{"id":"W3041899150","doi":"10.1109/tfuzz.2020.3007423","title":"Three-Way Multiattribute Decision-Making Based on Outranking Relations","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":236,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Decision matrix; Computer science; ELECTRE; Table (database); Construct (python library); Rationality; Set (abstract data type); Relation (database); Decision analysis; Contrast (vision); Multiple-criteria decision analysis; Mathematical optimization; Operations research; Data mining; Mathematics; Artificial intelligence; Mathematical 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003394565,0.0003134008,0.0003601001,0.0002508565,0.000650107,0.0003843806,0.0008412996,0.0001787101,0.00003087796],"category_scores_gemma":[0.00002992879,0.0002757767,0.0002570287,0.0009907089,0.00003695477,0.0004076021,0.000004366294,0.0004992269,0.0006490055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001319876,"about_ca_system_score_gemma":0.00008569402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004951002,"about_ca_topic_score_gemma":0.00003933466,"domain_scores_codex":[0.9974113,0.0001202914,0.0005578222,0.0007572356,0.0007139686,0.0004393902],"domain_scores_gemma":[0.9978542,0.0008437724,0.0001574089,0.0008058754,0.0001063521,0.0002323593],"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":[0.00006182935,0.0001850484,0.00008315186,0.00003413843,0.00004425833,0.0000487895,0.0005065686,0.938965,0.00007650031,0.002079965,0.001056671,0.05685809],"study_design_scores_gemma":[0.000705277,0.0003159511,0.0003513478,0.0003019531,0.00002527541,0.000009766641,0.00004306755,0.9954144,0.00009377101,0.0004959594,0.001882341,0.0003608885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008372435,0.0000779634,0.990532,0.002026728,0.002331375,0.0005637334,0.00005841246,0.0005836752,0.002988913],"genre_scores_gemma":[0.9701354,0.000004693352,0.02857917,0.0009956852,0.0001594389,0.00007363389,0.000002366903,0.00003077742,0.00001886803],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9692981,"threshold_uncertainty_score":0.9999694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03344129055900587,"score_gpt":0.2511987735956082,"score_spread":0.2177574830366023,"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."}}