{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001264239,0.0004852768,0.0006483216,0.0002519356,0.0001442634,0.0007770305,0.0004542094,0.0006051083,0.0003199159],"category_scores_gemma":[0.001878211,0.0003574102,0.000572147,0.0002296107,0.0002150943,0.0005022543,0.0003172096,0.0005096562,0.0001552722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005544868,"about_ca_system_score_gemma":0.0005357732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003634577,"about_ca_topic_score_gemma":0.003633322,"domain_scores_codex":[0.9996775,0.00008836378,0.00001729426,0.0001082525,0.00008207428,0.0000265016],"domain_scores_gemma":[0.9993936,0.0002973936,0.0001858124,0.00004934068,0.00006027513,0.00001355015],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001874566,0.000375098,0.03439771,0.0005367087,0.0002417669,0.0001308103,0.0002089534,0.2090379,0.6700596,0.0006331336,0.0002423509,0.08226142],"study_design_scores_gemma":[0.0001109799,0.002955143,0.0814651,0.00009094571,0.0002974471,0.0003249839,0.0001488244,0.7078647,0.203925,0.001030948,0.001669653,0.0001162874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7647552,0.0005369743,0.233824,0.00005423711,0.000008373634,0.00005252572,0.000115562,0.0001653557,0.0004877041],"genre_scores_gemma":[0.9413746,0.0004340568,0.05732132,0.00003042507,0.000004972742,0.00008107675,0.0002313065,0.00003962176,0.0004825402],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003634577,"threshold_uncertainty_score":0.007226825,"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."}}