{"id":"W2744390262","doi":"","title":"異種トラヒック条件下での2車線都市間道路のためのLOSを評価するための新しいアプローチ【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; Computer science; Aerospace 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":[0.0003965608,0.0002700005,0.0002932322,0.0005510264,0.001244552,0.001179958,0.0006232549,0.0004518143,0.009956657],"category_scores_gemma":[0.0009638761,0.0001569918,0.000216295,0.0004309936,0.0006629662,0.0008986623,0.000779209,0.0005721132,0.003605915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008908928,"about_ca_system_score_gemma":0.001086522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002732188,"about_ca_topic_score_gemma":0.003129998,"domain_scores_codex":[0.9996297,0.00004388266,0.00002899504,0.00005898788,0.0001810575,0.00005735115],"domain_scores_gemma":[0.9994915,0.00008056806,0.00006341119,0.00006038903,0.0002622107,0.00004192935],"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":[0.001219512,0.0003940551,0.02004326,0.00090502,0.0001322979,0.002309238,0.003612043,0.003297678,0.4595153,0.06580874,0.03278404,0.4099788],"study_design_scores_gemma":[0.00005632225,0.0006015803,0.0150122,0.000258733,0.0002060214,0.002326608,0.002892372,0.009624761,0.5864861,0.008853572,0.3735633,0.0001183224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5595426,0.008123093,0.07716317,0.003053294,0.002194461,0.0005164133,0.001191819,0.001602349,0.3466128],"genre_scores_gemma":[0.8787591,0.003583586,0.02345288,0.0006776417,0.0003866259,0.0005990784,0.0006782022,0.0001561295,0.09170669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009956657,"threshold_uncertainty_score":0.03330833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004978363180691643,"score_gpt":0.213538702092301,"score_spread":0.2085603389116094,"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."}}