{"id":"W2299931474","doi":"10.3233/ifs-151858","title":"Generalized statistically convergent sequences of fuzzy numbers","year":2016,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Approximation Theory and Sequence Spaces","field":"Mathematics","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Mathematics; Sequence (biology); Fuzzy logic; Cauchy distribution; Fuzzy number; Object (grammar); Cauchy sequence; Pure mathematics; Applied mathematics; Discrete mathematics; Fuzzy set; Artificial intelligence; Mathematical analysis; Computer science","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":[0.002040691,0.0002164876,0.0007063531,0.0001733919,0.00005844178,0.00004033547,0.0004168057,0.0001138111,0.0004622666],"category_scores_gemma":[0.0008828213,0.0001239257,0.0002508366,0.0001480977,0.0002314485,0.0002712521,0.00003313127,0.0001409396,0.00006416248],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001585565,"about_ca_system_score_gemma":0.000194211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003263561,"about_ca_topic_score_gemma":0.000005302135,"domain_scores_codex":[0.996642,0.0004337432,0.001733185,0.0001612241,0.0007586422,0.000271229],"domain_scores_gemma":[0.9959618,0.0009697613,0.001791524,0.0002718422,0.000798366,0.0002066661],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002844266,0.0001871202,0.0008275833,0.0004556235,0.000339869,0.00006492409,0.001636646,0.00009743944,0.0369986,0.9505566,0.006944401,0.001606742],"study_design_scores_gemma":[0.001542202,0.001086493,0.00005268165,0.002736208,0.0002956167,0.0007789088,0.008785,0.0002047767,0.1488538,0.8281481,0.006881075,0.0006351192],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7251025,0.001417617,0.2639431,0.0007610157,0.00259491,0.0005393015,0.00007930378,0.00004501032,0.005517188],"genre_scores_gemma":[0.986575,0.000451579,0.0111929,0.0000323998,0.0002645928,0.000008160635,0.000001004369,0.000025313,0.001449071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2614725,"threshold_uncertainty_score":0.5061496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05366005566159959,"score_gpt":0.3232235549084178,"score_spread":0.2695634992468182,"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."}}