{"id":"W3211819425","doi":"10.1109/tcyb.2021.3124235","title":"Design of Granular Model: A Method Driven by Hyper-Box Iteration Granulation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Dalian Science and Technology Innovation Fund; Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Granulation; Granular computing; Granularity; Granular material; Fuzzy logic; Partition (number theory)","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":[0.001524587,0.0007516934,0.001103385,0.001139913,0.0006408973,0.00181258,0.00123654,0.001002947,0.002523815],"category_scores_gemma":[0.003990748,0.0005336071,0.001514243,0.0009664753,0.000979375,0.001858477,0.001880412,0.001087455,0.0004537232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009061287,"about_ca_system_score_gemma":0.001424651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003363723,"about_ca_topic_score_gemma":0.002256342,"domain_scores_codex":[0.9988201,0.0003175587,0.0001160252,0.0002419182,0.0003976904,0.0001066799],"domain_scores_gemma":[0.9988759,0.000486143,0.0001255001,0.0002054805,0.0002477393,0.0000591873],"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":[0.0002607399,0.00007963161,0.001912996,0.0003042064,0.00009143889,0.0002898043,0.0004327758,0.7599285,0.01685539,0.06813855,0.002450901,0.149255],"study_design_scores_gemma":[0.00001619808,0.00003456134,0.0001130969,0.00001347472,0.0000150442,0.00004466002,0.00002842231,0.9846458,0.002427681,0.0111169,0.001531801,0.00001243766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004339708,0.00005912601,0.9945582,0.00005067725,0.00001311287,0.00004520655,0.00002857958,0.0002279816,0.0006774135],"genre_scores_gemma":[0.33166,0.0002128225,0.6655736,0.0001090612,0.00002833017,0.0004322116,0.0002310176,0.0001910977,0.00156179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003363723,"threshold_uncertainty_score":0.008442998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02282907568637616,"score_gpt":0.2488427478581698,"score_spread":0.2260136721717936,"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."}}