{"id":"W4238812166","doi":"10.5194/acp-2018-650","title":"Quantifying uncertainty from aerosol and atmospheric parameters and their impact on climate sensitivity","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Climate variability and models","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Radiative forcing; Aerosol; Climate sensitivity; Forcing (mathematics); Environmental science; Climate model; Atmospheric sciences; Radiative transfer; Climatology; Cloud forcing; Earth's energy budget; Sensitivity (control systems); Radiative flux; Climate change; Atmospheric model; Meteorology; Physics; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002654829,0.000935996,0.0004307634,0.0009131551,0.0003749659,0.001065687,0.0005448138,0.0008108875,0.001090659],"category_scores_gemma":[0.01414328,0.000364863,0.0008720917,0.0009843671,0.0007829805,0.001481524,0.001325074,0.0007327375,0.00007289464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007843303,"about_ca_system_score_gemma":0.0006843343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01090407,"about_ca_topic_score_gemma":0.007496487,"domain_scores_codex":[0.9990833,0.0004594551,0.00005435734,0.0001374202,0.0001724309,0.00009306991],"domain_scores_gemma":[0.9955311,0.003065951,0.0004195121,0.0005957393,0.0002880939,0.00009958015],"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":[0.0001418483,0.00006775239,0.1318893,0.00007002679,0.0006003174,0.0001114747,0.00008090794,0.8466378,0.004132133,0.00421367,0.0003919068,0.01166284],"study_design_scores_gemma":[0.00004763468,0.00009861986,0.08608234,0.0000413626,0.0002130766,0.00005522832,0.00009006516,0.8930141,0.006512142,0.01268385,0.001091971,0.0000696498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816585,0.000249254,0.0146959,0.0004022099,0.0000318187,0.00002139077,0.000516746,0.0001073364,0.002316967],"genre_scores_gemma":[0.9986625,0.00003212904,0.001058597,0.00002144646,0.000006706962,0.000004510353,0.0001341701,0.00001299756,0.00006697045],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01090407,"threshold_uncertainty_score":0.02168119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04303493640123808,"score_gpt":0.2803560012176631,"score_spread":0.237321064816425,"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."}}