{"id":"W2742247866","doi":"","title":"都市地下鉄網の列車遅延と乗客の焦り時間を用いた列車再スケジューリングモデル【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; 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":[0.0002146551,0.0002450001,0.0002020882,0.0005732343,0.001050818,0.001015161,0.000516493,0.0004254843,0.01219941],"category_scores_gemma":[0.0004981183,0.0001675433,0.0002045254,0.0004544026,0.0006383595,0.0008195064,0.000635693,0.0005515334,0.003671656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008765624,"about_ca_system_score_gemma":0.0006115256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001989861,"about_ca_topic_score_gemma":0.002255898,"domain_scores_codex":[0.999777,0.00001677974,0.00001055569,0.00004496217,0.0001117631,0.00003885129],"domain_scores_gemma":[0.9997489,0.00003411896,0.00003513061,0.00003484376,0.0001210743,0.00002586281],"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":[0.0006327555,0.0001973036,0.009705119,0.0005881152,0.00006850306,0.001583325,0.00180074,0.00326715,0.6801057,0.08139675,0.02359588,0.1970585],"study_design_scores_gemma":[0.00002845681,0.0002375112,0.009144101,0.0001092203,0.00008930255,0.001377029,0.001394024,0.01075907,0.7046866,0.006425447,0.2656694,0.00007984085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4464891,0.005167994,0.05292054,0.001798873,0.001857105,0.00031384,0.0009103211,0.001087096,0.4894553],"genre_scores_gemma":[0.873866,0.002044105,0.01572122,0.0004259456,0.0002612925,0.0002118846,0.0004952255,0.0001276186,0.1068467],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01219941,"threshold_uncertainty_score":0.04081106,"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."}}