{"id":"W2037392129","doi":"10.1198/jcgs.2011.10021","title":"Penalized Nonlinear Least Squares Estimation of Time-Varying Parameters in Ordinary Differential Equations","year":2011,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Model Reduction and Neural Networks","field":"Physics and Astronomy","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health; National Institute of Allergy and Infectious Diseases; King Abdullah University of Science and Technology","keywords":"Ode; Ordinary differential equation; Applied mathematics; Nonlinear system; Mathematics; Non-linear least squares; Ordinary least squares; Least-squares function approximation; Function (biology); Mathematical optimization; Polynomial; Estimation theory; Algorithm; Differential equation; Statistics; Mathematical analysis","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":[0.00008210022,0.00007607848,0.0001865161,0.0001266698,0.00004452401,0.00001396619,0.00004959352,0.00002360903,0.000229384],"category_scores_gemma":[0.00001485191,0.000063618,0.00005728244,0.0001246921,0.0000813865,0.00009259192,0.00001126234,0.0001521332,0.000001046877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004643504,"about_ca_system_score_gemma":0.00003144456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003153304,"about_ca_topic_score_gemma":5.64202e-7,"domain_scores_codex":[0.9991742,0.00005729432,0.0004353995,0.00006806576,0.0001855835,0.00007940167],"domain_scores_gemma":[0.999241,0.0002643157,0.000266593,0.00002778936,0.0001335855,0.00006670761],"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":[0.0006867458,0.0009903751,0.006665721,0.00005124193,0.0002163107,0.00001559644,0.0007040714,0.8045516,0.0001189234,0.081094,0.0003537403,0.1045517],"study_design_scores_gemma":[0.000711705,0.0001480542,0.009877947,0.00004157049,0.00003497249,0.000007565417,0.00002302454,0.8348039,0.00004035621,0.1542345,0.000005783162,0.00007065909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3288831,0.00001515718,0.6708991,0.0000307776,0.00004905356,0.00003581251,0.0000400545,0.0000015048,0.00004540451],"genre_scores_gemma":[0.9046039,0.000005197202,0.09528717,0.0000110488,0.00003897704,9.661253e-7,0.00003629953,0.000004382226,0.00001208496],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5757207,"threshold_uncertainty_score":0.2594267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02496376213044871,"score_gpt":0.2653934609609376,"score_spread":0.2404296988304889,"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."}}