{"id":"W2523549523","doi":"10.1002/mcda.1573","title":"A Fuzzy Topsis Method for Prioritized Aggregation in Multi‐Criteria Decision Making Problems","year":2016,"lang":"en","type":"article","venue":"Journal of Multi-Criteria Decision Analysis","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"TOPSIS; Computer science; Fuzzy logic; Multiple-criteria decision analysis; Operations research; Decision-making models; Artificial intelligence; Mathematics","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":["metaresearch","metaepi_narrow","bibliometrics","scholarly_communication","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03431318,0.001061161,0.003909071,0.01138969,0.0004753115,0.002018403,0.003795938,0.0007247498,0.002237742],"category_scores_gemma":[0.06494588,0.0006753276,0.003219732,0.008267242,0.0001972389,0.002924509,0.0008415616,0.0006054597,0.0001841284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008242386,"about_ca_system_score_gemma":0.0003363616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004801339,"about_ca_topic_score_gemma":0.0007893918,"domain_scores_codex":[0.9786829,0.002538992,0.009591697,0.002229906,0.005617048,0.001339421],"domain_scores_gemma":[0.9589014,0.02699537,0.005169726,0.002497839,0.005704516,0.0007311379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003541331,0.000850923,0.01072447,0.00002409925,0.0005893958,0.0001797578,0.001252972,0.001695958,0.05062732,0.00007897618,0.002581553,0.9278532],"study_design_scores_gemma":[0.04389106,0.0008178283,0.1290012,0.005222323,0.002417989,0.0005404545,0.002036873,0.6982159,0.002927318,0.08232643,0.02981576,0.002786806],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1948284,0.0008205127,0.7999437,0.0009663482,0.002187471,0.0009868747,0.000172865,0.00005465089,0.00003909979],"genre_scores_gemma":[0.4481723,0.0001961536,0.5506458,0.0002633055,0.0002533568,0.00004791737,0.000005359715,0.00007950023,0.0003363087],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9250664,"threshold_uncertainty_score":0.9998153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.178617816369056,"score_gpt":0.4952328943117909,"score_spread":0.3166150779427349,"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."}}