{"id":"W4411577401","doi":"10.5194/egusphere-2025-2653","title":"Historical Climate and Future Projection in the North Atlantic and Arctic: Insights from EC-Earth3 High-Resolution Simulations","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Geological Studies and Exploration","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"HORIZON EUROPE Climate, Energy and Mobility; HORIZON EUROPE Framework Programme; Vetenskapsrådet; Svenska Forskningsrådet Formas; European Commission","keywords":"Arctic; The arctic; Projection (relational algebra); Oceanography; Climatology; Resolution (logic); High resolution; Geography; Environmental science; Geology; Computer science; Remote sensing; Artificial intelligence; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008418934,0.0001460371,0.0001982583,0.00006905677,0.0003016734,0.00008513079,0.00007622016,0.0001602303,0.00007413737],"category_scores_gemma":[0.0000273144,0.00008375085,0.00002608597,0.0001543836,0.00003715037,0.00008928987,0.00006615913,0.0003159675,0.000005761605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001958458,"about_ca_system_score_gemma":0.00001638933,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06343081,"about_ca_topic_score_gemma":0.2714351,"domain_scores_codex":[0.9990144,0.0001341476,0.0002159874,0.0003403489,0.0001445758,0.000150538],"domain_scores_gemma":[0.9995489,0.0001955605,0.00007041502,0.0001201194,0.00003293605,0.0000320336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003739963,0.00001854251,0.9834847,0.00004405642,0.00001377622,0.000003038328,0.0007649289,0.009251059,2.194933e-7,0.0002862846,0.0002532932,0.005842679],"study_design_scores_gemma":[0.0001268954,0.00005479287,0.9594678,0.00001534099,0.0000295419,8.029576e-7,0.0002054263,0.02661908,8.563629e-8,0.003659921,0.009712425,0.0001078461],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922827,0.002031359,0.0001379245,0.003888355,0.0005780942,0.0003970584,0.0000510643,0.00002946977,0.000603998],"genre_scores_gemma":[0.9954844,0.002904652,0.0004059269,0.0001864761,0.0003448015,0.000005203865,0.00063403,9.180368e-7,0.0000335682],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2080043,"threshold_uncertainty_score":0.9428059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02371948625021391,"score_gpt":0.2102487683265705,"score_spread":0.1865292820763566,"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."}}