{"id":"W2743114132","doi":"","title":"混雑した交通ネットワークにおけるコードン通行料金レベルのサロゲートベースの最適化【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; Computer science; 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":[0.0001418827,0.0002289101,0.0002007967,0.0004222946,0.0007941751,0.0009794735,0.0006005358,0.0003660299,0.01034741],"category_scores_gemma":[0.0003763328,0.0001288171,0.0001846822,0.0003741163,0.0004112461,0.0007475513,0.0006353777,0.0004608916,0.003534722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007152059,"about_ca_system_score_gemma":0.0005203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001384993,"about_ca_topic_score_gemma":0.001682404,"domain_scores_codex":[0.9998356,0.00001280495,0.00000769136,0.00003118603,0.00007787253,0.0000348303],"domain_scores_gemma":[0.9998357,0.00001866695,0.00002337812,0.00002116616,0.00008116312,0.00001987124],"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":[0.0006404601,0.0002157175,0.00945017,0.000698693,0.00008101716,0.001673933,0.00129441,0.00462106,0.6035828,0.08702261,0.03859311,0.2521261],"study_design_scores_gemma":[0.00003578606,0.0003339471,0.0089184,0.000156339,0.0001292638,0.00186197,0.001350713,0.02560651,0.5838136,0.008767465,0.3689348,0.0000912293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4689764,0.006260082,0.05751487,0.003040109,0.002549591,0.0002729863,0.001028641,0.001672962,0.4586844],"genre_scores_gemma":[0.9009985,0.001970589,0.01358183,0.0004669446,0.0002791673,0.0002217125,0.0004469578,0.0001056919,0.08192851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01034741,"threshold_uncertainty_score":0.03461558,"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."}}