{"id":"W2593831893","doi":"10.1137/120892465","title":"Unconditionally Optimal Error Estimates of a Crank--Nicolson Galerkin Method for the Nonlinear Thermistor Equations","year":2014,"lang":"en","type":"article","venue":"SIAM Journal on Numerical Analysis","topic":"Numerical methods in inverse problems","field":"Mathematics","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"City University of Hong Kong; National Science Foundation","keywords":"Discretization; Mathematics; Galerkin method; Crank–Nicolson method; Nonlinear system; Finite element method; Applied mathematics; Error analysis; Discretization error; Discontinuous Galerkin method; Numerical analysis; Approximation error; Mathematical analysis; Temporal discretization","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003314564,0.0003051563,0.0009655789,0.0003379207,0.0004252528,0.0000797153,0.0006331375,0.0001243912,0.001049237],"category_scores_gemma":[0.009060147,0.0001894093,0.001297509,0.001333396,0.0001668602,0.0001090652,0.00006185543,0.0005321515,0.00001969185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001245429,"about_ca_system_score_gemma":0.00008723037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002102226,"about_ca_topic_score_gemma":0.000003519145,"domain_scores_codex":[0.9966657,0.0007221932,0.001064446,0.0003501163,0.0007953208,0.0004022614],"domain_scores_gemma":[0.9770653,0.02071127,0.0009999449,0.0005177917,0.0004865559,0.0002191414],"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":[0.00144419,0.004151273,0.001136409,0.0004273606,0.02495645,0.00001945775,0.001560277,0.485848,0.008122371,0.2563029,0.008928626,0.2071027],"study_design_scores_gemma":[0.0006055,0.0004519438,0.000121751,0.00003843972,0.003098368,0.00001461492,0.00009767477,0.8252745,0.0006270327,0.1645772,0.004855293,0.0002376968],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0006337723,0.00007100565,0.9953066,0.002950744,0.0001769365,0.0003152355,0.00005850264,0.00004222643,0.0004449816],"genre_scores_gemma":[0.04901167,0.00001101877,0.9497885,0.0005102626,0.0002925786,0.00009048227,0.00001119559,0.0000475348,0.0002367847],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3394265,"threshold_uncertainty_score":0.9998639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07911854369330538,"score_gpt":0.4069480340955735,"score_spread":0.3278294904022681,"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."}}