{"id":"W2021372279","doi":"10.2200/s00052ed1v01y200609cem011","title":"Adaptive Mesh Refinement for Time-Domain Numerical Electromagnetics","year":2006,"lang":"en","type":"article","venue":"Synthesis lectures on computational electromagnetics","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Adaptive mesh refinement; Electromagnetics; Computer science; Computational electromagnetics; Computational science; Domain (mathematical analysis); Mathematics; Physics; Electromagnetic field; Mathematical analysis; Engineering physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002879236,0.0005635257,0.0005573674,0.0002972991,0.0002367356,0.00009270301,0.0003234746,0.0002247012,0.0004764785],"category_scores_gemma":[0.0002065689,0.0005720824,0.000260012,0.000606829,0.00009109503,0.00005010154,0.0000217533,0.0003555593,0.00008772919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002682001,"about_ca_system_score_gemma":0.00009279152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008264394,"about_ca_topic_score_gemma":0.000004082464,"domain_scores_codex":[0.9969693,0.000217522,0.0006763298,0.0005625889,0.00065111,0.0009231122],"domain_scores_gemma":[0.9963313,0.00287716,0.0001142271,0.0003069252,0.0002037251,0.000166707],"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":[0.0005237423,0.0004876151,0.00001765331,0.00006727064,0.000215964,0.0000106085,0.00005384022,0.7842999,0.08335212,0.03721051,0.04035591,0.05340483],"study_design_scores_gemma":[0.001130079,0.004646785,0.001674364,0.00003078259,0.0001296042,0.00003578944,0.000005802367,0.8243259,0.03117671,0.1233937,0.01244527,0.001005142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07250992,0.001677111,0.8932809,0.001797234,0.0003746114,0.001754225,0.0001178154,0.001497924,0.0269903],"genre_scores_gemma":[0.7129669,0.00001966483,0.2849728,0.0005182853,0.0004397411,0.0003259738,0.000121273,0.0001616947,0.0004736625],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.640457,"threshold_uncertainty_score":0.9996731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007202279336338296,"score_gpt":0.231638827495118,"score_spread":0.2244365481587797,"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."}}