{"id":"W1964725806","doi":"10.1016/j.jcp.2012.08.032","title":"A class of semi-implicit predictor–corrector schemes for the time integration of atmospheric models","year":2012,"lang":"en","type":"article","venue":"Journal of Computational Physics","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Environment and Climate Change Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predictor–corrector method; Nonlinear system; Euler equations; Applied mathematics; Stability (learning theory); Class (philosophy); Shallow water equations; Testbed; Computer science; Grid; Mathematics; Mathematical analysis; Geometry; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002931744,0.00006609492,0.0001828257,0.0000103487,0.00005536462,0.000008903406,0.0001261053,0.00002797708,0.0001196576],"category_scores_gemma":[0.00006179103,0.0000387998,0.0001225245,0.0001414712,0.00005505838,0.0003131172,0.00000490181,0.00007923586,0.000003116741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004607972,"about_ca_system_score_gemma":0.00005580012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008802944,"about_ca_topic_score_gemma":8.749009e-7,"domain_scores_codex":[0.9991692,0.00004344589,0.0003677219,0.0000433227,0.0002782173,0.0000980871],"domain_scores_gemma":[0.997868,0.001272327,0.0004419537,0.00005203236,0.0003136292,0.0000520847],"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":[0.00007150574,0.00004967341,0.006137709,0.000009003203,0.00005488595,3.207196e-8,0.0002140199,0.9785542,0.0001817467,0.004518847,0.0002646113,0.009943734],"study_design_scores_gemma":[0.0002115629,0.000178778,0.04847583,0.00001114086,0.00003856104,0.000001896071,0.00003425729,0.8420991,0.00006626575,0.1086604,0.000184681,0.00003761939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5415624,0.0003093594,0.4573503,0.00008205628,0.0001580626,0.0001067719,0.00005417949,0.00000290764,0.0003739817],"genre_scores_gemma":[0.9848228,0.000005532276,0.0147488,0.00006379782,0.0003031983,4.887594e-7,0.00002930009,0.000002112871,0.00002398738],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4432604,"threshold_uncertainty_score":0.158221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03224510479916055,"score_gpt":0.2506234741125802,"score_spread":0.2183783693134196,"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."}}