{"id":"W2918303983","doi":"10.1016/j.jenvman.2019.01.081","title":"Are per capita carbon emissions predictable across countries?","year":2019,"lang":"en","type":"article","venue":"Journal of Environmental Management","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"Harvard Kennedy School; Harvard T.H. Chan School of Public Health; Chinese Academy of Sciences; Harvard University; World Bank Group","keywords":"Per capita; Greenhouse gas; Industrialisation; China; Economics; Kuznets curve; Population; East Asia; Developing country; Geography; Agricultural economics; Environmental science; Natural resource economics; Economy; Economic growth; Ecology; Demography","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00059504,0.000292277,0.0003626959,0.00005161249,0.0001533511,0.00005429898,0.0004963681,0.00009784468,0.01765075],"category_scores_gemma":[0.000008520172,0.0002468247,0.0002052103,0.00007041405,0.0002843914,0.0005017816,0.0007456031,0.0003035223,0.001316467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001446822,"about_ca_system_score_gemma":0.000004719037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000287619,"about_ca_topic_score_gemma":0.000006150166,"domain_scores_codex":[0.9973828,0.00007704204,0.0005986107,0.0003499929,0.0009975093,0.0005940759],"domain_scores_gemma":[0.9985151,0.00003068237,0.0006319787,0.000507266,0.000003048685,0.0003119844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001125386,0.0005395634,0.9895578,0.00005058674,0.00009440304,0.0001336438,0.0007274316,0.002031844,0.003720684,0.00001498947,0.001947822,0.001068669],"study_design_scores_gemma":[0.001119689,0.0002574134,0.92291,0.00003517647,0.00006854516,0.00005354086,0.006830337,0.000120637,0.000649237,0.0002018873,0.06746241,0.000291174],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9818689,0.0002347093,0.00002640923,0.0002888606,0.0003629877,0.0004887647,0.00002484775,0.00001435461,0.01669023],"genre_scores_gemma":[0.985007,0.0002943178,0.0003593106,0.0004708605,0.00006115729,0.000007997372,0.000003097535,0.00003323426,0.01376296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06664786,"threshold_uncertainty_score":0.9999984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003922100851333336,"score_gpt":0.2192572136732684,"score_spread":0.2153351128219351,"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."}}