{"id":"W2094439646","doi":"10.1016/j.patrec.2015.04.008","title":"Data granulation by the principles of uncertainty","year":2015,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Granulation; Granular computing; Rough set; Data mining; Computer science; Fuzzy set; Fuzzy logic; Mathematics; Artificial intelligence; Engineering","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":[0.0005128459,0.00008383552,0.00009816706,0.00003374304,0.00005345565,0.00007735981,0.0009988825,0.0000260282,0.00001112721],"category_scores_gemma":[0.00004793757,0.00005665733,0.00002585023,0.0001416515,0.00005267869,0.0003882732,0.0002620872,0.00007901516,0.00005963681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001843507,"about_ca_system_score_gemma":0.00001650218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000143883,"about_ca_topic_score_gemma":0.00002039256,"domain_scores_codex":[0.9990097,0.0001136274,0.0002072884,0.0002579995,0.00026487,0.0001465229],"domain_scores_gemma":[0.9989528,0.00008013409,0.0001381826,0.0007208971,0.00005825094,0.0000497371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001259091,0.00009251977,0.005710432,0.00002571511,0.00003793085,0.000006381,0.001007025,0.0007895831,0.001051988,0.0001214255,0.09878639,0.892358],"study_design_scores_gemma":[0.003511763,0.0002705644,0.02473149,0.0001650798,0.00008766204,0.00008419055,0.000386518,0.7892258,0.002223393,0.006818012,0.171358,0.001137596],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1737614,0.00007604221,0.7967699,0.02682284,0.0005002684,0.0002669623,0.0002876615,0.00009824817,0.001416695],"genre_scores_gemma":[0.9868432,0.00001079527,0.004494205,0.007897203,0.0001309507,0.00001240481,0.0005952757,0.000006989453,0.000009016559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8912205,"threshold_uncertainty_score":0.2310419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.170335676372428,"score_gpt":0.281979159625127,"score_spread":0.111643483252699,"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."}}