{"id":"W2282249876","doi":"10.3233/idt-140227","title":"Granular fuzzy rule-based architectures: Pursuing analysis and design in the framework of granular computing","year":2015,"lang":"en","type":"article","venue":"Intelligent Decision Technologies","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Granular computing; Granularity; Probabilistic logic; Computer science; Fuzzy logic; Exploit; Theoretical computer science; Fuzzy set; Granular material; Data mining; Artificial intelligence; Rough set; Engineering; Programming language","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":[0.002122357,0.0002310185,0.000440805,0.001110301,0.0001091128,0.0002097427,0.002362797,0.0002345078,0.00000132151],"category_scores_gemma":[0.001763446,0.0001426769,0.0001533885,0.003124519,0.0002683536,0.00007833952,0.0004964456,0.0004370683,0.000005072681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000359056,"about_ca_system_score_gemma":0.00004079829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000506442,"about_ca_topic_score_gemma":0.00001059573,"domain_scores_codex":[0.9976836,0.0002109854,0.0005649318,0.000520038,0.0006716127,0.0003488468],"domain_scores_gemma":[0.9963194,0.002142874,0.0002032261,0.001178673,0.0001107017,0.00004513786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003674215,0.0001145089,0.01467296,0.00001096719,0.00003673508,0.00007148212,0.001692233,0.05996084,0.00003599573,0.01104083,0.00009253835,0.9122342],"study_design_scores_gemma":[0.0002553569,0.0003167933,0.003837486,0.0001368046,0.00006107723,0.00001499913,0.001887712,0.1717943,0.009825934,0.8112727,0.0002971072,0.0002996562],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1015328,0.002452322,0.8943212,0.0009539731,0.00009530687,0.0002872525,0.00000141249,0.0002922367,0.00006350481],"genre_scores_gemma":[0.6352814,0.00006827195,0.3645251,0.0001058779,0.000005270014,0.00000817853,7.450649e-7,0.000004722523,3.99588e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9119345,"threshold_uncertainty_score":0.5818197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04353057156004322,"score_gpt":0.2935632493561298,"score_spread":0.2500326777960865,"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."}}