{"id":"W4328053597","doi":"10.1016/j.ocemod.2023.102194","title":"Ocean data assimilation for the initialization of seasonal prediction with the Community Earth System Model","year":2023,"lang":"en","type":"article","venue":"Ocean Modelling","topic":"Climate variability and models","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Northern British Columbia","funders":"Fundamental Research Funds for the Central Universities; National University's Basic Research Foundation of China; Hohai University; National Natural Science Foundation of China","keywords":"Initialization; Data assimilation; Climatology; Environmental science; Sea surface temperature; Benchmark (surveying); Forecast skill; Kalman filter; Computer science; Meteorology; Earth system science; Geology; Oceanography; Artificial intelligence; 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":[0.0007051881,0.0004623858,0.0003910571,0.0003471549,0.0006792519,0.0004309525,0.001189337,0.000586833,0.006215634],"category_scores_gemma":[0.003341182,0.0003772934,0.0004594535,0.0008004606,0.0001833068,0.0008916461,0.0007727414,0.001222585,0.001504516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006329987,"about_ca_system_score_gemma":0.002285821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08932137,"about_ca_topic_score_gemma":0.1067637,"domain_scores_codex":[0.999804,0.00005293773,0.00001271571,0.00003275285,0.0000658469,0.00003179484],"domain_scores_gemma":[0.9994138,0.00009110932,0.00002719911,0.0001279611,0.0002912638,0.00004865145],"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":[0.0006358578,0.000238249,0.008925529,0.0001411547,0.0001509809,0.0001431147,0.0001667446,0.6922703,0.01038081,0.02574765,0.07361591,0.1875837],"study_design_scores_gemma":[0.00003420109,0.000009822588,0.001022904,0.000006719493,0.00000720521,0.000006650246,0.00001177352,0.9896134,0.001931583,0.001988406,0.005354313,0.00001296004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.229587,0.0005614694,0.7144219,0.001066856,0.001210848,0.0002867929,0.0146751,0.01499509,0.02319501],"genre_scores_gemma":[0.5945165,0.0001557072,0.3802208,0.0001125535,0.0001047452,0.0003105804,0.01418117,0.003148885,0.007249232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08932137,"threshold_uncertainty_score":0.1776029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1187838544109711,"score_gpt":0.2702628203405095,"score_spread":0.1514789659295384,"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."}}