{"id":"W3122578905","doi":"10.1142/s2010007817500063","title":"EMPIRICALLY CONSTRAINED CLIMATE SENSITIVITY AND THE SOCIAL COST OF CARBON","year":2017,"lang":"en","type":"preprint","venue":"Climate Change Economics","topic":"Climate Change Policy and Economics","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Fraser Institute","funders":"","keywords":"Climate sensitivity; Sensitivity (control systems); Monte Carlo method; Econometrics; Climate change; Dice; Environmental science; Climate model; Social cost; Quantile; Economics; Climatology; Mathematics; Statistics; Ecology; Engineering; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003625761,0.0005717259,0.0005902543,0.0009626885,0.0003148264,0.001339025,0.0008188054,0.001224205,0.00221059],"category_scores_gemma":[0.03574321,0.0005147638,0.0004207745,0.001012205,0.001429909,0.002371849,0.001537051,0.001432941,0.00007678816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002065227,"about_ca_system_score_gemma":0.0007167814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0053428,"about_ca_topic_score_gemma":0.003368997,"domain_scores_codex":[0.9987683,0.0007754354,0.00004288754,0.0001862112,0.0001400454,0.00008721153],"domain_scores_gemma":[0.9760236,0.01948943,0.002124883,0.001402418,0.0006574742,0.0003021514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008552038,0.00005723286,0.01764959,0.00005188023,0.000148116,0.00007434825,0.00007223866,0.9256666,0.0003793275,0.04813062,0.0008622661,0.006822375],"study_design_scores_gemma":[0.00001738067,0.00002927747,0.009704493,0.00002689141,0.00003757861,0.000041507,0.00006066387,0.8562145,0.0005886844,0.1324104,0.0008331735,0.00003550675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9159224,0.0007703962,0.06973623,0.001748992,0.00004495204,0.00003536641,0.0005443553,0.0000970382,0.01110025],"genre_scores_gemma":[0.9961957,0.0001524776,0.00310474,0.00005463534,0.00001420711,0.00001360761,0.0001314395,0.00001572136,0.0003174065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0053428,"threshold_uncertainty_score":0.01917511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1970304283651394,"score_gpt":0.3132287358658429,"score_spread":0.1161983075007035,"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."}}