{"id":"W2084023235","doi":"10.1115/1.4005931","title":"Galerkin Approximations for Higher Order Delay Differential Equations","year":2012,"lang":"en","type":"article","venue":"Journal of Computational and Nonlinear Dynamics","topic":"Numerical methods for differential equations","field":"Mathematics","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Indian Institute of Technology Kharagpur","keywords":"Galerkin method; Mathematics; Nonlinear system; Delay differential equation; Mathematical analysis; Lagrange multiplier; Scalar (mathematics); Partial differential equation; Boundary value problem; Applied mathematics; Differential equation; Mathematical optimization; Physics; 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":[0.0003716504,0.0001351566,0.0002751589,0.0001583414,0.0001608191,0.00004331039,0.0001041554,0.0000745529,0.00008190551],"category_scores_gemma":[0.0006322032,0.0001104119,0.0001423738,0.0001941488,0.00006691733,0.0002527561,0.0000329597,0.0001718075,0.00000275678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006109434,"about_ca_system_score_gemma":0.00007085397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001594069,"about_ca_topic_score_gemma":0.00000341734,"domain_scores_codex":[0.9987135,0.00009007295,0.0005985482,0.0000849744,0.0003145063,0.0001983932],"domain_scores_gemma":[0.996753,0.001978925,0.0004406305,0.00007569058,0.0005975715,0.0001541857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001403648,0.001614971,0.0008366429,0.0001802378,0.0004291093,0.000001074013,0.000512097,0.005244861,0.0001547993,0.9551389,0.0004639236,0.035283],"study_design_scores_gemma":[0.0007714894,0.0001000403,0.001732397,0.00002378962,0.0001688248,0.00003140475,0.00004032628,0.4264653,0.00001134284,0.5694317,0.00108429,0.0001390766],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06290293,0.00006028136,0.9353432,0.0007932663,0.0005426374,0.0001845151,0.00008617916,0.00001618854,0.00007080378],"genre_scores_gemma":[0.2030489,0.000004714499,0.7959285,0.00005949115,0.0005723562,0.000009515127,0.00007149135,0.0000231415,0.0002818666],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4212204,"threshold_uncertainty_score":0.4502468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.064128021417896,"score_gpt":0.3629935400161325,"score_spread":0.2988655185982365,"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."}}