{"id":"W3048736466","doi":"10.1007/s40815-020-00924-8","title":"Citation Analysis of Fuzzy Set Theory Journals: Bibliometric Insights About Authors and Research Areas","year":2020,"lang":"en","type":"article","venue":"International Journal of Fuzzy Systems","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Computer science; Citation; Field (mathematics); Data science; Set (abstract data type); Fuzzy set; Citation analysis; Management science; Computational intelligence; Bibliometrics; Fuzzy logic; Operations research; Data mining; Mathematics; Artificial intelligence; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006720916,0.0004426736,0.001177987,0.05755227,0.001630305,0.009112543,0.001045409,0.001251426,0.003484361],"category_scores_gemma":[0.06302288,0.000210387,0.000929916,0.06191868,0.0009357856,0.005074237,0.001492169,0.0006868541,0.0005224753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002929348,"about_ca_system_score_gemma":0.002380511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0060054,"about_ca_topic_score_gemma":0.005719276,"domain_scores_codex":[0.9941802,0.001566261,0.0006274917,0.0004036801,0.002940482,0.0002818895],"domain_scores_gemma":[0.9505844,0.03082484,0.006434178,0.001446836,0.009566771,0.00114285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005830849,0.0005684945,0.4899258,0.001575526,0.001491662,0.0004068151,0.004864757,0.01823677,0.006116793,0.05404671,0.007261157,0.4149225],"study_design_scores_gemma":[0.00007598877,0.0004062038,0.6713915,0.0009880129,0.001508061,0.000946142,0.009661967,0.1496786,0.009801606,0.1337492,0.02146063,0.0003319962],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9111111,0.01284899,0.02623532,0.003368248,0.0002468045,0.0001007183,0.002728983,0.0001926265,0.04316719],"genre_scores_gemma":[0.9915717,0.001644478,0.00465548,0.00005620675,0.0002324395,0.00002456559,0.0005980346,0.00001694571,0.001200282],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9424477,"threshold_uncertainty_score":0.03554404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4355136552884411,"score_gpt":0.5309238991282088,"score_spread":0.0954102438397677,"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."}}