{"id":"W2081119453","doi":"10.1080/07055900.2014.922240","title":"Reducing Drift and Bias of a Global Ocean Model by Frequency-Dependent Nudging","year":2014,"lang":"en","type":"article","venue":"ATMOSPHERE-OCEAN","topic":"Oceanographic and Atmospheric Processes","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada; Environment and Climate Change Canada; Dalhousie University","funders":"Ministère de la Défense Nationale","keywords":"Argo; Altimeter; Temperature salinity diagrams; Environmental science; Climatology; Sea surface temperature; Salinity; Global temperature; Atmosphere (unit); Atmospheric sciences; Meteorology; Climate change; Geology; Oceanography; Global warming; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004934325,0.0003350636,0.0004298491,0.000003317943,0.0002408377,0.000103339,0.0004109342,0.000145963,0.0002859287],"category_scores_gemma":[0.0001151521,0.0002861682,0.0000988309,0.0004676102,0.0002580221,0.0004135739,0.00004110901,0.0001894612,0.00001588387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009583239,"about_ca_system_score_gemma":0.0001130888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003008363,"about_ca_topic_score_gemma":0.0003117071,"domain_scores_codex":[0.9977984,0.00008441585,0.0005040199,0.0005835093,0.0004693739,0.0005602512],"domain_scores_gemma":[0.9988635,0.0001280639,0.000273071,0.0003537323,0.00008215709,0.0002995023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002583776,0.00003897832,0.9601718,0.00008913098,0.00004889379,0.000005023736,0.0002574335,0.0164042,0.00002682469,0.000617695,0.003485556,0.01882863],"study_design_scores_gemma":[0.001930028,0.0007398135,0.1293734,0.0003132461,0.0002060054,0.0000985263,0.001723906,0.8294102,0.0005819799,0.03232953,0.001785647,0.001507652],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9799969,0.002971549,0.002623062,0.000194219,0.0001684603,0.0001520348,0.00009738815,0.0001266549,0.01366979],"genre_scores_gemma":[0.9909797,0.000240144,0.008076699,0.000336951,0.00008059077,1.711589e-7,0.0000393991,0.00001337366,0.0002329683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8307983,"threshold_uncertainty_score":0.9999591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009980757576730972,"score_gpt":0.2032271835621364,"score_spread":0.1932464259854054,"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."}}