{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001691641,0.0003398002,0.0006176649,0.0001808294,0.00008286314,0.00002309664,0.000335447,0.0003219473,0.0002382034],"category_scores_gemma":[0.00006032389,0.0003476431,0.0003208208,0.0004642957,0.00009201017,0.0009995613,0.000002899508,0.001020044,0.00004013082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005815416,"about_ca_system_score_gemma":0.0000878585,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003519761,"about_ca_topic_score_gemma":0.00003131328,"domain_scores_codex":[0.9978057,0.00003584253,0.001209809,0.0002292209,0.0003706197,0.0003487799],"domain_scores_gemma":[0.9988471,0.00007549598,0.0004186219,0.0001726459,0.0002392446,0.0002468416],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001196424,0.0001452001,0.001386087,0.0009893727,0.0006234761,0.0009119502,0.02018173,0.8918208,0.04548035,0.01341577,0.001137154,0.02271163],"study_design_scores_gemma":[0.03144351,0.01598763,0.5701994,0.003794325,0.004393015,0.0005226268,0.08774823,0.0156708,0.06260674,0.07681308,0.1247186,0.006102048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9607932,0.01377498,0.01607894,0.004319195,0.001799969,0.0002827097,0.00009103244,0.0002300476,0.002629953],"genre_scores_gemma":[0.9877391,0.003316443,0.008205639,0.0002039509,0.000425502,0.000002805078,0.00002608981,0.00005022452,0.00003018788],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8761501,"threshold_uncertainty_score":0.9998975,"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."}}