{"id":"W3043686091","doi":"10.1109/lawp.2020.3022593","title":"SLIM: A Well-Conditioned Single-Source Boundary Element Method for Modeling Lossy Conductors in Layered Media","year":2020,"lang":"en","type":"article","venue":"IEEE Antennas and Wireless Propagation Letters","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; CMC Microsystems; Advanced Micro Devices","keywords":"Lossy compression; Electrical conductor; Materials science; Boundary (topology); Boundary element method; Boundary value problem; Electronic engineering; Acoustics; Finite element method; Computer science; Physics; Mathematical analysis; Composite material; Engineering; Structural engineering; Mathematics","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":[0.0003492468,0.0005832793,0.0005060628,0.0004103526,0.0003283679,0.0005961129,0.001100998,0.001110884,0.003366057],"category_scores_gemma":[0.0008337323,0.0003238543,0.0005123543,0.0003754434,0.0003580693,0.0007806002,0.0007870042,0.000870212,0.001459778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002770305,"about_ca_system_score_gemma":0.000772112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001201866,"about_ca_topic_score_gemma":0.001451455,"domain_scores_codex":[0.9998405,0.00004262439,0.000006716839,0.00001049161,0.00008746181,0.00001216793],"domain_scores_gemma":[0.9998106,0.00007978256,0.00002198834,0.00001859867,0.00005314391,0.00001587344],"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":[0.00010419,0.00008561186,0.0007352585,0.0004521105,0.00004851373,0.0002941734,0.0003281918,0.7499166,0.06468184,0.06284311,0.008338697,0.1121716],"study_design_scores_gemma":[0.000007559282,0.00001476377,0.00002661364,0.00001504134,0.000002864559,0.00003733263,0.00001445791,0.9900438,0.003058851,0.002171005,0.004601878,0.000005911366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003503704,0.0001122092,0.9932014,0.00008631164,0.00003724327,0.00002818448,0.00007199348,0.0005045437,0.002454413],"genre_scores_gemma":[0.1249819,0.0004043416,0.8670596,0.0001590823,0.00003827965,0.0002951973,0.0003964325,0.0004819375,0.006183151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003366057,"threshold_uncertainty_score":0.01126057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03043604769730754,"score_gpt":0.2617038151216655,"score_spread":0.231267767424358,"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."}}