{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004148373,0.00007689765,0.0001319093,0.00002820818,0.000009419268,0.00000309683,0.0001068635,0.00005301491,0.00002251237],"category_scores_gemma":[0.00005194256,0.00007407928,0.00004851803,0.00005695871,0.00003913957,0.000002187243,0.00002541625,0.00002521473,0.000004271852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008041271,"about_ca_system_score_gemma":0.00007976498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004054005,"about_ca_topic_score_gemma":0.00002211998,"domain_scores_codex":[0.9992905,0.00006390524,0.0001584726,0.0002033445,0.0001546976,0.0001290329],"domain_scores_gemma":[0.9995182,0.0000169418,0.0000809038,0.0001648988,0.0001955402,0.00002354787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002884532,0.0002109622,0.03176481,0.0000777426,0.000128223,6.903471e-8,0.0003100671,0.003265714,0.9154595,0.0381632,0.001202812,0.009128496],"study_design_scores_gemma":[0.003746224,0.004001223,0.5378875,0.0000506743,0.0001920083,0.00000359769,0.0002886299,0.01058041,0.3410953,0.09932757,0.00231263,0.0005142885],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916191,0.0005460142,0.003807509,0.00004051426,0.0000613023,0.0004220615,0.00002142769,0.000002399287,0.003479707],"genre_scores_gemma":[0.9897915,0.00002052039,0.009878372,0.00004686343,0.00006175446,0.00004133946,0.00002889677,0.00001111676,0.0001197076],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5743642,"threshold_uncertainty_score":0.3020865,"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."}}