{"id":"W2150129101","doi":"10.1109/mwsym.2000.860959","title":"Application of wavelet-Galerkin method to electrically-large optical waveguide problems","year":2002,"lang":"en","type":"article","venue":"","topic":"Seismic Waves and Analysis","field":"Earth and Planetary Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Wavelet; Galerkin method; Scaling; Waveguide; Simple (philosophy); Dispersion (optics); Sampling (signal processing); Time domain; Computer science; Mathematics; Optics; Physics; Finite element method; Geometry; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003981881,0.0001058452,0.000215593,0.0001185296,0.00005689817,0.00002039646,0.0001985899,0.0000601232,0.004775511],"category_scores_gemma":[0.00004000127,0.00007878814,0.00009217588,0.0006140203,0.00001589845,0.00006195882,0.00001060604,0.00008002402,0.0006514283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003688956,"about_ca_system_score_gemma":0.000007046915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001137698,"about_ca_topic_score_gemma":0.0001852431,"domain_scores_codex":[0.9987748,0.00004825399,0.0003166016,0.000280173,0.0002664637,0.0003136726],"domain_scores_gemma":[0.9993778,0.0001265683,0.00005719047,0.0002156636,0.00005074882,0.0001720191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000209374,0.0002152878,0.0364866,0.00004413406,0.000119771,0.000005637394,0.000201426,0.007986259,0.007167269,0.008514022,0.007703993,0.9315346],"study_design_scores_gemma":[0.0001625717,0.0001431252,0.02830707,0.000004652681,0.00003311348,0.000006296499,0.00003648346,0.9183394,0.001939337,0.0009982203,0.04985375,0.0001759436],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02359573,0.0002153826,0.8848318,0.002249992,0.00003261878,0.0003100822,0.00003419396,0.00005739845,0.08867279],"genre_scores_gemma":[0.8936748,0.00003727603,0.102954,0.001044107,0.00006686548,0.000002352383,0.00004009739,0.000003542889,0.002176975],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9313587,"threshold_uncertainty_score":0.9961343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01420448307158104,"score_gpt":0.2319512903011396,"score_spread":0.2177468072295585,"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."}}