{"id":"W2068059066","doi":"10.1016/j.knosys.2014.11.025","title":"Designing granular fuzzy models: A hierarchical approach to fuzzy modeling","year":2014,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Deanship of Scientific Research, King Saud University","keywords":"Granularity; Fuzzy logic; Granular computing; Computer science; Data mining; Hierarchy; Relevance (law); Fuzzy set; Context (archaeology); Fuzzy clustering; Artificial intelligence; Mathematics; Rough set","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002936952,0.0005614993,0.0009342351,0.0004847037,0.0004636731,0.0006569397,0.002194991,0.0002999456,4.532554e-7],"category_scores_gemma":[0.0001410307,0.0004852168,0.0003060231,0.001010879,0.00005734365,0.0004652629,0.0002770782,0.0004305078,0.0003474649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002084167,"about_ca_system_score_gemma":0.0002899378,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002128488,"about_ca_topic_score_gemma":0.00001046186,"domain_scores_codex":[0.9945273,0.001246402,0.0009697112,0.001381224,0.0007933679,0.001082043],"domain_scores_gemma":[0.9967217,0.0003516158,0.0001743212,0.00166353,0.0003965907,0.0006922571],"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":[0.00003291592,0.0002375549,0.00004357665,0.0002758787,0.00003181233,0.000005785434,0.001306309,0.6509085,0.000871553,0.3422949,0.0007131271,0.00327814],"study_design_scores_gemma":[0.001177159,0.0001820749,0.000004166117,0.0002506323,0.00002011641,0.0000238255,0.0001218377,0.9801501,0.00005460031,0.01607057,0.001325499,0.0006194393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001115358,0.001421556,0.8181686,0.0001511463,0.001173663,0.0009819111,0.000003113484,0.0006647514,0.1763198],"genre_scores_gemma":[0.9594471,0.000001513786,0.03840969,0.000244412,0.0008237416,0.0006095707,0.000008935253,0.00005907707,0.0003959613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9583318,"threshold_uncertainty_score":0.99976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03785357154091802,"score_gpt":0.2349199106680351,"score_spread":0.1970663391271171,"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."}}