{"id":"W4402714525","doi":"10.1016/j.animal.2024.101330","title":"Prediction of growth and feed efficiency in mink using machine learning algorithms","year":2024,"lang":"en","type":"article","venue":"animal","topic":"Genetic and phenotypic traits in livestock","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mink; Algorithm; Computer science; Artificial intelligence; Machine learning; Feed conversion ratio; Biology; Body weight; Ecology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001025687,0.0009461977,0.0009524694,0.0008731239,0.0001868518,0.0006510079,0.0004239223,0.0006270434,0.0006765994],"category_scores_gemma":[0.001872381,0.0002250586,0.0008709693,0.0007918699,0.0001890168,0.0005720093,0.0003092029,0.0006005444,0.0004373053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00063841,"about_ca_system_score_gemma":0.0004801935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005583038,"about_ca_topic_score_gemma":0.004458313,"domain_scores_codex":[0.9996592,0.00006628157,0.00002969881,0.0001201229,0.00008217623,0.00004253517],"domain_scores_gemma":[0.9989317,0.0005532625,0.0001797614,0.0000470224,0.0002468023,0.00004127007],"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":[0.00114542,0.0003636099,0.1811983,0.0004971544,0.000392448,0.0002699931,0.0001152731,0.5137318,0.03364238,0.0004295754,0.001255589,0.2669584],"study_design_scores_gemma":[0.00001519091,0.0002978332,0.06928204,0.00003944033,0.00006332588,0.00009322363,0.00005178295,0.9203271,0.008602459,0.0004436123,0.0007455178,0.00003843898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.853688,0.001280006,0.1416714,0.0001560481,0.00006415589,0.00006582674,0.000857224,0.0007516391,0.00146569],"genre_scores_gemma":[0.9444747,0.000538024,0.05228377,0.00004813605,0.00002079295,0.00008355953,0.001120056,0.00004859865,0.001382217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005583038,"threshold_uncertainty_score":0.01110113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01483331660826863,"score_gpt":0.2388592806048694,"score_spread":0.2240259639966008,"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."}}