{"id":"W2086917595","doi":"10.1016/j.eswa.2010.10.039","title":"An OWA-TOPSIS method for multiple criteria decision analysis","year":2010,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Ministry of Education of the People's Republic of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"TOPSIS; Ideal solution; Decision maker; Multiple-criteria decision analysis; Computer science; Mathematical optimization; Extreme point; Robustness (evolution); Similarity (geometry); Ideal (ethics); Data mining; Mathematics; Operations research; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004294031,0.001484367,0.002720865,0.005634909,0.001599517,0.002927671,0.001487822,0.001146206,0.007955798],"category_scores_gemma":[0.008919491,0.0006976284,0.002994247,0.007703932,0.0007423738,0.002166602,0.001977792,0.001631178,0.001872284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001074154,"about_ca_system_score_gemma":0.003538446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004052911,"about_ca_topic_score_gemma":0.006556313,"domain_scores_codex":[0.9919013,0.00281661,0.0006422045,0.0004326517,0.003959087,0.0002481289],"domain_scores_gemma":[0.9972852,0.001306847,0.0001193642,0.0001610471,0.00106132,0.00006626544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001641175,0.0002414322,0.0009054092,0.001556497,0.0005978862,0.0002076476,0.0003625449,0.03675151,0.007852859,0.04159779,0.004961778,0.9048005],"study_design_scores_gemma":[0.0001619458,0.0006581974,0.003754329,0.0006339398,0.0007560339,0.0008462617,0.0006006893,0.8003483,0.01137332,0.1350686,0.04544248,0.0003558645],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002557524,0.0003952938,0.9924555,0.00008625167,0.0001368535,0.0002236718,0.0001308113,0.0002778148,0.003736203],"genre_scores_gemma":[0.04111115,0.0005149024,0.9546404,0.00006329857,0.0000449057,0.0005861896,0.0002085647,0.00006320977,0.002767366],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007955798,"threshold_uncertainty_score":0.02661479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1021920778304559,"score_gpt":0.4924242896579465,"score_spread":0.3902322118274906,"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."}}