{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0121927,0.0004789882,0.0007052909,0.0037457,0.001147445,0.003891318,0.00289019,0.0001711773,0.0002629249],"category_scores_gemma":[0.004479735,0.0004038056,0.0003488183,0.004454713,0.0001894476,0.001470301,0.001550508,0.00022615,0.0004133303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002305265,"about_ca_system_score_gemma":0.00003116239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003333329,"about_ca_topic_score_gemma":0.00004660061,"domain_scores_codex":[0.9929456,0.0005037034,0.00134706,0.001405696,0.002496866,0.001301039],"domain_scores_gemma":[0.9910861,0.005704911,0.0005303517,0.001905849,0.0005504638,0.0002222771],"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":[0.000166343,0.0001163722,0.002048978,0.00005683131,0.00007920536,0.00004419134,0.001055296,0.0009780056,0.006466282,0.03621462,0.02422433,0.9285495],"study_design_scores_gemma":[0.002854342,0.0004386901,0.1379637,0.0008085968,0.00008878668,0.00004099351,0.004916933,0.1112493,0.002444922,0.4147042,0.322837,0.001652434],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1852086,0.00004678055,0.7994756,0.001201069,0.001131816,0.00381615,0.00005975067,0.0009319235,0.008128418],"genre_scores_gemma":[0.942589,0.00001157915,0.05439688,0.0008033886,0.0002775717,0.001035713,0.00001991578,0.00008761794,0.0007783683],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9268971,"threshold_uncertainty_score":0.9998414,"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."}}