{"id":"W2750537320","doi":"","title":"信号交差点における自動ほ場で測定した交通コンフリクトの推定におけるParamiicとVISSIMの比較【Powered by NICT】","year":2016,"lang":"ja","type":"article","venue":"Journal of Advanced Transportation","topic":"Educational Robotics and Engineering","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"VisSim; Aerospace engineering; Aeronautics; 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":[0.0001611868,0.0002602993,0.0001673566,0.0004543778,0.0009317709,0.0006774473,0.0005184102,0.0003396966,0.01029088],"category_scores_gemma":[0.0004287038,0.0001428674,0.0001534494,0.00034702,0.0005045626,0.0007000093,0.0005358597,0.0004424645,0.00287811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006231382,"about_ca_system_score_gemma":0.0006743306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001475673,"about_ca_topic_score_gemma":0.001652165,"domain_scores_codex":[0.9998449,0.00001246445,0.000007828514,0.00002782236,0.00007683891,0.00003006594],"domain_scores_gemma":[0.9997827,0.0000284446,0.00002989812,0.00002793282,0.0001021173,0.00002886492],"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":[0.001029745,0.0002986102,0.01009657,0.0004739678,0.00005827544,0.001513582,0.001451567,0.001967474,0.6734223,0.05235567,0.01723976,0.2400925],"study_design_scores_gemma":[0.00004068203,0.0004568996,0.01223425,0.00006946675,0.00008503252,0.002381413,0.001271578,0.009726229,0.7922564,0.007115946,0.1742915,0.00007062538],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6497095,0.003018778,0.06475014,0.002084429,0.001147462,0.0002957003,0.001006513,0.001717357,0.2762702],"genre_scores_gemma":[0.9155216,0.001397811,0.01798561,0.0003049987,0.0001652087,0.0002478383,0.0005203886,0.0001286149,0.06372786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01029088,"threshold_uncertainty_score":0.03442633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008184981283430072,"score_gpt":0.2424546711430718,"score_spread":0.2342696898596417,"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."}}