{"id":"W2128914839","doi":"10.1109/wcica.2006.1712466","title":"Global Regularization of Nonlinear Differential-Algebraic Equation Systems","year":2006,"lang":"en","type":"article","venue":"","topic":"Stability and Control of Uncertain Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Regularization (linguistics); Nonlinear system; Algebraic number; Backus–Gilbert method; Mathematics; Applied mathematics; Algebraic equation; Differential equation; Regularization perspectives on support vector machines; Differential algebraic equation; Computer science; Mathematical optimization; Mathematical analysis; Inverse problem; Tikhonov regularization; Ordinary differential equation; Artificial intelligence; Physics","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":[0.00007960628,0.00008806256,0.000168205,0.00003086883,0.0000183898,0.00002563834,0.00006923473,0.00007254595,0.00002761356],"category_scores_gemma":[0.00001075636,0.0000805143,0.00004716901,0.0001611753,0.00001568523,0.00007904067,0.000006371775,0.00002230432,0.000009145159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007009896,"about_ca_system_score_gemma":0.000009787114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004524328,"about_ca_topic_score_gemma":0.00008195302,"domain_scores_codex":[0.9992669,0.00002622261,0.000320608,0.00009436017,0.0001768369,0.0001150254],"domain_scores_gemma":[0.9996855,0.00002554873,0.00004195671,0.0001635022,0.00006306649,0.00002039057],"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":[0.00003299197,0.000120893,0.02119297,0.0006434754,0.00009927845,0.00000123395,0.00006918525,0.6831082,0.01601489,0.2751594,0.0009962498,0.002561225],"study_design_scores_gemma":[0.0004789476,0.00002418801,0.01282974,0.00003321507,0.00002129133,0.000001819685,0.00005587625,0.9834502,0.0009075292,0.001625389,0.0004407482,0.0001311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3432426,0.0003676836,0.638413,0.00001978428,0.0006256043,0.0002753498,0.00002278838,0.0002735801,0.01675958],"genre_scores_gemma":[0.9992778,0.000001925793,0.0002156089,0.000002131339,0.0001762785,0.00001102889,0.00004888659,0.000008516672,0.0002577708],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6560352,"threshold_uncertainty_score":0.3283278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00751879786637558,"score_gpt":0.1890153242455415,"score_spread":0.181496526379166,"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."}}