{"id":"W3201254245","doi":"10.1029/2021gl094777","title":"Role of Mixed‐Layer Instabilities in the Seasonal Evolution of Eddy Kinetic Energy Spectra in a Global Submesoscale Permitting Simulation","year":2021,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Agence Nationale de la Recherche","keywords":"Geostrophic wind; Enstrophy; Kinetic energy; Spectral line; Mixed layer; Power law; Turbulence; Physics; Flattening; Atmospheric sciences; Turbulence kinetic energy; Scaling; Climatology; Geology; Mechanics; Meteorology; Classical mechanics; Vorticity; Vortex","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005732485,0.0005944989,0.0004152797,0.0004042421,0.0004910223,0.0008621837,0.00050933,0.0007523599,0.0008830678],"category_scores_gemma":[0.001665166,0.0002914754,0.0005261019,0.0003880306,0.0004727404,0.0004566072,0.0004756477,0.0005234505,0.00007431992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005486944,"about_ca_system_score_gemma":0.0007211555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02703889,"about_ca_topic_score_gemma":0.01566366,"domain_scores_codex":[0.9998949,0.00004074519,0.000006907107,0.00001829126,0.00001519089,0.00002400331],"domain_scores_gemma":[0.9994851,0.0002532796,0.00005959563,0.00004619622,0.00005714593,0.0000985631],"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":[0.0002849634,0.0002035229,0.04912997,0.00003026676,0.0001068884,0.0002550423,0.00007922586,0.9380746,0.007530575,0.001033306,0.000431415,0.002840135],"study_design_scores_gemma":[0.00003770812,0.00005363332,0.01042596,0.000004863225,0.00001791208,0.00001216649,0.00003766306,0.9881741,0.0009023363,0.0001658128,0.0001544975,0.00001334415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971744,0.00002658595,0.0009711445,0.00009300378,0.00001016475,0.00001106175,0.0002203299,0.00009450568,0.001398808],"genre_scores_gemma":[0.9991782,0.0000124446,0.0005357364,0.00001634762,0.000001865276,0.000006604866,0.0001209566,0.00002029076,0.0001074283],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02703889,"threshold_uncertainty_score":0.05376297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01632062394622241,"score_gpt":0.26000219680932,"score_spread":0.2436815728630976,"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."}}