{"id":"W2415628841","doi":"10.1007/978-3-319-12307-3_60","title":"Parameter Range Reduction in ODE Models in the Presence of Partial Data Sets","year":2015,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Ode; Reduction (mathematics); Range (aeronautics); Series (stratigraphy); A priori and a posteriori; Estimation theory; Mathematics; Scheme (mathematics); Algorithm; Applied mathematics; Mathematical optimization; Computer science; Engineering; Mathematical analysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001086954,0.0003551876,0.0006348778,0.0003016681,0.00001446287,0.00004957484,0.0007040395,0.0002626966,0.000007122152],"category_scores_gemma":[0.0004553843,0.0003314494,0.00002526847,0.0001123135,0.00007819366,0.0003992011,0.0001481823,0.0005879418,0.000005635657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002055276,"about_ca_system_score_gemma":0.00004216898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002388006,"about_ca_topic_score_gemma":0.0000641073,"domain_scores_codex":[0.9976586,0.00001132857,0.001099881,0.0003661996,0.0005567326,0.0003073334],"domain_scores_gemma":[0.9986691,0.0002308548,0.0003681475,0.0005226916,0.0001684098,0.00004077406],"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":[0.00005914723,0.0001467297,0.00008904149,0.005857356,0.00008941351,0.00004077876,0.01810016,0.4970898,0.0001193088,0.4718651,0.00210446,0.004438723],"study_design_scores_gemma":[0.0003620742,0.0000161158,0.000008743275,0.0008993252,0.00003538975,0.000008772694,0.0001963521,0.7182756,0.000008110295,0.2795968,0.0003381158,0.0002545713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002057111,0.002354416,0.8999599,0.000058584,0.0008508556,0.004909924,0.001363322,0.0002298299,0.08821601],"genre_scores_gemma":[0.2699583,0.001518375,0.7236341,0.00001179348,0.0003108524,0.000345874,0.0002946715,0.0004560516,0.003470066],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2679012,"threshold_uncertainty_score":0.9999138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06797564470099278,"score_gpt":0.2824419336032425,"score_spread":0.2144662889022498,"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."}}