{"id":"W4319456377","doi":"10.1016/j.engappai.2023.105931","title":"Revisiting the consistency improvement and consensus reaching processes of intuitionistic multiplicative preference relations","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Sichuan Province Science and Technology Support Program; Social Science Planning Project of Shandong Province; Research Grants Council, University Grants Committee; National Social Science Fund of China; National Natural Science Foundation of China","keywords":"Consistency (knowledge bases); Pairwise comparison; Computer science; Multiplicative function; Preference; Group decision-making; Preference relation; Viewpoints; Process (computing); Relation (database); Construct (python library); Data mining; Artificial intelligence; Mathematics; Statistics; Psychology; Social psychology","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":[0.0236305,0.001005338,0.001723095,0.001735818,0.00144788,0.004363869,0.00359208,0.002596354,0.004477374],"category_scores_gemma":[0.09283951,0.001024917,0.002392262,0.001898225,0.005536084,0.01071824,0.003595036,0.00473947,0.0004430208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002588971,"about_ca_system_score_gemma":0.003433278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003053646,"about_ca_topic_score_gemma":0.002139798,"domain_scores_codex":[0.9872376,0.007525433,0.0005808373,0.001577697,0.00245488,0.0006236011],"domain_scores_gemma":[0.9113985,0.07293746,0.003766777,0.005398897,0.005718499,0.0007797406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001717204,0.0001004745,0.0008095517,0.0003433538,0.00009962762,0.00009133595,0.00117398,0.06821799,0.001675735,0.8853666,0.0006005513,0.0413491],"study_design_scores_gemma":[0.00003874131,0.0001077623,0.000485616,0.00004896208,0.00004067356,0.00003663815,0.0001301832,0.283794,0.001109423,0.7131861,0.0009836884,0.0000381172],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06929615,0.0007231268,0.9124571,0.001419477,0.0001243822,0.0001284395,0.00004662072,0.00009644288,0.01570825],"genre_scores_gemma":[0.7964999,0.0004763941,0.19883,0.0003146408,0.0001429065,0.0002154774,0.00006260336,0.00007305302,0.003385082],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0236305,"threshold_uncertainty_score":0.1249715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1881558444961989,"score_gpt":0.3907232188182142,"score_spread":0.2025673743220153,"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."}}