{"id":"W3087743853","doi":"10.5194/esd-12-253-2021","title":"Climate model projections from the Scenario Model Intercomparison Project (ScenarioMIP) of CMIP6","year":2021,"lang":"en","type":"article","venue":"Earth System Dynamics","topic":"Climate variability and models","field":"Environmental Science","cited_by":774,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Pacific Northwest National Laboratory; Lawrence Livermore National Laboratory; Horizon 2020 Framework Programme; Office of Science; Agence Nationale de la Recherche; Biological and Environmental Research; European Commission; Center for Neuroscience and Regenerative Medicine; U.S. Department of Energy; Deutsches Klimarechenzentrum; Battelle","keywords":"Coupled model intercomparison project; Environmental science; Radiative forcing; Climatology; Climate model; Precipitation; Forcing (mathematics); Climate change; Earth system science; Range (aeronautics); Meteorology; Geology; Geography","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":[0.002662467,0.001474332,0.0003996655,0.001237591,0.0002937652,0.0008101885,0.00107182,0.0007213249,0.006251551],"category_scores_gemma":[0.002794693,0.0003520034,0.0011167,0.004107042,0.000208056,0.001525736,0.0007953569,0.001305126,0.001698071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00136344,"about_ca_system_score_gemma":0.001397074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01567842,"about_ca_topic_score_gemma":0.007644228,"domain_scores_codex":[0.9993863,0.0002685488,0.00003105345,0.0001016409,0.0001411854,0.00007120181],"domain_scores_gemma":[0.9991,0.0001711029,0.0001461324,0.0001447307,0.0003522311,0.00008594809],"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":[0.00150528,0.0002518746,0.03725155,0.001690027,0.001909742,0.0004759131,0.0003108104,0.6087118,0.004374143,0.03029296,0.2246325,0.08859342],"study_design_scores_gemma":[0.001336767,0.0005292966,0.1475382,0.0007260982,0.001120459,0.000653394,0.000669225,0.3906943,0.01728747,0.04400937,0.3949903,0.0004451801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2161819,0.001791661,0.04615346,0.003674783,0.0009399954,0.0003944327,0.6863438,0.002945744,0.04157418],"genre_scores_gemma":[0.4427,0.001685761,0.05856237,0.000538716,0.0001523481,0.001193223,0.4920177,0.0008804497,0.002269496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01567842,"threshold_uncertainty_score":0.0311743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02671044441657204,"score_gpt":0.2507566556391961,"score_spread":0.224046211222624,"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."}}