{"id":"W3049229383","doi":"10.1029/2019ms002031","title":"Accelerated Greenland Ice Sheet Mass Loss Under High Greenhouse Gas Forcing as Simulated by the Coupled CESM2.1‐CISM2.1","year":2020,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"EMD Inc. (Canada)","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Council for Eurasian and East European Research","keywords":"Greenland ice sheet; Ice sheet; Climatology; Greenhouse gas; Environmental science; Glacier mass balance; Atmospheric sciences; Albedo (alchemy); Forcing (mathematics); Future sea level; Ice-albedo feedback; Climate model; Ablation zone; Sea ice; Climate change; Cryosphere; Geology; Ice stream; Glacier; Geomorphology; Oceanography","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":[0.000590393,0.001088378,0.0007149592,0.000357186,0.0005652116,0.000824837,0.0009734459,0.001194013,0.001599866],"category_scores_gemma":[0.0007343811,0.0003537032,0.001136875,0.0006332335,0.0005899421,0.0004975219,0.0005483535,0.0007488039,0.0001981271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00198278,"about_ca_system_score_gemma":0.00124301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1139502,"about_ca_topic_score_gemma":0.06134724,"domain_scores_codex":[0.9998451,0.00004778214,0.000006620263,0.00003315123,0.00001641994,0.00005079518],"domain_scores_gemma":[0.9997588,0.00005838151,0.00003121708,0.00003312012,0.00005131046,0.0000671712],"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":[0.0003130844,0.0001240588,0.02693089,0.00004852507,0.000193417,0.0001952517,0.00004735278,0.9642343,0.00450083,0.0007049662,0.001358701,0.001348688],"study_design_scores_gemma":[0.0002995558,0.000122452,0.04297545,0.00001271749,0.00009877435,0.00003183469,0.00009258016,0.9529498,0.002099407,0.0004508328,0.0008079665,0.00005859099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955311,0.00004461904,0.0004938692,0.0002053792,0.00002259756,0.00001423337,0.001671333,0.0001517449,0.001865073],"genre_scores_gemma":[0.9975771,0.00002909353,0.0005545152,0.00006387598,0.000006568348,0.00002190418,0.001395337,0.00003476628,0.0003169005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1139502,"threshold_uncertainty_score":0.2265738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03330951829467412,"score_gpt":0.2506242611543845,"score_spread":0.2173147428597104,"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."}}