{"id":"W2766846120","doi":"10.1109/tfuzz.2017.2768327","title":"On the Accuracy–Convergence Tradeoff in Sigmoid Fuzzy Cognitive Maps","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Cognitive Science and Mapping","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Sigmoid function; Convergence (economics); Stability (learning theory); Computer science; Fuzzy logic; Artificial intelligence; Function (biology); Fuzzy cognitive map; Mathematics; Pattern recognition (psychology); Algorithm; Fuzzy set; Machine learning; Fuzzy classification; Artificial neural network","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":[0.01126731,0.000756155,0.0008920603,0.001351064,0.0007890208,0.002114513,0.001190834,0.001985667,0.001027837],"category_scores_gemma":[0.08180747,0.0004847664,0.0006896408,0.0007601909,0.002290867,0.003202451,0.002089502,0.00178529,0.0002587802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001436422,"about_ca_system_score_gemma":0.0009967829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002612828,"about_ca_topic_score_gemma":0.001405172,"domain_scores_codex":[0.996231,0.001739857,0.000278714,0.0004568075,0.0009995574,0.000293931],"domain_scores_gemma":[0.9498046,0.04131027,0.001672577,0.001891126,0.00472945,0.0005919432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001228062,0.0001354285,0.006796479,0.0004586405,0.0001440132,0.0004915391,0.002053281,0.6227891,0.02000906,0.1953595,0.0008186232,0.1497162],"study_design_scores_gemma":[0.00002449776,0.0001717386,0.0008736051,0.00004888745,0.00002095709,0.0001337919,0.00006330972,0.9613343,0.005965807,0.03101519,0.0003223699,0.00002548703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1556481,0.0009716815,0.8385775,0.0008187151,0.00004853309,0.00006534822,0.00003373038,0.000178567,0.003657867],"genre_scores_gemma":[0.8711971,0.0004979555,0.126713,0.000117708,0.00006726402,0.00009106326,0.0000328334,0.00007460609,0.00120843],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01126731,"threshold_uncertainty_score":0.0595879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04452467674384179,"score_gpt":0.2783534962408616,"score_spread":0.2338288194970198,"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."}}