{"id":"W4414856476","doi":"10.1109/tsmc.2025.3612434","title":"A Spanning Tree-Induced Method to Derive Weights From Fuzzy Preference Relations: A Monte Carlo Simulation-Based Investigation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Advanced Database Systems and Queries","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Spanning tree; Monte Carlo method; Minimum spanning tree; Logarithm; Equivalence (formal languages); Weight; Fuzzy logic","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"],"consensus_categories":[],"category_scores_codex":[0.0004488938,0.0004070006,0.0005651638,0.0005404279,0.0005109802,0.0003803585,0.0003578933,0.0002186073,0.000001953376],"category_scores_gemma":[0.00002842942,0.0003874394,0.00009963374,0.0008649384,0.00004408317,0.0005252088,0.0000114527,0.0002863957,0.00003999039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002292747,"about_ca_system_score_gemma":0.0001944379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004839819,"about_ca_topic_score_gemma":0.0004816975,"domain_scores_codex":[0.9967465,0.0005884218,0.0008931572,0.000920193,0.0004886914,0.0003629749],"domain_scores_gemma":[0.9971833,0.0009991019,0.0003027469,0.0009448394,0.0003027643,0.0002672928],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003446398,0.00005328557,0.0001557292,0.0001721143,0.0001176314,0.000007592252,0.002254257,0.9729084,0.001948272,0.01754313,0.00009098107,0.00471413],"study_design_scores_gemma":[0.0007424048,0.0001565394,0.0005008544,0.001699194,0.00006978535,0.000005155563,0.0008235907,0.988144,0.002432989,0.0002178755,0.004731175,0.0004764707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03622686,0.0005145926,0.9589227,0.0001949758,0.001677649,0.001447433,0.0001242252,0.0003115863,0.00057993],"genre_scores_gemma":[0.9641957,0.000006633612,0.03324068,0.0001444478,0.00008270507,0.0004809206,0.000008295532,0.00002774187,0.001812838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9279689,"threshold_uncertainty_score":0.9998577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03463362853336261,"score_gpt":0.2768043526713014,"score_spread":0.2421707241379388,"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."}}