{"id":"W4412375699","doi":"10.1109/tsmc.2025.3580657","title":"Asynchronous Consensus Evolution Mechanism for Large Group Emergency Decision Making: Risk Mitigation Strategy Selection Under Uncertainty","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Selection (genetic algorithm); Mechanism (biology); Asynchronous communication; Computer science; Risk analysis (engineering); Group decision-making; Consensus conference; Operations research; Business; Artificial intelligence; Engineering; Psychology; Computer network","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":[0.001745814,0.0005936909,0.0005475466,0.0005374823,0.0005783653,0.0007472343,0.001389787,0.0007627619,0.00133393],"category_scores_gemma":[0.005091633,0.0002176125,0.0004308455,0.0003678277,0.0005418339,0.001132847,0.001412887,0.0008054693,0.0001536059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006273228,"about_ca_system_score_gemma":0.0009908883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001338968,"about_ca_topic_score_gemma":0.001101012,"domain_scores_codex":[0.9990765,0.0002886221,0.00004629254,0.0002390077,0.0002209838,0.0001286135],"domain_scores_gemma":[0.9979488,0.000977002,0.0003527517,0.0001835734,0.0003901039,0.0001478243],"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":[0.0001598541,0.00007848005,0.001506044,0.00005278714,0.00006296501,0.0001491319,0.0002097743,0.9088051,0.008849934,0.02705692,0.0006075679,0.05246145],"study_design_scores_gemma":[0.00001260341,0.00004204737,0.0001368399,0.000002943168,0.000009127901,0.00001932236,0.00001964472,0.9930708,0.001141708,0.005239614,0.0002993376,0.000006050258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05492372,0.00007500873,0.9424954,0.0001760799,0.00002645493,0.00005184517,0.00002253799,0.0001218459,0.002106977],"genre_scores_gemma":[0.9579653,0.00004982842,0.04082394,0.00005472963,0.00001752249,0.00007859861,0.00002681165,0.0000102111,0.0009731399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001745814,"threshold_uncertainty_score":0.009232879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01028594744035957,"score_gpt":0.2690109434819501,"score_spread":0.2587249960415906,"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."}}