{"id":"W3145050603","doi":"10.1016/j.compag.2021.106106","title":"Mining data from milk mid-infrared spectroscopy and animal characteristics to improve the prediction of dairy cow's liveweight using feature selection algorithms based on partial least squares and Elastic Net regressions","year":2021,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; European Commission","keywords":"Feature selection; Partial least squares regression; Statistics; Mathematics; Robustness (evolution); Dairy cattle; Mean squared error; Regression; Cross-validation; Elastic net regularization; Regression analysis; Computer science; Animal science; Artificial intelligence; Biology","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":[0.0008359891,0.0004932943,0.000707464,0.001094939,0.0001816208,0.0004430845,0.0005520039,0.0003794097,0.0004362094],"category_scores_gemma":[0.001529422,0.0001821864,0.0006863435,0.001144507,0.000112164,0.0004335451,0.0003617659,0.0004090413,0.0002211773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001672933,"about_ca_system_score_gemma":0.0003227068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002765637,"about_ca_topic_score_gemma":0.003893759,"domain_scores_codex":[0.9997918,0.00004541615,0.00002022147,0.00005783448,0.00006089681,0.00002380482],"domain_scores_gemma":[0.9993492,0.0003158159,0.0001056016,0.00006021385,0.0001464965,0.00002270548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00105009,0.001444179,0.3340521,0.0002288047,0.0008728568,0.0004287736,0.0001307921,0.1177048,0.0541933,0.000564299,0.00337894,0.4859511],"study_design_scores_gemma":[0.00003844975,0.0002822784,0.1129782,0.00001580717,0.000171234,0.0001662444,0.00009166084,0.8745289,0.009406196,0.001424732,0.0008713228,0.00002481866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8861364,0.0003607607,0.1110611,0.0001894362,0.00002954271,0.00003063865,0.001322399,0.0004790484,0.0003907103],"genre_scores_gemma":[0.9637327,0.000130266,0.03286791,0.00004137387,0.00002945523,0.00003212566,0.002476056,0.00002547925,0.0006647561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002765637,"threshold_uncertainty_score":0.005499125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710082805569742,"score_gpt":0.2564354097963061,"score_spread":0.2393345817406087,"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."}}