{"id":"W2747835451","doi":"","title":"確率的車両到着と乗客の歩行時間が配位した移動の最適化【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":[0.0002239792,0.0002334524,0.0001994665,0.0005346246,0.001044771,0.0009467716,0.0005762316,0.0004191194,0.01145216],"category_scores_gemma":[0.0005361747,0.0001524289,0.0001970181,0.0004097266,0.0005971931,0.0008208057,0.0006498707,0.0005571924,0.003427318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009257358,"about_ca_system_score_gemma":0.000686845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002360646,"about_ca_topic_score_gemma":0.002745647,"domain_scores_codex":[0.9997684,0.00001861108,0.00001146679,0.00004245065,0.0001177009,0.00004132643],"domain_scores_gemma":[0.9997233,0.0000359596,0.00003629417,0.00003733865,0.0001399952,0.00002710679],"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":[0.0007268091,0.0002303573,0.01204674,0.0006174924,0.00007392661,0.001677789,0.001868792,0.003461218,0.6369599,0.08672853,0.02857779,0.2270308],"study_design_scores_gemma":[0.0000284959,0.0002676446,0.0100626,0.0001255646,0.00009656278,0.00154032,0.001429442,0.01156278,0.6783456,0.006359741,0.2901,0.00008126348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4526089,0.005454815,0.05657232,0.002111248,0.001977778,0.0003358331,0.0009636105,0.001253339,0.4787222],"genre_scores_gemma":[0.877115,0.002125527,0.01673232,0.0004551755,0.0002680927,0.0002323393,0.0004844953,0.0001251481,0.1024619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01145216,"threshold_uncertainty_score":0.0383113,"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."}}