{"id":"W4416675808","doi":"10.1002/asjc.3891","title":"Knowledge learning and empirical research of improved pan‐logical fuzzy sets","year":2025,"lang":"en","type":"article","venue":"Asian Journal of Control","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Arts Foundation; University of Toronto","funders":"Division of Graduate Education; Nanjing University; Government of Jiangsu Province; Nanjing University of Finance and Economics; National Natural Science Foundation of China","keywords":"Negation; Fuzzy logic; Premise; Membership function; Fuzzy set; Fuzzy classification; Fuzzy set operations; Defuzzification; Type-2 fuzzy sets and systems","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":[0.002792532,0.0001217352,0.0005272499,0.00031117,0.0001410821,0.0001140463,0.0006731589,0.0001212887,0.000002938648],"category_scores_gemma":[0.0006978431,0.00008622142,0.0001492127,0.0004487273,0.0001508393,0.000211871,0.0001422411,0.0006853546,0.000005421839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004233415,"about_ca_system_score_gemma":0.0003545676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004058254,"about_ca_topic_score_gemma":0.000003353866,"domain_scores_codex":[0.9975386,0.0009252484,0.0006379372,0.0002114101,0.000344382,0.0003424543],"domain_scores_gemma":[0.998041,0.0006902959,0.0002675356,0.0002010968,0.0006442067,0.0001558697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007543779,0.0005115994,0.04860209,0.000163416,0.0005535688,0.0002331672,0.003422444,0.00002943924,0.01504837,0.1243988,0.004462022,0.8018208],"study_design_scores_gemma":[0.05418574,0.01597935,0.3946041,0.002060612,0.0003827436,0.001686444,0.006896398,0.06888109,0.001109531,0.3430506,0.1095998,0.001563674],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1706266,0.03150585,0.3531682,0.05447495,0.002585084,0.001460124,0.000004964979,0.0001370729,0.3860371],"genre_scores_gemma":[0.9984317,0.00002709671,0.0008809167,0.000106709,0.0001244109,0.000004893544,7.912028e-8,0.000004300916,0.0004198979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8278051,"threshold_uncertainty_score":0.3516007,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03961606750072557,"score_gpt":0.3671416767403054,"score_spread":0.3275256092395799,"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."}}