{"id":"W2746296944","doi":"","title":"切断混合単純化した位相型分布走行速度に基づく不均一車群流拡散モデル【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; Environmental 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":[0.000206629,0.000225782,0.0001867249,0.0006231082,0.001185588,0.001281756,0.0004584283,0.0003635081,0.01439902],"category_scores_gemma":[0.0004284428,0.0001353539,0.0001730968,0.000451256,0.0008970654,0.0009433001,0.0006344549,0.0005685916,0.003429987],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001235952,"about_ca_system_score_gemma":0.0008519458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003075606,"about_ca_topic_score_gemma":0.002635854,"domain_scores_codex":[0.9997839,0.0000184291,0.000008972313,0.00003976311,0.000106946,0.00004200779],"domain_scores_gemma":[0.9997459,0.00003188141,0.00002915292,0.00003165618,0.000135817,0.00002548575],"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":[0.0004898171,0.0002201327,0.01548891,0.0006087078,0.00007120753,0.001529738,0.0031157,0.004481689,0.4145684,0.2864351,0.03191245,0.2410781],"study_design_scores_gemma":[0.00003041733,0.0002144037,0.01822709,0.0001497496,0.00008588179,0.001707021,0.003181271,0.01384856,0.5026358,0.0263557,0.4334596,0.0001045157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3134884,0.003792392,0.03200281,0.002048253,0.001104402,0.0001635481,0.0006255559,0.0005592093,0.6462155],"genre_scores_gemma":[0.8715891,0.001795838,0.009140247,0.0003302617,0.0002640297,0.0001352006,0.0003232841,0.00009120498,0.1163309],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01439902,"threshold_uncertainty_score":0.04816955,"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."}}