{"id":"W2120373944","doi":"10.1109/coginf.2010.5599759","title":"Granular computing and human-centricity in computational intelligence","year":2010,"lang":"en","type":"article","venue":"","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Granular computing; Computer science; Fuzzy logic; Fuzzy set; Set (abstract data type); Computational intelligence; Cluster analysis; Facet (psychology); Theoretical computer science; Realization (probability); Rough set; Data mining; Artificial intelligence; Mathematics","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":[0.001742701,0.0005293225,0.001036998,0.001107136,0.0008537089,0.005340748,0.0008081798,0.001443028,0.007603757],"category_scores_gemma":[0.005745102,0.000245009,0.0007478152,0.002412462,0.004631368,0.004221084,0.001985682,0.003195714,0.001168326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00209676,"about_ca_system_score_gemma":0.0009747503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001082695,"about_ca_topic_score_gemma":0.001152329,"domain_scores_codex":[0.9986948,0.0004755903,0.0001099453,0.0002679281,0.0003646044,0.00008708975],"domain_scores_gemma":[0.9962898,0.002702108,0.0001828965,0.0004296413,0.0002676933,0.0001278456],"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":[0.00002939891,0.00001639656,0.0002762143,0.0004676061,0.00004421279,0.00006546563,0.0002116627,0.009794024,0.0002635921,0.9284867,0.0103267,0.05001807],"study_design_scores_gemma":[0.000005719531,0.00002586869,0.000220422,0.0001529869,0.00002378549,0.00006555077,0.00009921629,0.01125692,0.0002173309,0.9450076,0.04290556,0.0000189871],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02123841,0.1837578,0.5809276,0.04653855,0.006459082,0.0001324936,0.0005114388,0.00074344,0.1596912],"genre_scores_gemma":[0.6879031,0.1135093,0.1577125,0.004189204,0.008756642,0.0003409268,0.0004507621,0.0003221774,0.02681555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007603757,"threshold_uncertainty_score":0.02543712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0186684238181276,"score_gpt":0.2783161313705388,"score_spread":0.2596477075524112,"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."}}