{"id":"W2747447723","doi":"","title":"内因性メンバシップ関数とリスクテイキング行動によるマルチクラスファジィユーザ平衡【Powered by NICT】","year":2016,"lang":"ja","type":"article","venue":"Journal of Advanced Transportation","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Aeronautics; Aerospace engineering; Engineering; Computer science; Systems engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000128167,0.0002233542,0.0001952131,0.0003896061,0.0007865165,0.0009316319,0.0005734718,0.0003623971,0.0108629],"category_scores_gemma":[0.0003212653,0.0001319257,0.0001850799,0.0003709315,0.0004202781,0.0007154025,0.0006284587,0.0004447245,0.003330532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006286899,"about_ca_system_score_gemma":0.000446685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001235142,"about_ca_topic_score_gemma":0.001594891,"domain_scores_codex":[0.99985,0.00001018433,0.000006277745,0.00002898833,0.000072016,0.00003250445],"domain_scores_gemma":[0.9998634,0.00001472453,0.00001987733,0.00001855886,0.0000654877,0.00001798475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005757007,0.0001959852,0.006558264,0.0006531362,0.00006954963,0.001373519,0.001037051,0.005154854,0.667711,0.08425621,0.0332693,0.1991454],"study_design_scores_gemma":[0.00003406297,0.0002976015,0.006939658,0.000122109,0.0001037806,0.001312848,0.001150981,0.03144773,0.6331151,0.008375368,0.3170167,0.00008416873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4812202,0.005143496,0.05475433,0.002632834,0.002751469,0.000259776,0.0009390028,0.00150625,0.4507927],"genre_scores_gemma":[0.9071928,0.001481895,0.01232317,0.0003983698,0.0002238755,0.0001797062,0.0004228115,0.00009103924,0.07768634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0108629,"threshold_uncertainty_score":0.03634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004916072688546676,"score_gpt":0.2133579776355112,"score_spread":0.2084419049469645,"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."}}