{"id":"W3028822835","doi":"10.1177/0015732520920769","title":"The Drivers of Greenhouse Gas Emissions Intensity Improvements in Major Economies: Analysis of Trends 1995–2009","year":2020,"lang":"en","type":"article","venue":"Foreign Trade Review","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada; Environment and Climate Change Canada","funders":"","keywords":"Greenhouse gas; Consumption (sociology); Production (economics); Economics; Environmental science; Natural resource economics; Emission intensity; Agricultural economics; Macroeconomics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003428599,0.0001434425,0.0005438043,0.0000422978,0.00005732926,0.00000492458,0.000320312,0.00004064852,0.001471459],"category_scores_gemma":[0.00006790056,0.00009817095,0.0003352531,0.0008660514,0.0002922183,0.0001242121,0.0001472514,0.0001157538,0.000008850913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001836842,"about_ca_system_score_gemma":0.000008849597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002743122,"about_ca_topic_score_gemma":0.0001422083,"domain_scores_codex":[0.9987045,0.00007673584,0.0005592447,0.0002451644,0.0001884105,0.0002259659],"domain_scores_gemma":[0.9992241,0.00004619415,0.0002305152,0.0003501523,0.000002377429,0.0001466383],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008638602,0.0004349929,0.8378611,0.0008699687,0.0005289898,0.000009410031,0.001509058,0.0005317157,0.001487505,0.0002833748,0.004140327,0.1522572],"study_design_scores_gemma":[0.0007161028,0.0004449711,0.9694647,0.0002356623,0.001424947,0.000001349627,0.001289892,0.002493093,0.001440695,0.0004962913,0.02165535,0.0003370003],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842681,0.005647968,0.0000326005,0.003311319,0.00001664872,0.0006642774,0.00009412384,0.00001720262,0.005947763],"genre_scores_gemma":[0.9883098,0.01097113,0.00009183942,0.0005233619,0.000003709034,0.00001090113,0.0000175552,0.00000756262,0.00006411737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1519202,"threshold_uncertainty_score":0.9994413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01633341180106358,"score_gpt":0.2506297915547067,"score_spread":0.2342963797536431,"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."}}