{"id":"W2747416081","doi":"","title":"選択された回廊での周期的待ち行列最適化と組み合わせた運転者の反応【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; Systems engineering; 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":[0.0002021133,0.0002296573,0.0001809496,0.0005160809,0.0009869641,0.001099894,0.0005154882,0.000387094,0.01250304],"category_scores_gemma":[0.000435916,0.0001534974,0.0001773339,0.0004056113,0.0006707803,0.0008266522,0.0006027783,0.0005264462,0.003560521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009475328,"about_ca_system_score_gemma":0.0006621857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002365953,"about_ca_topic_score_gemma":0.00243904,"domain_scores_codex":[0.9997942,0.00001729202,0.000009405402,0.0000399673,0.0001024144,0.00003682557],"domain_scores_gemma":[0.9997806,0.00002750757,0.0000274281,0.00002909377,0.0001134004,0.0000220372],"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":[0.0006246796,0.0002045075,0.009787035,0.0005934376,0.00006265772,0.001417412,0.002058179,0.004241066,0.6118544,0.127498,0.02689836,0.2147604],"study_design_scores_gemma":[0.00002888215,0.0002297685,0.00945705,0.0001127044,0.00008622682,0.001248393,0.001677735,0.01558951,0.6662045,0.009749018,0.2955319,0.00008430178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3972853,0.004422061,0.0519064,0.001951078,0.001605672,0.0002616557,0.0007973976,0.001097078,0.5406734],"genre_scores_gemma":[0.8850915,0.001695123,0.01366403,0.0003263899,0.0002130128,0.0001632249,0.0003672501,0.0001114296,0.09836797],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01250304,"threshold_uncertainty_score":0.04182678,"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."}}