{"id":"W2886115924","doi":"10.1029/2018gl079564","title":"Spatiotemporal Changes of China's Carbon Emissions","year":2018,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; McMaster University; University of Regina; University of Prince Edward Island","funders":"Foundation for Innovative Research Team of Jimei University; University of Prince Edward Island; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"China; Greenhouse gas; Urbanization; Carbon fibers; Environmental science; Climate change; Relaxation (psychology); Horizontal resolution; Climatology; Geography; Meteorology; Geology; Materials science; Economic growth","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003029775,0.0002371491,0.0001867596,0.001116153,0.00022493,0.0005365364,0.0002448799,0.000244051,0.0008570167],"category_scores_gemma":[0.0005553108,0.0001488048,0.0003293938,0.002010864,0.0002121441,0.0003104456,0.0003947103,0.0001582846,0.0001098514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008989362,"about_ca_system_score_gemma":0.00063155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07506213,"about_ca_topic_score_gemma":0.06917398,"domain_scores_codex":[0.9998752,0.00001187183,0.00001320364,0.00004146538,0.00002945309,0.00002882728],"domain_scores_gemma":[0.9996502,0.00003684078,0.0001045399,0.0000505249,0.0001153694,0.00004253592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001009523,0.00003789655,0.9573187,0.00008949728,0.0002887661,0.0003992496,0.0003236578,0.01920675,0.004515588,0.001030101,0.002494972,0.014194],"study_design_scores_gemma":[0.000003059706,0.00001132094,0.9914911,0.000005561736,0.00002154762,0.00003814836,0.0001089607,0.006128392,0.0004716166,0.00006328246,0.001646158,0.00001093068],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994949,0.00009528417,0.0001718904,0.00008056319,0.000005986653,0.000004611389,0.003808048,0.00002751204,0.0008572232],"genre_scores_gemma":[0.9958476,0.00008425757,0.0001575257,0.00001240207,0.000005151365,0.000005640713,0.003579663,0.000002927715,0.0003048767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07506213,"threshold_uncertainty_score":0.1492504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02575582421721788,"score_gpt":0.3246196827889353,"score_spread":0.2988638585717174,"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."}}