{"id":"W2009611019","doi":"10.1016/j.asoc.2011.06.012","title":"An adaptive neuro-fuzzy approach to risk factor analysis of Salmonella Typhimurium infection","year":2011,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Animal Nutrition and Physiology","field":"Agricultural and Biological Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Public Health Agency of Canada","keywords":"Fuzzy logic; Computer science; Salmonella; Adaptive neuro fuzzy inference system; Neuro-fuzzy; Machine learning; Function (biology); Data mining; Artificial intelligence; Fuzzy control system; Biology","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":[0.0001073359,0.0001127797,0.0002451887,0.00004127926,0.0001403927,0.00001039982,0.0001420251,0.0000755285,0.0000741508],"category_scores_gemma":[0.00001181595,0.00005329252,0.0001051053,0.00079266,0.00003761778,0.00003006513,0.00005524843,0.0001100394,0.00001547646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007982534,"about_ca_system_score_gemma":0.000002091577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006866333,"about_ca_topic_score_gemma":0.00005559756,"domain_scores_codex":[0.9991494,0.00007796958,0.0001956764,0.0003081974,0.00008957327,0.0001792112],"domain_scores_gemma":[0.9995112,0.0001370804,0.0001422843,0.00005940739,0.00005564371,0.000094333],"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":[0.0006251009,0.001401768,0.1159086,0.00001485271,0.0004977329,9.188283e-7,0.002934385,0.004952337,0.753924,0.01024987,0.000135509,0.109355],"study_design_scores_gemma":[0.00006161719,0.0003740143,0.9905869,0.000001637176,0.00008259055,3.341391e-7,0.0002085976,0.007198863,0.0007407098,0.000530916,0.00007641609,0.0001374083],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919285,0.000004789059,0.00321172,0.000005728005,0.00003116928,0.0001470103,0.00003030124,0.00007684121,0.004563973],"genre_scores_gemma":[0.9979258,0.000002786523,0.001763296,0.000174922,0.00008772541,0.000003823579,0.00003803201,0.000001154883,0.000002412646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8746783,"threshold_uncertainty_score":0.2173206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0445290892875637,"score_gpt":0.2307140508047922,"score_spread":0.1861849615172285,"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."}}