{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001187168,0.0001810024,0.0002033231,0.00002070655,0.0001194479,0.00002750053,0.0001501685,0.0001099993,0.0005173864],"category_scores_gemma":[0.00001308199,0.0001669465,0.00005744912,0.000209446,0.00009665597,0.0002484163,0.0001635055,0.0002099303,0.00004646773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001635125,"about_ca_system_score_gemma":0.000009579768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007468504,"about_ca_topic_score_gemma":0.0001244284,"domain_scores_codex":[0.9988707,0.0000462236,0.000182615,0.0003654958,0.0001303022,0.0004046266],"domain_scores_gemma":[0.9994494,0.00001202299,0.00003990849,0.0001486268,1.788498e-7,0.0003498625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001432629,0.00002311137,0.9246008,0.000009512639,0.000006615492,0.00001263217,0.002374433,0.000858312,0.0003257578,0.00007858362,0.0002085871,0.07148726],"study_design_scores_gemma":[0.0003370364,0.00006074676,0.9858384,0.000008262788,0.000009283923,0.000005345922,0.0007598382,0.0002242966,0.0004251986,0.00005961592,0.01208797,0.0001840186],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980839,0.0004357517,0.000007305951,0.01712845,0.00005482116,0.0002220823,0.00007449566,0.00003449884,0.001203616],"genre_scores_gemma":[0.9988701,0.00009725879,0.0001660932,0.0005227634,0.0002752529,0.00000435411,0.00001431714,0.00001118081,0.00003872288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07130324,"threshold_uncertainty_score":0.6807882,"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."}}