{"id":"W2139893869","doi":"10.1109/tsmcb.2003.808190","title":"Recursive information granulation: aggregation and interpretation issues","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Granulation; Cluster analysis; Data mining; Relevance (law); Computer science; Granular computing; Set (abstract data type); Fuzzy set; Interpretation (philosophy); Algorithm; Fuzzy logic; Data set; Rough set; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003587225,0.0002795255,0.0002913152,0.0002265103,0.0002769753,0.0006273187,0.0002041798,0.0001740787,0.00001472374],"category_scores_gemma":[0.00001576377,0.0002684167,0.00006096888,0.0002930992,0.000109143,0.0007902171,0.000005189109,0.0002148322,0.00007732557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005296068,"about_ca_system_score_gemma":0.00003027118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008565484,"about_ca_topic_score_gemma":0.00003079797,"domain_scores_codex":[0.998159,0.0002165055,0.0005483399,0.0003945514,0.00038174,0.000299901],"domain_scores_gemma":[0.9988943,0.000112201,0.0002220616,0.0004361612,0.0001637815,0.0001715494],"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":[0.0001464153,0.0004520957,0.0005934802,0.0005105106,0.0002892168,0.00002013794,0.0278413,0.04025946,0.000122269,0.4156787,0.004831067,0.5092553],"study_design_scores_gemma":[0.004790233,0.002137164,0.003279566,0.001077474,0.0002946609,0.0005409314,0.002749679,0.7030863,0.005949919,0.02604568,0.2475121,0.002536286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02588794,0.001122861,0.9595578,0.0005667135,0.001828091,0.0007654425,0.00002066681,0.0002014255,0.01004908],"genre_scores_gemma":[0.993905,0.001029578,0.004002216,0.0002196751,0.00006203513,0.00006090089,0.000009415273,0.00001543717,0.0006957386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.968017,"threshold_uncertainty_score":0.9999768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323313772899707,"score_gpt":0.2280071287875616,"score_spread":0.2147739910585645,"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."}}