{"id":"W2892760810","doi":"10.1175/jcli-d-18-0023.1","title":"How Well Are Clouds Simulated over Greenland in Climate Models? Consequences for the Surface Cloud Radiative Effect over the Ice Sheet","year":2018,"lang":"en","type":"article","venue":"Journal of Climate","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Centre National d’Etudes Spatiales; National Aeronautics and Space Administration; National Science Foundation","keywords":"Greenland ice sheet; Radiative transfer; Atmospheric sciences; Longwave; Environmental science; Cloud fraction; Climate model; Cloud cover; Cloud forcing; Climatology; Lidar; Ice sheet; Shortwave; Cloud top; Radiative flux; Meteorology; Climate change; Cloud computing; Geology; Remote sensing; Satellite; Geography; Physics; Astronomy","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":[0.001452413,0.001199043,0.0008587856,0.0005650376,0.0007795974,0.001959587,0.0009711804,0.001390888,0.001004532],"category_scores_gemma":[0.00244291,0.0004712442,0.001192738,0.0008249034,0.001002804,0.001068015,0.0006028846,0.0006114049,0.0001758654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003309925,"about_ca_system_score_gemma":0.001911935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2258507,"about_ca_topic_score_gemma":0.1200284,"domain_scores_codex":[0.9995334,0.0001738487,0.0000303601,0.0001166126,0.00003939484,0.0001063709],"domain_scores_gemma":[0.9986395,0.0006244372,0.0001883262,0.0001551613,0.0002325862,0.0001598801],"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":[0.0001368767,0.00007350404,0.04027345,0.00001820359,0.0001433323,0.00007749113,0.00005721783,0.9565075,0.0008770025,0.0003465717,0.0002720813,0.001216833],"study_design_scores_gemma":[0.0001388906,0.00009239987,0.02474056,0.00002377068,0.00007352637,0.00002071185,0.0001531936,0.9726471,0.001122631,0.0005598649,0.0003917835,0.00003552135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970867,0.00006706365,0.0004906831,0.0002084291,0.00001277293,0.00001142984,0.0007111409,0.0001065759,0.001305175],"genre_scores_gemma":[0.998646,0.00004589031,0.000389432,0.00007492569,0.000006195392,0.00000949782,0.0005607368,0.00003237667,0.0002350279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2258507,"threshold_uncertainty_score":0.4490722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01263871817772666,"score_gpt":0.2555155114687628,"score_spread":0.2428767932910362,"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."}}