{"id":"W2120489505","doi":"","title":"Accelerating the Mitigation of Greenhouse Gas Emissions: The Influence of Uncertainties in Economic Growth and Technological Change","year":2008,"lang":"en","type":"article","venue":"Integrated Assessment","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Greenhouse gas; Technological change; Natural resource economics; Environmental science; Reduction (mathematics); Stochastic programming; Climate change; Economics; Environmental economics; Mathematical optimization; Macroeconomics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004397706,0.0001187407,0.0002927736,0.0001504725,0.0001098392,0.00002104291,0.0002442687,0.00009457778,0.00004173361],"category_scores_gemma":[0.0001200748,0.00008025015,0.00004754301,0.000166558,0.0003606295,0.0001830025,0.0000873095,0.000211519,0.00000448946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001066837,"about_ca_system_score_gemma":0.00003265473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003520242,"about_ca_topic_score_gemma":0.0003736133,"domain_scores_codex":[0.9989641,0.00002708071,0.0006248663,0.0001948987,0.00002192156,0.0001671563],"domain_scores_gemma":[0.9991347,0.0001903772,0.0004170482,0.0002130928,0.00002179153,0.00002298132],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002162725,0.0001175749,0.6451823,0.00006129633,0.00005007295,0.000002429414,0.004017687,0.00065722,0.0005754535,0.3475808,0.00008181157,0.001651733],"study_design_scores_gemma":[0.001108761,0.000314561,0.8651404,0.0002735434,0.00001511121,0.00003629834,0.007921642,0.02650784,0.004655144,0.09233714,0.001199334,0.000490179],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958231,0.0004034952,0.00003313443,0.002337589,0.00004357139,0.0002661797,0.00009954868,0.00001440921,0.000978969],"genre_scores_gemma":[0.9962184,0.003242417,0.0002506788,0.0001447244,0.00002286929,0.00009146331,0.000008863723,0.000008991316,0.00001163075],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2552437,"threshold_uncertainty_score":0.5321577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1080151579129345,"score_gpt":0.2884673188181123,"score_spread":0.1804521609051778,"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."}}