{"id":"W2782771813","doi":"10.1109/lawp.2018.2794206","title":"On Accuracy of Perfect Electric Conductor Implementation in Lebedev FDTD","year":2018,"lang":"en","type":"article","venue":"IEEE Antennas and Wireless Propagation Letters","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Finite-difference time-domain method; Perfect conductor; Conductor; Discretization; Planar; Grid; Focus (optics); Electrical conductor; Electric field; Computer science; Anisotropy; Optics; Physics; Mathematical analysis; Mathematics; Electrical engineering; Geometry; Engineering; Computer graphics (images); Scattering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002345158,0.0003858708,0.0004847287,0.0004942286,0.0004229437,0.001389039,0.001060418,0.0007657446,0.001613173],"category_scores_gemma":[0.009386558,0.0002947775,0.0003964725,0.0004556261,0.0007805196,0.001180276,0.0008682068,0.001235487,0.0004904203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009996049,"about_ca_system_score_gemma":0.0008826484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008103762,"about_ca_topic_score_gemma":0.004481676,"domain_scores_codex":[0.9985468,0.0004961169,0.00007611669,0.0000880989,0.0006815246,0.0001114564],"domain_scores_gemma":[0.9968584,0.001905593,0.0001159469,0.0004674041,0.0005949944,0.00005761773],"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":[0.000265095,0.00007579318,0.003265817,0.0003042371,0.00003849886,0.0002652632,0.0004520234,0.6829834,0.01305007,0.1792323,0.003737008,0.1163306],"study_design_scores_gemma":[0.000008928341,0.00002236772,0.0001987359,0.00004697913,0.000004635135,0.00004459001,0.00002810216,0.9831697,0.005352669,0.007849688,0.003261998,0.00001176807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02892218,0.001370617,0.952862,0.0006667869,0.000257191,0.00003205096,0.00008256467,0.0004468962,0.01535974],"genre_scores_gemma":[0.683948,0.001497524,0.3086674,0.0002124888,0.00008758318,0.00008325784,0.000173684,0.0002748707,0.005055322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008103762,"threshold_uncertainty_score":0.01611316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01231797521865713,"score_gpt":0.2861342771312568,"score_spread":0.2738163019125997,"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."}}