{"id":"W4393142710","doi":"10.3390/su16072684","title":"Development of a Generic Decision Tree for the Integration of Multi-Criteria Decision-Making (MCDM) and Multi-Objective Optimization (MOO) Methods under Uncertainty to Facilitate Sustainability Assessment: A Methodical Review","year":2024,"lang":"en","type":"review","venue":"Sustainability","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of British Columbia, Okanagan Campus; Okanagan University College; University of British Columbia","funders":"","keywords":"Multiple-criteria decision analysis; Management science; Computer science; TOPSIS; Ranking (information retrieval); Decision tree; Sustainability; A priori and a posteriori; Risk analysis (engineering); Operations research; Engineering; Data mining; Artificial intelligence; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow"],"consensus_categories":["metaresearch","metaepi_narrow"],"category_scores_codex":[0.06587058,0.001474629,0.005898103,0.001866166,0.0006080276,0.0006063175,0.002646536,0.0007677461,0.0001673824],"category_scores_gemma":[0.2228802,0.0008683504,0.001900919,0.005707731,0.0008142812,0.0005755411,0.002747429,0.001007963,0.000004853459],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.005403152,"about_ca_system_score_gemma":0.00755098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009717877,"about_ca_topic_score_gemma":0.0003162939,"domain_scores_codex":[0.9757848,0.008042793,0.008582878,0.003616714,0.002913389,0.001059444],"domain_scores_gemma":[0.8989211,0.07531022,0.003659951,0.004690239,0.0168794,0.000539085],"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":[0.0002475848,0.0003180609,0.000006106153,0.02122634,0.0002083132,0.000004422824,0.002690172,0.006766349,0.000004794313,0.0002996488,0.0001955984,0.9680326],"study_design_scores_gemma":[0.001716992,0.0004414963,0.000642443,0.04569251,0.003437861,0.00004295808,0.01514761,0.5454171,0.00001018309,0.05949146,0.3258402,0.002119173],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00006250757,0.4295616,0.5612336,0.000156334,0.0004901984,0.008256391,0.0001899724,0.00004503737,0.000004357984],"genre_scores_gemma":[0.0004656024,0.2340249,0.7632755,0.00007543529,0.00004209434,0.00188699,0.00004022596,0.00009635326,0.00009287959],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9659134,"threshold_uncertainty_score":0.9998003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3606065260425197,"score_gpt":0.5849824995761069,"score_spread":0.2243759735335872,"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."}}