{"id":"W2011127117","doi":"10.1017/s0021859609008442","title":"Application of the law of diminishing returns to estimate maintenance requirement for amino acids and their efficiency of utilization for accretion in young chicks","year":2009,"lang":"en","type":"article","venue":"The Journal of Agricultural Science","topic":"Animal Nutrition and Physiology","field":"Agricultural and Biological Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Manitoba; Canadian Science Centre for Human and Animal Health","funders":"","keywords":"Parameterized complexity; Energy requirement; Mathematics; Amino acid; Valine; Linear regression; Threonine; Energy balance; Animal science; Accretion (finance); Regression analysis; Regression; Range (aeronautics); Lysine; Consistency (knowledge bases); Environmental science; Statistics; Chemistry; Biology; Biochemistry; Materials science; Physics; Ecology; Algorithm","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":[0.002971274,0.0004741826,0.0005333908,0.000934186,0.0001581743,0.0006202113,0.0006414268,0.0005400425,0.0004993494],"category_scores_gemma":[0.01061436,0.0002947603,0.0006954588,0.0004827312,0.0003504662,0.0005583492,0.000463171,0.0005899402,0.0002732232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001032428,"about_ca_system_score_gemma":0.0008896993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0132231,"about_ca_topic_score_gemma":0.005417401,"domain_scores_codex":[0.9994332,0.000276778,0.00003957834,0.0001145692,0.00009954701,0.0000362374],"domain_scores_gemma":[0.9955921,0.003405245,0.0004521462,0.0002068854,0.0003058945,0.00003772856],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0004160486,0.000139881,0.1398105,0.0002314008,0.0003212084,0.0001891471,0.0002669152,0.752245,0.0201389,0.0029081,0.0003164209,0.08301646],"study_design_scores_gemma":[0.000006045493,0.00009213148,0.01905762,0.000007676316,0.00002500345,0.0001094489,0.00002762044,0.9770323,0.002798052,0.0006360407,0.0001916753,0.0000162866],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7004605,0.0006013936,0.2973436,0.0001116355,0.00001070742,0.00006863694,0.0002150399,0.0002132683,0.000975235],"genre_scores_gemma":[0.9670269,0.0001969914,0.03187971,0.00002185738,0.000004761184,0.00006262343,0.0001374636,0.0000383422,0.0006315333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0132231,"threshold_uncertainty_score":0.02629226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02693842063517737,"score_gpt":0.2817731733092895,"score_spread":0.2548347526741121,"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."}}