{"id":"W2774438824","doi":"10.1109/tmtt.2017.2771444","title":"New Higher Order Method of Moments for Accurate Inductance Extraction in Transmission Lines of Complex Cross Sections","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Microwave Theory and Techniques","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Manitoba; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inductance; Method of moments (probability theory); Discretization; Impedance parameters; Mathematical analysis; Electric power transmission; Basis function; Transmission line; Mathematics; Finite element method; Conductor; Electrical impedance; Geometry; Physics; Voltage; Engineering; Electrical engineering","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":[0.0006393243,0.0007642199,0.0006741696,0.0009500368,0.0004082145,0.0008178069,0.001013518,0.0007124297,0.002264672],"category_scores_gemma":[0.001596679,0.0003429296,0.000849854,0.0007712367,0.0005121952,0.001042241,0.0005524326,0.0009820126,0.0009783626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005857174,"about_ca_system_score_gemma":0.0008900987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00116103,"about_ca_topic_score_gemma":0.001921949,"domain_scores_codex":[0.9995946,0.0001151907,0.00001761321,0.00003600232,0.0002020011,0.00003457103],"domain_scores_gemma":[0.9994116,0.0002794719,0.00006916458,0.00009176713,0.0001191271,0.00002884603],"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":[0.0001672442,0.0001157844,0.001202786,0.0006782558,0.00009841031,0.0006006641,0.0003867641,0.4203348,0.08236566,0.2282379,0.008070095,0.2577417],"study_design_scores_gemma":[0.0000136586,0.0000339237,0.0002224895,0.00002268767,0.000008400452,0.0001630838,0.00002144263,0.9729436,0.005841189,0.01262213,0.008083208,0.00002418663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003266028,0.0002566445,0.9944884,0.0000466049,0.00003910467,0.00002264276,0.00004035091,0.0002919848,0.001548286],"genre_scores_gemma":[0.1474968,0.0006893293,0.8457399,0.0000883177,0.00008949863,0.0002049983,0.0002059296,0.0004522002,0.005032947],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002264672,"threshold_uncertainty_score":0.007576108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02753126863600406,"score_gpt":0.3612230849084367,"score_spread":0.3336918162724327,"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."}}