{"id":"W3115417977","doi":"10.1109/pesgm41954.2020.9281642","title":"Time-Domain Modeling of Transmission Line Crossing Using Electromagnetic Scattering Theory","year":2020,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Transmission line; Finite-difference time-domain method; Electric power transmission; Time domain; Scattering; Solver; Computational electromagnetics; Electromagnetic field; Physics; Perfectly matched layer; Mathematical analysis; Perfect conductor; Conductor; Lossless compression; Optics; Computer science; Geometry; Mathematics; Algorithm; Electrical engineering; Telecommunications; Engineering; Mathematical optimization","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":[0.0001658438,0.0001549694,0.0002457497,0.00005855236,0.00006812396,0.00003030318,0.0001068751,0.00006711993,0.0005916351],"category_scores_gemma":[0.00002859793,0.0001480562,0.00007716603,0.0003210636,0.00005205796,0.00007519944,0.00001477056,0.0001472997,0.000008701807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002271987,"about_ca_system_score_gemma":0.00001905575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002891798,"about_ca_topic_score_gemma":5.299361e-8,"domain_scores_codex":[0.9990028,0.0000833857,0.0003451742,0.000168197,0.0001402127,0.000260202],"domain_scores_gemma":[0.9996074,0.00009831594,0.00002497995,0.0001079933,0.00002764672,0.0001336869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002049652,0.000005374932,0.000001673072,0.00004008333,0.00000791201,7.121139e-7,0.0002588918,0.3894439,0.5981757,0.00005403214,0.000002481183,0.0119887],"study_design_scores_gemma":[0.0002895946,0.0001556859,0.000006711021,0.00003095001,0.00001763991,0.000003972422,0.00002548668,0.920041,0.07813006,0.001068028,0.00007512278,0.0001557819],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.438708,0.0003370209,0.5591515,0.0000691984,0.00001557415,0.00006302582,3.872819e-7,0.0001748694,0.001480369],"genre_scores_gemma":[0.7816338,0.000009120974,0.2181047,0.0001251679,0.00005184553,0.000001197003,0.000001429723,0.00003846116,0.0000342613],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.530597,"threshold_uncertainty_score":0.647799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02378571766704196,"score_gpt":0.2705019283699557,"score_spread":0.2467162107029138,"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."}}