{"id":"W7117256038","doi":"10.1093/jas/skaf444","title":"ASAS-NANP Symposium: mathematical modeling in animal nutrition: construction of supervised machine learning regression pipelines for livestock data modeling: a case studys","year":2025,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Effects of Environmental Stressors on Livestock","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; National Institute of Food and Agriculture; U.S. Department of Agriculture","keywords":"Pipeline (software); Livestock; Interpretability; Pipeline transport; Adaptability; Software; Residual","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":[0.01303867,0.0009744267,0.0009813977,0.001219993,0.0009439943,0.003154254,0.002369167,0.001304475,0.01147465],"category_scores_gemma":[0.0212124,0.0008453135,0.002674017,0.00163647,0.001156087,0.003386363,0.004564326,0.003163276,0.004830246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001670842,"about_ca_system_score_gemma":0.004901522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003937653,"about_ca_topic_score_gemma":0.004107811,"domain_scores_codex":[0.9966753,0.001411332,0.0002196489,0.0004954942,0.001008691,0.0001895629],"domain_scores_gemma":[0.9867539,0.006691285,0.0004103635,0.002119901,0.003183617,0.0008407924],"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":[0.0003620415,0.0007512498,0.01006736,0.0009014805,0.0002710045,0.0009265963,0.001293303,0.268538,0.01311084,0.07441091,0.1128906,0.5164765],"study_design_scores_gemma":[0.00002549435,0.0001494108,0.002358631,0.0001450686,0.00003365441,0.0002359359,0.0001753003,0.8425371,0.01021825,0.03355121,0.1104981,0.00007175616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02038278,0.0005967875,0.956569,0.004511596,0.000544538,0.0002196258,0.001281723,0.009388695,0.006505204],"genre_scores_gemma":[0.09527546,0.001443986,0.8810028,0.0006201975,0.0003991213,0.0003548647,0.00453286,0.003450945,0.01291978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01303867,"threshold_uncertainty_score":0.0689559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05954601626175651,"score_gpt":0.301558349911126,"score_spread":0.2420123336493695,"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."}}