{"id":"W2977953675","doi":"10.5194/esd-11-617-2020","title":"Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for central Europe","year":2020,"lang":"en","type":"article","venue":"Earth System Dynamics","topic":"Climate variability and models","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ouranos; Université du Québec à Montréal","funders":"Leibniz-Gemeinschaft; Environment and Climate Change Canada; Bayerische Akademie der Wissenschaften; Bayerisches Staatsministerium für Bildung und Kultus, Wissenschaft und Kunst; Gauss Centre for Supercomputing; Bundesministerium für Bildung und Forschung; Leibniz-Rechenzentrum; Université du Québec à Montréal","keywords":"Climatology; North Atlantic oscillation; Climate model; Environmental science; Precipitation; Climate change; Atmospheric circulation; Forcing (mathematics); Nested set model; Range (aeronautics); Advection; Geography; Meteorology; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.001007362,0.0004949079,0.0005411513,0.000298516,0.0004126994,0.0004582506,0.0006423224,0.0004266017,0.0004118997],"category_scores_gemma":[0.001569693,0.000238524,0.0008012941,0.0003237558,0.0001666415,0.0005327516,0.0004285329,0.0004142141,0.0000624144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007027495,"about_ca_system_score_gemma":0.0008488302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09854639,"about_ca_topic_score_gemma":0.1062776,"domain_scores_codex":[0.9998277,0.0000466048,0.00001098421,0.00006733246,0.00002620008,0.00002110301],"domain_scores_gemma":[0.9994909,0.0001352072,0.00005265424,0.0001072133,0.0001408127,0.00007324742],"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":[0.0003182922,0.0002040484,0.1726745,0.00002566491,0.0008150362,0.0001134137,0.00007511333,0.8062468,0.004814753,0.0003559798,0.0006962574,0.0136601],"study_design_scores_gemma":[0.00002559628,0.00005002153,0.03216724,0.000003126826,0.00006421741,0.000008563938,0.00002392561,0.9669263,0.0004710195,0.00009709947,0.0001507906,0.00001213189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963075,0.00006248041,0.002877795,0.00002541295,0.00001302187,0.000008034231,0.0002629611,0.00005972666,0.0003829762],"genre_scores_gemma":[0.9970039,0.00001835206,0.002061818,0.000009231568,0.000004899936,0.000007277721,0.0007857324,0.000009522802,0.00009940453],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09854639,"threshold_uncertainty_score":0.1959456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07311628844192807,"score_gpt":0.2723802957783595,"score_spread":0.1992640073364314,"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."}}