{"id":"W2967597372","doi":"10.1109/tpwrd.2019.2934099","title":"Time-Domain Modeling of Transmission Line Crossing Using Electromagnetic Scattering Theory","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Lightning and Electromagnetic Phenomena","field":"Physics and Astronomy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Transmission line; Electric power transmission; Scattering; Computational electromagnetics; Electromagnetic theory; Physics; Scattering parameters; Time domain; Electromagnetic radiation; Line (geometry); Transmission (telecommunications); Electronic engineering; Transmission-line matrix method; Electrical engineering; Optics; Electromagnetic field; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001692044,0.0002374377,0.0002930822,0.0001757491,0.0002411168,0.00005674076,0.0001610431,0.00006031019,0.001613036],"category_scores_gemma":[2.48731e-7,0.0002324504,0.0001925839,0.0002188543,0.00007878838,0.0001782416,0.000001829558,0.0002773705,0.00008212784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004925099,"about_ca_system_score_gemma":0.0001018705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005766754,"about_ca_topic_score_gemma":2.052653e-7,"domain_scores_codex":[0.9986119,0.00008315884,0.0003557593,0.000334053,0.000211802,0.000403296],"domain_scores_gemma":[0.9993953,0.00006943313,0.00008346038,0.0003003399,0.00006030334,0.00009113884],"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":[0.0001704635,0.0002121771,0.00001137272,0.00002168901,0.00008797496,8.01757e-7,0.000946852,0.1614223,0.8309043,0.00009856388,0.000004037643,0.006119461],"study_design_scores_gemma":[0.001963199,0.001220242,0.00001661924,0.0003929097,0.0001862196,0.00001111633,0.0004672025,0.7022847,0.2882805,0.004340132,0.0001203027,0.0007169246],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.595406,0.00006109392,0.4024192,0.00001563603,0.0001092618,0.0001182942,0.000009654119,0.00003317262,0.001827744],"genre_scores_gemma":[0.9959646,0.000003262058,0.003010671,0.00003603709,0.00003765135,0.000006229636,0.000003704185,0.00004577347,0.0008920163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5426239,"threshold_uncertainty_score":0.9992996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007590757691226331,"score_gpt":0.2146543394593807,"score_spread":0.2070635817681544,"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."}}