{"id":"W2998748900","doi":"10.1029/2019ef001354","title":"China's Trade‐Off Between Economic Benefits and Sulfur Dioxide Emissions in Changing Global Trade","year":2020,"lang":"en","type":"article","venue":"Earth s Future","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology; Major Science and Technology Program for Water Pollution Control and Treatment; National Natural Science Foundation of China-Yunnan Joint Fund; Natural Science Foundation of Jiangsu Province","keywords":"China; International trade; East Asia; Economics; Economic integration; Balance of trade; Distribution (mathematics); International economics; Trade barrier; Environmental degradation; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004793235,0.0002176307,0.0002016899,0.0007040322,0.0003703608,0.001090738,0.0001605738,0.0002657715,0.001996097],"category_scores_gemma":[0.0006089892,0.00008738409,0.0005401789,0.001139304,0.0006139387,0.0005817051,0.0006080786,0.0002748056,0.00008422222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001344075,"about_ca_system_score_gemma":0.0006772155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02844915,"about_ca_topic_score_gemma":0.04391463,"domain_scores_codex":[0.9998422,0.00003469249,0.000008174332,0.00002914875,0.0000320832,0.00005371198],"domain_scores_gemma":[0.9996583,0.00008354973,0.0001141055,0.00002424524,0.00005905677,0.00006056575],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002807031,0.00008381352,0.9325222,0.00009270086,0.0002813275,0.0008196715,0.0009191572,0.03167826,0.006626116,0.007695879,0.0008290393,0.01817112],"study_design_scores_gemma":[0.000009189652,0.00006247074,0.9820731,0.00001392134,0.00007205687,0.00003587089,0.001148752,0.01268679,0.0009473482,0.001523147,0.00141163,0.00001573683],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974743,0.00007401295,0.0001544936,0.0001975377,0.000003296079,0.000003024713,0.000119339,0.00000392493,0.00197011],"genre_scores_gemma":[0.999563,0.00003984705,0.00004072876,0.00001453431,0.000001867344,0.000001170195,0.00005572613,6.697148e-7,0.0002824013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02844915,"threshold_uncertainty_score":0.05656713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007532501007667355,"score_gpt":0.213128103583641,"score_spread":0.2055956025759737,"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."}}