{"id":"W2030407843","doi":"10.1016/j.mcm.2012.08.011","title":"Local Linearization—Runge–Kutta methods: A class of A-stable explicit integrators for dynamical systems","year":2012,"lang":"en","type":"article","venue":"Mathematical and Computer Modelling","topic":"Numerical methods for differential equations","field":"Mathematics","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Ode; Mathematics; Runge–Kutta methods; Ordinary differential equation; Integrator; Linearization; Applied mathematics; Dynamical systems theory; Differential equation; Exponential integrator; Convergence (economics); Mathematical analysis; Nonlinear system; Differential algebraic equation; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001051176,0.0002412311,0.0006871069,0.00009789147,0.0001029349,0.00005864694,0.0001754521,0.0001585897,0.00001655252],"category_scores_gemma":[0.0001612774,0.0001855648,0.0001560164,0.0001887505,0.00009247089,0.000160969,0.0001222177,0.000175326,0.000004806832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003879669,"about_ca_system_score_gemma":0.0000183849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008696861,"about_ca_topic_score_gemma":2.55839e-7,"domain_scores_codex":[0.9980795,0.0002293752,0.0007839664,0.0002577795,0.0002377423,0.000411655],"domain_scores_gemma":[0.996409,0.002729313,0.0001976868,0.000291509,0.0001542239,0.000218278],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000180097,0.0003865722,0.0000135139,0.0009304947,0.00008268193,1.34998e-7,0.0007722778,0.004213826,0.0002352062,0.983608,0.0000223794,0.009716922],"study_design_scores_gemma":[0.0001603258,0.00004613925,7.068483e-7,0.0001204753,0.00008288749,0.000004620937,0.00009930845,0.6067869,0.0004849458,0.391976,0.0001117561,0.0001259242],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01241666,0.0001301671,0.9864334,0.0000469514,0.0002254367,0.0005679675,0.00001436419,0.00008806659,0.00007700062],"genre_scores_gemma":[0.1702114,0.000004409547,0.8293984,0.00001988531,0.0001451175,0.00009534901,0.000007476197,0.00003979225,0.00007817637],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6025731,"threshold_uncertainty_score":0.7567114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1139923744020839,"score_gpt":0.3645892111291466,"score_spread":0.2505968367270627,"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."}}