{"id":"W2743902469","doi":"","title":"タクシーデータを有する不均質到着過程モデルの調査と経験的評価【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; 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":[0.0001533428,0.0002202484,0.0002118831,0.000430348,0.0008312889,0.00114457,0.000645876,0.0003967576,0.0112084],"category_scores_gemma":[0.0003874433,0.0001544449,0.0001862418,0.0003996874,0.0005424878,0.0008506443,0.0005977341,0.00054569,0.003377365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008158828,"about_ca_system_score_gemma":0.0004631591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001237081,"about_ca_topic_score_gemma":0.0013897,"domain_scores_codex":[0.9998389,0.00001136554,0.000006799136,0.0000343911,0.00007480509,0.00003364567],"domain_scores_gemma":[0.9998434,0.00002038356,0.00002284519,0.00002030092,0.00007357765,0.00001956078],"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":[0.0004667884,0.0001676927,0.00544818,0.0007055252,0.00006940235,0.001183933,0.001246604,0.004345744,0.6512752,0.1486466,0.02713722,0.1593073],"study_design_scores_gemma":[0.00003166762,0.0002092107,0.005992135,0.0001239107,0.00009277518,0.001173037,0.001238923,0.02379208,0.6361313,0.01287272,0.3182536,0.00008858158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4154681,0.006205676,0.05259656,0.002918454,0.002374397,0.0002328801,0.000949919,0.001191929,0.5180621],"genre_scores_gemma":[0.904052,0.001890306,0.01215869,0.0004516617,0.0002595858,0.0001988077,0.0004257399,0.0001138119,0.08044934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0112084,"threshold_uncertainty_score":0.03749579,"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."}}