{"id":"W3160363957","doi":"","title":"中国,北京の行政区域における道路自動車テール数限界の大気質に及ぼす多重要因の影響【JST・京大機械翻訳】","year":2020,"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":"Computer science","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":[0.001141515,0.0002790294,0.0002030969,0.0009354179,0.001863946,0.002734498,0.0003874905,0.0007500698,0.01181778],"category_scores_gemma":[0.002654443,0.0002352636,0.000300906,0.0005727198,0.002703436,0.002338692,0.0006810906,0.001050561,0.003049655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001592498,"about_ca_system_score_gemma":0.002126763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007436185,"about_ca_topic_score_gemma":0.006962134,"domain_scores_codex":[0.9991975,0.00009004778,0.00006258854,0.0001554127,0.0003992248,0.00009526082],"domain_scores_gemma":[0.9981792,0.0003536328,0.0002181334,0.0001383038,0.0009421381,0.0001686102],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0003492926,0.0003533008,0.0381062,0.0007092304,0.0001794811,0.001901555,0.01232851,0.003855936,0.06585947,0.5200454,0.03017682,0.3261349],"study_design_scores_gemma":[0.00007815506,0.000520094,0.1262345,0.0003812357,0.0003315586,0.002965758,0.02049352,0.006554545,0.1075523,0.2485488,0.4860842,0.0002553628],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2940372,0.003497411,0.04103991,0.006844488,0.001382224,0.0002048155,0.0006434567,0.000270346,0.6520801],"genre_scores_gemma":[0.8601781,0.002121756,0.01846459,0.0008943723,0.0005620904,0.0001027522,0.0003086603,0.0000822469,0.1172854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01181778,"threshold_uncertainty_score":0.03953445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009294862867174056,"score_gpt":0.2206331719163166,"score_spread":0.2113383090491425,"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."}}