{"id":"W2748429030","doi":"","title":"自動車追従模型における重い車両を含むモデル化,キャリブレーションと検証【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; Environmental 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":[0.0002584974,0.0002679548,0.0002134095,0.0006189036,0.001055552,0.001105617,0.0004790054,0.000440564,0.006024444],"category_scores_gemma":[0.0006464192,0.0001703559,0.0002250577,0.0004789643,0.0007958573,0.0008803525,0.000523704,0.0005531273,0.00195861],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008356196,"about_ca_system_score_gemma":0.0007633239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00183479,"about_ca_topic_score_gemma":0.001782316,"domain_scores_codex":[0.9997253,0.00002753482,0.00001649612,0.00005384242,0.0001292486,0.00004761248],"domain_scores_gemma":[0.9996767,0.00005246797,0.0000443693,0.00004142791,0.0001619429,0.00002310125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006794232,0.0001966197,0.01642542,0.001070565,0.0001087043,0.002531207,0.002519274,0.003959001,0.5100814,0.1586362,0.0217951,0.281997],"study_design_scores_gemma":[0.00002629216,0.0002295735,0.01153126,0.0001364085,0.0001316952,0.002327987,0.001931758,0.009771142,0.6722443,0.01373245,0.2878397,0.00009755875],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4853279,0.009017648,0.0682085,0.002123822,0.001805898,0.0002732403,0.0009782712,0.001027184,0.4312376],"genre_scores_gemma":[0.9165086,0.003345961,0.02024424,0.0003871472,0.0003046544,0.0002394409,0.0005008385,0.0001008424,0.05836838],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006024444,"threshold_uncertainty_score":0.02015382,"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."}}