":[0.00508992,0.001452601,0.001209947,0.001131232,0.0004240349,0.0009829735,0.001324797,0.0008640338,0.001665694],"category_scores_gemma":[0.008146402,0.0004803057,0.00239033,0.0009081569,0.000430583,0.0009512016,0.0009012668,0.001018465,0.0005014574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001469957,"about_ca_system_score_gemma":0.001052007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02333538,"about_ca_topic_score_gemma":0.01285785,"domain_scores_codex":[0.9987077,0.0007225366,0.00005937841,0.0003088782,0.00009005669,0.0001114777],"domain_scores_gemma":[0.9955555,0.003451998,0.0003808634,0.0001493181,0.0003568299,0.0001055768],"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":[0.001026684,0.0002009812,0.0863746,0.0003075711,0.0008042437,0.0002520804,0.000550438,0.8552811,0.004611172,0.002297059,0.001175858,0.04711825],"study_design_scores_gemma":[0.00002434813,0.0001648248,0.03019301,0.00004016825,0.0001222944,0.00008735407,0.00015119,0.9659935,0.0007012901,0.001559874,0.0009071747,0.00005493743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8859651,0.000667082,0.1103648,0.0001932964,0.00003171321,0.0001059286,0.001013454,0.0005056481,0.001152982],"genre_scores_gemma":[0.9671335,0.000332541,0.02781173,0.0000510116,0.00001708261,0.0002563607,0.002451858,0.000211364,0.001734538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02333538,"threshold_uncertainty_score":0.04639906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.021391266657724,"score_gpt":0.2839575625619631,"score_spread":0.2625662959042391,"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."}}