{"id":"W2039130949","doi":"10.1109/tfuzz.2006.889765","title":"Discrete Interval Type 2 Fuzzy System Models Using Uncertainty in Learning Parameters","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Simon Fraser University","funders":"","keywords":"Fuzzy set operations; Type-2 fuzzy sets and systems; Fuzzy number; Defuzzification; Fuzzy logic; Fuzzy classification; Mathematics; Fuzzy set; Fuzzy control system; Neuro-fuzzy; Algorithm; Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008794935,0.001083941,0.001169187,0.0008681034,0.0005655152,0.002714603,0.001563129,0.001625857,0.003730887],"category_scores_gemma":[0.002151086,0.0004773881,0.001314948,0.001055334,0.0006640077,0.001472175,0.0007251516,0.001503549,0.0007227768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00141128,"about_ca_system_score_gemma":0.001156146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01184152,"about_ca_topic_score_gemma":0.007372177,"domain_scores_codex":[0.9994343,0.0001430968,0.00003841142,0.000144362,0.0001876332,0.0000522495],"domain_scores_gemma":[0.9994379,0.0002689869,0.00009987307,0.00004474812,0.0001274218,0.00002116596],"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":[0.00005017989,0.00002213117,0.00037235,0.00007091023,0.00004493161,0.000147762,0.0001161038,0.9526622,0.0008486961,0.03005509,0.0005718317,0.01503785],"study_design_scores_gemma":[0.000004403191,0.00001153745,0.00006363455,0.000007519368,0.00001044306,0.0000159029,0.000008518976,0.9920217,0.0002329072,0.00684443,0.0007705911,0.000008396705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01662728,0.0005951777,0.972555,0.0002914777,0.0001029976,0.00006488158,0.000294961,0.0003761606,0.009092079],"genre_scores_gemma":[0.8845887,0.001232906,0.09956715,0.0001064797,0.0001123251,0.000334482,0.0004395534,0.00005945352,0.01355906],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01184152,"threshold_uncertainty_score":0.02354515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03580579793276814,"score_gpt":0.2565192484391173,"score_spread":0.2207134505063491,"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."}}