{"id":"W2162747772","doi":"10.5267/j.dsl.2013.08.004","title":"An application of fuzzy TOPSIS on ranking products: A case study of faucet devices","year":2013,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"TOPSIS; Fuzzy logic; Ranking (information retrieval); Computer science; Engineering; Operations research; Environmental economics; Business; Artificial intelligence; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01129748,0.0003077551,0.0006940524,0.002350916,0.00046135,0.0008027234,0.003693096,0.00007537859,0.0001644328],"category_scores_gemma":[0.007257036,0.0002162899,0.0001146874,0.006739089,0.0006367812,0.002580478,0.0004689026,0.0002059528,0.0002352709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008327047,"about_ca_system_score_gemma":0.00009146009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008810802,"about_ca_topic_score_gemma":0.0001104289,"domain_scores_codex":[0.9884337,0.0004381932,0.002153855,0.001818599,0.006638777,0.0005168908],"domain_scores_gemma":[0.9898067,0.00322417,0.001296487,0.003453099,0.001963011,0.0002565897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00009164798,0.0008155277,0.04278867,0.000006700929,0.000008729195,0.0001004983,0.005777635,0.0029041,0.3902548,0.0001033272,0.0009157629,0.5562326],"study_design_scores_gemma":[0.006465515,0.00310518,0.7022555,0.0003620946,0.00009551697,0.001094318,0.09830545,0.1082235,0.06410909,0.01168224,0.002260775,0.002040738],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714025,0.00001345468,0.02583613,0.0006815199,0.000491435,0.001399063,0.000007086085,0.00004008982,0.0001286925],"genre_scores_gemma":[0.9794804,6.818034e-7,0.01931013,0.001032096,0.00007554297,0.00007360913,7.044001e-7,0.00001882412,0.000007957674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6594669,"threshold_uncertainty_score":0.8820048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1170921785663718,"score_gpt":0.4365821609625899,"score_spread":0.3194899823962181,"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."}}