{"id":"W2324091296","doi":"10.1021/es3003684","title":"Black Carbon Emissions in China from 1949 to 2050","year":2012,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":313,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"National Development and Reform Commission; National Natural Science Foundation of China","keywords":"Carbon black; Environmental science; China; Greenhouse gas; Carbon fibers; Waste management; Environmental chemistry; Environmental protection; Chemistry; Engineering; Geography; Computer science; Geology; Oceanography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001780346,0.0001200119,0.0001182924,0.00003927623,0.0001411285,0.00001429335,0.0004967447,0.0001055574,0.002530792],"category_scores_gemma":[0.0000352801,0.0001031369,0.00001832534,0.0007573796,0.0008592549,0.0001947425,0.00009799688,0.0001920799,0.0004111665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004223419,"about_ca_system_score_gemma":0.00001552664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001000463,"about_ca_topic_score_gemma":0.0001152367,"domain_scores_codex":[0.9987047,0.000009965147,0.000141581,0.000334509,0.0002333962,0.0005758916],"domain_scores_gemma":[0.9994386,0.00001804075,0.00003663998,0.0002658168,8.245492e-7,0.0002400356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003836143,0.0000367627,0.8654923,4.587822e-7,9.811093e-7,0.000004387044,0.0002810659,0.0003369868,0.125751,0.000007685113,0.00002712043,0.008057341],"study_design_scores_gemma":[0.000101769,0.0000414708,0.9367985,0.0000079028,0.000002431597,0.000006733667,0.0007240712,0.0004687356,0.05731065,0.0002732472,0.004094646,0.0001698841],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898986,0.000177464,0.00001382165,0.0006543107,0.0001310279,0.0001041854,0.00002152052,0.00004605148,0.008953053],"genre_scores_gemma":[0.9983407,0.00002030736,0.001195838,0.00009841722,0.00005261818,0.000002128733,0.00001592494,0.000002311591,0.0002717914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0713061,"threshold_uncertainty_score":0.998381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004853488624925716,"score_gpt":0.1906545389585622,"score_spread":0.1858010503336365,"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."}}