{"id":"W2103429022","doi":"10.1016/j.cam.2006.03.018","title":"Parameter range reduction for ODE models using cumulative backward differentiation formulas","year":2006,"lang":"en","type":"article","venue":"Journal of Computational and Applied Mathematics","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mathematics; Ode; Monotonic function; Consistency (knowledge bases); Range (aeronautics); Discretization; Reduction (mathematics); Applied mathematics; Nonlinear system; Stability (learning theory); Series (stratigraphy); Mathematical optimization; Mathematical analysis; Computer science; Discrete mathematics; Geometry","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":[0.0001015071,0.0001003815,0.0002123669,0.0000866236,0.00005065027,0.00003218916,0.00003232664,0.00004335849,0.000001202161],"category_scores_gemma":[0.000006545,0.0000889395,0.00005155208,0.00005549135,0.00001214086,0.0002246218,0.000004994642,0.00005512772,2.550178e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005721746,"about_ca_system_score_gemma":0.000009215775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.398387e-7,"about_ca_topic_score_gemma":2.138488e-7,"domain_scores_codex":[0.9992342,0.000004461508,0.0004564846,0.00005510193,0.0001634098,0.00008635009],"domain_scores_gemma":[0.9993837,0.000143713,0.0002624713,0.00003332579,0.0001516847,0.00002513912],"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":[0.00001843634,0.00002341959,0.000001257684,0.0001026273,0.00003465222,1.061361e-7,0.0001785842,0.9718001,0.001491534,0.02563505,0.00004167106,0.0006725814],"study_design_scores_gemma":[0.0005914454,0.00001157504,0.00001853017,0.00002975473,0.00003599547,0.00002246166,0.00003754907,0.6781489,0.0002591654,0.3207726,0.00001021828,0.00006168793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1355955,0.00007872975,0.8638825,0.00001443461,0.00008040617,0.0002162257,0.000006439215,0.00001967749,0.0001060518],"genre_scores_gemma":[0.6141042,0.000003925227,0.3857292,0.00000322898,0.0001247718,0.000005455734,0.000008489809,0.00001385118,0.000006827057],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4785087,"threshold_uncertainty_score":0.3626848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02183473590055144,"score_gpt":0.2374609931239203,"score_spread":0.2156262572233688,"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."}}