{"id":"W2916176230","doi":"10.3382/ps/pez055","title":"Application of adaptive neuro-fuzzy inference systems to estimate digestible critical amino acid requirements in young broiler chicks","year":2019,"lang":"en","type":"article","venue":"Poultry Science","topic":"Animal Nutrition and Physiology","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Williams","keywords":"Adaptive neuro fuzzy inference system; Broiler; Particle swarm optimization; Mathematics; Animal science; Biology; Fuzzy logic; Computer science; Algorithm; Artificial intelligence; Fuzzy control system","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":[0.000269231,0.0001032712,0.0001703742,0.00003595701,0.00009690639,0.0000430733,0.0004363142,0.00005328344,0.00003823333],"category_scores_gemma":[0.0002504153,0.00004924391,0.00002480277,0.0008504197,0.0002152415,0.0003101849,0.0001140333,0.0001017349,0.0001462054],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003077874,"about_ca_system_score_gemma":0.0000186923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007115285,"about_ca_topic_score_gemma":0.0000650192,"domain_scores_codex":[0.9986709,0.00004902402,0.0002357203,0.0004225053,0.0003013106,0.000320567],"domain_scores_gemma":[0.9993683,0.0001929635,0.00006786361,0.00008888303,0.0001553865,0.0001265842],"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":[0.00002560203,0.00005538485,0.2537359,0.000008472658,4.322596e-7,3.46e-7,0.00003787476,0.00007634518,0.7382161,0.00700665,0.00001116001,0.0008257363],"study_design_scores_gemma":[0.00008261821,0.00041727,0.9930086,0.00005218606,0.000001622052,0.000003599075,0.0002408358,0.002299605,0.003023747,0.0006382058,0.0001077103,0.0001240631],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956326,0.00002184039,0.0001601415,0.000367822,0.0001729143,0.0003220032,0.0000200174,0.00002588174,0.003276726],"genre_scores_gemma":[0.9995222,0.00000366567,0.0002090463,0.0001534021,0.00003589063,0.00002894731,0.000007687963,6.817901e-7,0.00003847002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7392727,"threshold_uncertainty_score":0.2008108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03008156928619945,"score_gpt":0.3091798676842997,"score_spread":0.2790982983981002,"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."}}