{"id":"W2744735327","doi":"","title":"累積走時応答(CTR)交通信号制御アルゴリズムのフィールドでの実現可能性研究【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":[0.0001798739,0.0002671678,0.0002136731,0.0005643145,0.001069739,0.001368833,0.0007597781,0.0005332258,0.02090464],"category_scores_gemma":[0.000399324,0.000162052,0.0002044482,0.0004947459,0.0006307305,0.001003664,0.0007036026,0.0005825582,0.006325651],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001057888,"about_ca_system_score_gemma":0.0006861636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002621182,"about_ca_topic_score_gemma":0.002532778,"domain_scores_codex":[0.9997839,0.0000142424,0.000009016316,0.00005200872,0.00009576593,0.00004491345],"domain_scores_gemma":[0.9997929,0.00001892758,0.00002521693,0.00002629313,0.000111267,0.00002530148],"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":[0.0006059913,0.0002781162,0.009231563,0.000722312,0.00007453896,0.001518837,0.001884408,0.005412083,0.4725156,0.1984463,0.05093976,0.2583705],"study_design_scores_gemma":[0.00004273617,0.0003307783,0.01072568,0.0001288961,0.00009824328,0.001512441,0.001945927,0.0239863,0.453221,0.01578527,0.4920931,0.0001297797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3036142,0.003307507,0.04525745,0.002453309,0.002141215,0.0002947,0.001018564,0.001401515,0.6405115],"genre_scores_gemma":[0.8189735,0.001354381,0.01645031,0.0005315114,0.000313876,0.0002542485,0.0005371693,0.0001400337,0.161445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02090464,"threshold_uncertainty_score":0.06993294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004833862274080227,"score_gpt":0.2130257407435226,"score_spread":0.2081918784694424,"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."}}