{"id":"W2742339066","doi":"","title":"要素増分法による列車移動シミュレーション【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":[0.0001732001,0.0002413117,0.0002154427,0.0004050445,0.0007129429,0.0007080684,0.0005051254,0.0003900465,0.007339403],"category_scores_gemma":[0.0004756428,0.0001443589,0.0002005778,0.0003370744,0.0004427717,0.0006993858,0.0005334002,0.0004366105,0.002516337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005715314,"about_ca_system_score_gemma":0.0004752854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001165071,"about_ca_topic_score_gemma":0.001251937,"domain_scores_codex":[0.9997719,0.00001884325,0.00001140801,0.00004134186,0.0001132002,0.00004322887],"domain_scores_gemma":[0.9997749,0.00003468533,0.00003317385,0.00003278959,0.0001044174,0.00002010203],"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":[0.0007146003,0.0001709628,0.008364901,0.0006213901,0.00006708338,0.001732796,0.0009461271,0.00251415,0.7366419,0.04743674,0.01844025,0.182349],"study_design_scores_gemma":[0.00002351126,0.000263904,0.00600715,0.00008196108,0.00009452357,0.001609454,0.0006874304,0.01213696,0.810277,0.003984749,0.1647726,0.00006078725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.619213,0.005915346,0.07032737,0.002164684,0.002199549,0.0002784507,0.0009769902,0.001764563,0.29716],"genre_scores_gemma":[0.936536,0.001581709,0.01393651,0.0004098953,0.0002162936,0.0001813292,0.0003909753,0.00009725029,0.04665012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007339403,"threshold_uncertainty_score":0.02455282,"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."}}