{"id":"W1966772329","doi":"10.1080/00071668.2013.803517","title":"Study of broiler chicken responses to dietary protein and lysine using neural network and response surface models","year":2013,"lang":"en","type":"article","venue":"British Poultry Science","topic":"Animal Nutrition and Physiology","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Broiler; Lysine; Response surface methodology; Dietary protein; Food science; Artificial neural network; Animal science; Biology; Chemistry; Biochemistry; Amino acid; Computer science; Chromatography; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004496858,0.00009716096,0.0001792879,0.00001394415,0.0003614836,0.0001311422,0.00020529,0.00003496332,0.00004040226],"category_scores_gemma":[0.000106262,0.00005348707,0.00001839066,0.0005778778,0.0003455004,0.0004262156,0.0001976119,0.0000767009,0.000002697989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008852517,"about_ca_system_score_gemma":0.00001408876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003234936,"about_ca_topic_score_gemma":0.0001905869,"domain_scores_codex":[0.9987012,0.000216163,0.0001885083,0.0003949332,0.0002046762,0.0002945631],"domain_scores_gemma":[0.9994308,0.0001556091,0.00005816952,0.00005594518,0.0001270426,0.0001724803],"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":[0.0003499433,0.0001240071,0.02612726,0.000003794876,0.000002675109,0.000008404963,0.0001570601,0.0004093525,0.9668489,0.000008482716,0.0001526501,0.005807523],"study_design_scores_gemma":[0.0001742545,0.0009215513,0.9949381,0.00004428104,0.000003495833,0.0000666436,0.001456383,0.001740432,0.0001587393,0.00032818,0.00002514738,0.0001427476],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9986146,0.0001778159,0.000001070457,0.0005357272,0.000027125,0.0005737968,0.00001611212,0.00002250124,0.00003121614],"genre_scores_gemma":[0.9988825,0.00001244319,0.0006623007,0.0002724488,0.00004766734,0.00001042222,0.000001303983,9.326662e-7,0.0001100353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9688109,"threshold_uncertainty_score":0.4890277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0506655681179209,"score_gpt":0.2598429554684234,"score_spread":0.2091773873505025,"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."}}