{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006928453,0.00006005644,0.00006058888,0.00003487769,0.00002157661,0.000008128209,0.00003730522,0.00006002633,0.000005307153],"category_scores_gemma":[0.00001954127,0.00005681234,0.00001947227,0.00006740711,0.00004449071,0.000002053831,0.00003597032,0.00006815398,4.717047e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000281795,"about_ca_system_score_gemma":0.00002374316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000617589,"about_ca_topic_score_gemma":0.000004546461,"domain_scores_codex":[0.9995738,0.00002162741,0.000103589,0.0001620863,0.00004871618,0.00009019598],"domain_scores_gemma":[0.9999014,0.000007442361,0.00001567492,0.00004382423,0.00001094682,0.00002072533],"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":[0.0000408181,0.00003116439,0.0329688,0.00007317221,0.00001677475,0.000001384624,0.0002378383,0.0002341238,0.9633239,0.001025633,0.00001149907,0.002034877],"study_design_scores_gemma":[0.0009011356,0.002718286,0.7001103,0.0001415243,0.00006238766,0.00008855514,0.0002250848,0.04457771,0.2489312,0.0007550034,0.001189849,0.0002990371],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920494,0.003162475,0.004264125,0.00001736879,0.00007102498,0.00005132805,0.00001197735,0.000007439504,0.0003648734],"genre_scores_gemma":[0.993283,0.00004701344,0.006497202,0.000007350399,0.00008352597,0.000001380647,0.00001565506,0.000008075066,0.00005684043],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7143927,"threshold_uncertainty_score":0.231674,"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."}}