{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001562538,0.0005578769,0.0007570421,0.0005545869,0.0005202105,0.0007739327,0.001061425,0.0009036125,0.000719665],"category_scores_gemma":[0.007952268,0.0004982877,0.0005352244,0.0004590096,0.0004926426,0.0009631325,0.001003214,0.0009181848,0.0001534482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005520604,"about_ca_system_score_gemma":0.0009622827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02011591,"about_ca_topic_score_gemma":0.01713882,"domain_scores_codex":[0.9996798,0.0001396806,0.00003065943,0.00006846849,0.00003992113,0.00004149569],"domain_scores_gemma":[0.9985775,0.0006348551,0.0001672428,0.0003229629,0.0002307057,0.00006672023],"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":[0.00008641213,0.00003435372,0.008884539,0.00001862905,0.00005822832,0.00003589855,0.00004722741,0.974586,0.002564888,0.0009686286,0.0002501315,0.01246514],"study_design_scores_gemma":[0.00001340176,0.00001697516,0.0008346842,0.000002384351,0.000006378884,0.000003909335,0.000003639632,0.998172,0.0005097867,0.0002661068,0.0001640035,0.000006658443],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7325483,0.0002197476,0.2626336,0.0003077004,0.0002117084,0.00006190284,0.0003736621,0.001266453,0.002377027],"genre_scores_gemma":[0.9379646,0.00008696322,0.06055612,0.00008097627,0.00003101011,0.00006369711,0.0003188627,0.0001652004,0.0007325821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02011591,"threshold_uncertainty_score":0.03999764,"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."}}