{"id":"W4360869630","doi":"10.1007/s40747-023-01002-w","title":"A fuzzy rough copula Bayesian network model for solving complex hospital service quality assessment","year":2023,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Fundamental Research Funds for the Central Universities; Postdoctoral Research Foundation of China; Sun Yat-sen University","keywords":"Rough set; Copula (linguistics); Bayesian network; Computer science; Data mining; Fuzzy logic; Computational intelligence; Fuzzy set; Service quality; Bayesian probability; Artificial intelligence; Dominance-based rough set approach; Machine learning; Operations research; Mathematics; Econometrics; Service (business); Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002212301,0.001168856,0.001603236,0.001461607,0.000704727,0.002054229,0.001936382,0.001490199,0.002818957],"category_scores_gemma":[0.004635637,0.0007188051,0.001375768,0.001761026,0.0007952834,0.002348034,0.001206285,0.001592248,0.0003596471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00203394,"about_ca_system_score_gemma":0.002494299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01847003,"about_ca_topic_score_gemma":0.01225677,"domain_scores_codex":[0.9984677,0.0006810646,0.00006720882,0.0002895045,0.0003498704,0.0001446455],"domain_scores_gemma":[0.9989286,0.0005521726,0.0001504759,0.00002848917,0.0002812617,0.00005896719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002980043,0.00002879698,0.0005108663,0.00007812021,0.0000674203,0.0001001406,0.00008456013,0.9415499,0.0003734457,0.04079515,0.0009494105,0.0154323],"study_design_scores_gemma":[0.000004754766,0.000007751783,0.0000665367,0.000006125858,0.00001212765,0.000009245757,0.000009607366,0.9919105,0.00004883562,0.007599064,0.0003183857,0.000007125705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006528704,0.000369792,0.989754,0.0002432163,0.00002975909,0.00004861043,0.00007668469,0.00006425278,0.002885099],"genre_scores_gemma":[0.645075,0.002202261,0.3432863,0.0002312256,0.0001685059,0.0006834782,0.0004833099,0.00007586305,0.007794085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01847003,"threshold_uncertainty_score":0.03672504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1769031604259733,"score_gpt":0.3609447790113868,"score_spread":0.1840416185854135,"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."}}