{"id":"W3197550566","doi":"10.1007/978-3-030-63591-6_25","title":"Using Shooting Approaches to Generate Initial Guesses for ODE Parameter Estimation","year":2021,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Ode; Parameterized complexity; Ordinary differential equation; Set (abstract data type); Shooting method; Computer science; Mathematical optimization; Estimation; Estimation theory; Mathematics; Differential (mechanical device); Applied mathematics; Algorithm; Differential equation; Engineering; Boundary value problem; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001393571,0.001311389,0.0009086984,0.001046133,0.0007808082,0.001289793,0.001262377,0.00188148,0.009270938],"category_scores_gemma":[0.007376376,0.001218176,0.000819695,0.0008261604,0.0009832499,0.00144133,0.002026862,0.00274453,0.002497789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006930263,"about_ca_system_score_gemma":0.0008324434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00358091,"about_ca_topic_score_gemma":0.005716996,"domain_scores_codex":[0.9996783,0.000100069,0.00002214086,0.00005303765,0.0001227984,0.00002373846],"domain_scores_gemma":[0.9970975,0.001996078,0.0001434312,0.0002888123,0.0003789518,0.00009525417],"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":[0.0001591321,0.0001068777,0.0006369893,0.0002533809,0.00007183234,0.000184973,0.0003673303,0.7904997,0.01380409,0.0358391,0.003929641,0.154147],"study_design_scores_gemma":[0.00001126199,0.00001493387,0.00003855824,0.00001644151,0.000005089899,0.00002059798,0.00001594121,0.9890323,0.002649809,0.00679689,0.001388143,0.00001010729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002175706,0.00003145616,0.9957301,0.00002079304,0.00002030066,0.00002939282,0.00003215357,0.0005013773,0.001458691],"genre_scores_gemma":[0.06266224,0.00009227167,0.9330952,0.00004748389,0.00001325214,0.0001488018,0.0002025356,0.0006330989,0.003105015],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009270938,"threshold_uncertainty_score":0.03101438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2098970060607345,"score_gpt":0.3354747858039829,"score_spread":0.1255777797432484,"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."}}