{"id":"W2103654611","doi":"10.1142/s0217595914500249","title":"IMPROVING CONSISTENCY EVALUATION IN FUZZY MULTI-ATTRIBUTE PAIRWISE COMPARISON-BASED DECISION-MAKING METHODS","year":2014,"lang":"en","type":"article","venue":"Asia Pacific Journal of Operational Research","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Natural Resources; University of Alberta","funders":"","keywords":"Pairwise comparison; Consistency (knowledge bases); Vagueness; Computer science; Data mining; Fuzzy logic; Set (abstract data type); Fuzzy set; Reliability (semiconductor); Mathematics; Artificial intelligence","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":[0.04570923,0.001773729,0.003408916,0.004906002,0.001687335,0.003161275,0.004156054,0.001923959,0.00176749],"category_scores_gemma":[0.08702414,0.001095479,0.002787275,0.005284132,0.001559008,0.003785853,0.003743834,0.003338785,0.0003369832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00187716,"about_ca_system_score_gemma":0.002567441,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001712732,"about_ca_topic_score_gemma":0.001230497,"domain_scores_codex":[0.9610442,0.02302888,0.002507726,0.002604009,0.01016567,0.0006496098],"domain_scores_gemma":[0.9359658,0.04834284,0.003343635,0.002771286,0.009067972,0.0005084604],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006459983,0.0003478322,0.0074926,0.001438922,0.001181402,0.0003933453,0.001557737,0.5376955,0.005435409,0.05143807,0.001377181,0.390996],"study_design_scores_gemma":[0.00006393123,0.0002651403,0.001016831,0.0001145243,0.0001272353,0.00009967008,0.0001166515,0.9580546,0.003198762,0.03564505,0.001237339,0.00006029807],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009037234,0.00030991,0.9897841,0.00006160131,0.00003120454,0.0001229937,0.00002599354,0.00009887262,0.0005281692],"genre_scores_gemma":[0.1729704,0.0002269668,0.8257801,0.00006011689,0.00005098411,0.0004166092,0.000125669,0.00007095298,0.0002981428],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04570923,"threshold_uncertainty_score":0.2417365,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4070756605377037,"score_gpt":0.5853305995668792,"score_spread":0.1782549390291755,"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."}}