{"id":"W6938900287","doi":"10.6068/dp168fa9d262133","title":"TREND: World Bank. Climate Change Data [Archive]: GHG Emissions and Energy Use | Country: Algeria, Argentina, Australia, Austria, Azerbaijan, Bangladesh, Barbados, Belarus, Belgium, Brazil, Canada, Chile, China, Congo (Brazzaville), Cote D'Ivoire, Denmark, Djibouti, Eritrea, France, Gambia, Germany, Guatemala, Hong Kong, India, Indonesia, Israel, Japan, Kazakhstan, Malaysia, Mexico, New Zealand, Norway, Pakistan, Philippines, Russia, Saudi Arabia, Singapore, South Korea, Switzerland, United Kingdom, United States | Socioeconomic Indicator: CO2 emissions per units of GDP, 1990 - 2008. Data Planet™ Statistical Datasets: A SAGE Publishing Resource Dataset-ID: 051-005-004","year":2019,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Greenhouse gas; Climate change; Oak Ridge National Laboratory; Global warming; Population; Climate change mitigation; Gross domestic product; Energy intensity","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001276471,0.001612459,0.001481294,0.003618724,0.0007340774,0.002704804,0.002379098,0.001436764,0.06171484],"category_scores_gemma":[0.006999402,0.001023273,0.001235814,0.01474353,0.0003850503,0.002400252,0.0016133,0.002668394,0.06083633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002026908,"about_ca_system_score_gemma":0.00375135,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07463107,"about_ca_topic_score_gemma":0.04782407,"domain_scores_codex":[0.9990076,0.0001498827,0.0001783915,0.0002337778,0.0002725646,0.0001577159],"domain_scores_gemma":[0.9957568,0.0006924255,0.0006167244,0.0006509746,0.001966426,0.0003165483],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002292259,0.000009101356,0.0007492436,0.0002960837,0.00002018205,0.000007410395,0.00001265219,0.0001447102,0.00002309186,0.0002594015,0.9970585,0.001396743],"study_design_scores_gemma":[0.0001804942,0.000009420009,0.009249239,0.0003399687,0.00003374317,0.00002565307,0.0001126419,0.0003674971,0.0001855092,0.0007340548,0.9887299,0.00003177923],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005658889,0.00002583035,0.00002727518,0.00006920363,0.00002168566,0.000006380407,0.9993429,0.00009443971,0.0003557421],"genre_scores_gemma":[0.0003928346,0.00006048452,0.0001651412,0.00003882566,0.000008545802,0.00008141387,0.9987232,0.00006655519,0.0004630014],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9253689,"threshold_uncertainty_score":0.2064567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02858198774402804,"score_gpt":0.2636508193926521,"score_spread":0.2350688316486241,"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."}}