{"id":"W2743142607","doi":"","title":"回帰クリギングを用いた都市域における走行(VMT)推定車両マイル【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; 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":[0.0001434142,0.0002166174,0.0001600965,0.000421984,0.0008529157,0.0008094712,0.0004521602,0.0003419841,0.006586373],"category_scores_gemma":[0.000380925,0.0001320296,0.0001689931,0.0004030124,0.0005607148,0.0007074987,0.000559464,0.0003770149,0.002101034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004889225,"about_ca_system_score_gemma":0.0004259019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0014669,"about_ca_topic_score_gemma":0.001569278,"domain_scores_codex":[0.9998125,0.00001450833,0.000009896246,0.00004557455,0.00008351514,0.00003395635],"domain_scores_gemma":[0.9998301,0.00002130329,0.00002991754,0.00002541052,0.00007645869,0.00001682091],"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":[0.0003958088,0.0001376019,0.01104903,0.0005734161,0.00005598104,0.001613353,0.00152136,0.003319982,0.6195993,0.1125978,0.01556494,0.2335713],"study_design_scores_gemma":[0.00001793045,0.0002223609,0.01039141,0.00008363016,0.00007562795,0.001939479,0.001065863,0.01202589,0.7339571,0.007902627,0.2322512,0.00006694032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5788339,0.004153869,0.07107376,0.001123519,0.00112326,0.0002081878,0.0007027498,0.001011367,0.3417693],"genre_scores_gemma":[0.9314141,0.001171221,0.01682943,0.0001849707,0.0001611846,0.0001142471,0.000307616,0.0000763594,0.04974075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006586373,"threshold_uncertainty_score":0.02203363,"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."}}