{"id":"W4396624746","doi":"10.3390/atmos15050567","title":"Why Does the Ensemble Mean of CMIP6 Models Simulate Arctic Temperature More Accurately Than Global Temperature?","year":2024,"lang":"en","type":"article","venue":"Atmosphere","topic":"Climate variability and models","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"NOAA Pacific Marine Environmental Laboratory; Global Ocean Monitoring and Observing Program; National Oceanic and Atmospheric Administration","keywords":"Climatology; Mean radiant temperature; Environmental science; The arctic; Meteorology; Data assimilation; Atmospheric sciences; Climate change; 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":[0.003864162,0.0008551201,0.001261648,0.0004099309,0.0005644988,0.002195741,0.001225858,0.001318185,0.001198368],"category_scores_gemma":[0.02208053,0.0005527649,0.0009178066,0.001067511,0.0003736079,0.004456191,0.0007021945,0.001277695,0.0007203988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000839048,"about_ca_system_score_gemma":0.001651505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05010791,"about_ca_topic_score_gemma":0.04961397,"domain_scores_codex":[0.99826,0.0008440383,0.0001084309,0.0003329745,0.0002665824,0.0001880147],"domain_scores_gemma":[0.9954783,0.001627791,0.0004788075,0.000978457,0.001268734,0.0001679438],"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":[0.0003079886,0.00008930088,0.1320545,0.0003150251,0.001539579,0.00007082886,0.0002946987,0.7843975,0.00191868,0.007662492,0.01855318,0.05279611],"study_design_scores_gemma":[0.0001750627,0.0001112569,0.03188174,0.0003399743,0.0003769713,0.00008117835,0.0003504579,0.9160667,0.003808541,0.02459551,0.02203355,0.0001790193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7484983,0.007693841,0.1710878,0.02867063,0.003205607,0.00006878627,0.01004418,0.003614322,0.0271166],"genre_scores_gemma":[0.96501,0.00192126,0.02718792,0.001664764,0.0004534381,0.00004306967,0.002261191,0.0006510603,0.000807357],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05010791,"threshold_uncertainty_score":0.0996325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01714594925833296,"score_gpt":0.2591954223668227,"score_spread":0.2420494731084898,"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."}}