{"id":"W4377042403","doi":"10.30564/mmpp.v5i2.5578","title":"An Effective Compromising Ranking Technique for Decision Making","year":2023,"lang":"en","type":"article","venue":"Macro Management & Public Policies","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Canada West","funders":"","keywords":"VIKOR method; TOPSIS; Regret; Decision matrix; Ranking (information retrieval); Ideal solution; Computer science; Multiple-criteria decision analysis; Process (computing); Data mining; Matrix (chemical analysis); Operations research; Mathematical optimization; Artificial intelligence; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005091439,0.001638422,0.001765008,0.003696395,0.001548299,0.002282207,0.001513129,0.001164309,0.007302321],"category_scores_gemma":[0.009576733,0.0004150858,0.001677722,0.003615632,0.0009306357,0.002645725,0.002039361,0.001733629,0.002189214],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008906679,"about_ca_system_score_gemma":0.001626829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001015376,"about_ca_topic_score_gemma":0.001769396,"domain_scores_codex":[0.9918425,0.00343166,0.0003328533,0.0007153797,0.003442315,0.0002353408],"domain_scores_gemma":[0.9971423,0.001505292,0.0002505183,0.0002847344,0.0007584479,0.00005863271],"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":[0.0001067319,0.0001309241,0.0007044619,0.001012042,0.0002024797,0.0002363424,0.0006807151,0.06254331,0.01242903,0.1938456,0.006744816,0.7213635],"study_design_scores_gemma":[0.00006742872,0.0006951644,0.001375608,0.0004472305,0.0002428296,0.001154697,0.000524176,0.7011573,0.01531165,0.1887121,0.09012387,0.0001878872],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001972746,0.0003653385,0.9911446,0.000150765,0.00008088165,0.0001012599,0.00003388576,0.0002037977,0.005946666],"genre_scores_gemma":[0.05163108,0.000561684,0.9438506,0.000114886,0.00006221205,0.0002210049,0.00008221222,0.00007242068,0.003403897],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007302321,"threshold_uncertainty_score":0.02692646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1301975321490733,"score_gpt":0.470276085463908,"score_spread":0.3400785533148347,"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."}}