{"id":"W2171608061","doi":"10.1109/22.920145","title":"Time-domain wavelet Galerkin modeling of two-dimensional electrically large dielectric waveguides","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Discretization; Galerkin method; Finite-difference time-domain method; Time domain; Wavelet; Dielectric; Mathematical analysis; Finite difference method; Dispersion (optics); Scaling; Optics; Mathematics; Materials science; Physics; Finite element method; Computer science; Geometry; Optoelectronics","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":[0.0007065002,0.0002264598,0.0003011284,0.0002949843,0.0001136716,0.00001277406,0.0000851447,0.000128581,0.0002404476],"category_scores_gemma":[0.00001162725,0.0001972025,0.000109416,0.0004513146,0.00006728843,0.0000726248,0.000001604868,0.0003199981,0.000008382335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004339778,"about_ca_system_score_gemma":0.00001571992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003068166,"about_ca_topic_score_gemma":0.000001726954,"domain_scores_codex":[0.9987038,0.0002372435,0.0003573983,0.0002263133,0.0001402117,0.0003349944],"domain_scores_gemma":[0.9992213,0.0004047714,0.00003712186,0.0001878231,0.00006252764,0.00008648922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002383894,0.0001235515,7.272677e-7,0.00001737509,0.00005672307,0.000006499785,0.00006808613,0.008775651,0.9165412,0.001679607,0.00003623405,0.07245601],"study_design_scores_gemma":[0.0003672543,0.0004209814,0.000001476218,0.00003856513,0.00004088485,0.00007233453,0.00000673579,0.1324599,0.8354878,0.0306739,0.0001786036,0.0002515105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2689163,0.0002174658,0.7278132,0.00002428247,0.00003070756,0.0001715322,0.000006943036,0.0003717797,0.002447779],"genre_scores_gemma":[0.958939,0.0002250352,0.04028368,0.0001256778,0.00002625935,0.00002573254,0.00000287505,0.00004350833,0.0003282913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6900226,"threshold_uncertainty_score":0.8041683,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009143315462087498,"score_gpt":0.2530966319837888,"score_spread":0.2439533165217013,"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."}}