{"id":"W3016998145","doi":"10.1017/s1751731120000798","title":"Improving the estimation of amino acid requirements to maximize nitrogen retention in precision feeding for growing-finishing pigs","year":2020,"lang":"en","type":"article","venue":"animal","topic":"Animal Nutrition and Physiology","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Université de Montréal; Université de Sherbrooke","funders":"Agriculture and Agri-Food Canada; Swine Innovation Porc","keywords":"Animal science; Completely randomized design; Feed conversion ratio; Energy requirement; Nitrogen balance; Lysine; Nitrogen; Mathematics; Chemistry; Amino acid; Body weight; Biology; Biochemistry; Regression; Statistics","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":[0.000213137,0.00006706818,0.0001126098,0.000007962511,0.0001000384,0.00002628367,0.0001467211,0.00004765466,0.00003412886],"category_scores_gemma":[0.0002295707,0.00002783582,0.00006362435,0.0001811406,0.00001524806,0.0002037635,0.00006677657,0.00005630283,0.000006270303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001332671,"about_ca_system_score_gemma":0.000002773065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00010211,"about_ca_topic_score_gemma":0.00001850518,"domain_scores_codex":[0.9993187,0.00004048016,0.0002134764,0.0001824826,0.0001038278,0.0001410461],"domain_scores_gemma":[0.9997012,0.0001090049,0.00008687215,0.00002118234,0.00003976349,0.00004199085],"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":[0.000343825,0.00001753821,0.001582624,0.00001228772,0.000002525842,2.279063e-7,0.0001374857,0.00001007885,0.9793959,0.0003156224,0.0001453323,0.01803653],"study_design_scores_gemma":[0.001420507,0.007172094,0.7701938,0.0002029553,0.00004644734,0.000003535436,0.002878269,0.03469301,0.1753329,0.006390808,0.001186725,0.0004789541],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996025,0.00002770381,0.0002995798,0.003227565,0.00003016624,0.000305156,0.00001839013,0.00001844461,0.00004804429],"genre_scores_gemma":[0.9978069,0.000002867488,0.001264666,0.0007350929,0.0001258241,0.00002343638,0.00003732164,9.060566e-7,0.000002955419],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.804063,"threshold_uncertainty_score":0.1135112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06926160096015994,"score_gpt":0.2669453334685862,"score_spread":0.1976837325084263,"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."}}