{"id":"W4399120337","doi":"10.1109/spi60975.2024.10539230","title":"Uncertainty Quantification of the Insertion Loss of an Automotive PCB Stripline","year":2024,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Compatibility and Noise Suppression","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Robert Bosch","keywords":"Stripline; Printed circuit board; Dielectric; Materials science; Polynomial chaos; Method of moments (probability theory); Monte Carlo method; Conductor; Solver; Computational physics; Mathematics; Engineering; Electrical engineering; Physics; Composite material; Optoelectronics; Statistics; 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":[0.0001233718,0.00005273025,0.0000753782,0.00003537078,0.0000147417,0.000006030678,0.00009057835,0.00003678037,0.0001067535],"category_scores_gemma":[0.00002236226,0.000034733,0.00003720946,0.0002160792,0.00003311748,0.00007037407,0.00001320614,0.00007534749,0.000002249008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002111461,"about_ca_system_score_gemma":0.0000188204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000774231,"about_ca_topic_score_gemma":0.0001097161,"domain_scores_codex":[0.999539,0.00003149063,0.0001796385,0.00008848241,0.00009976477,0.00006158788],"domain_scores_gemma":[0.999656,0.00004892749,0.00001550314,0.0002156552,0.00004908113,0.00001481112],"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":[0.00002340052,0.00008640478,0.0003644018,0.0003773417,0.00002539277,3.587054e-7,0.0007108668,0.09571353,0.8781821,0.003560258,0.0002839359,0.02067206],"study_design_scores_gemma":[0.00005282023,0.00006451074,0.01839603,0.00006533633,0.000009898185,6.83679e-7,0.00003852361,0.5210994,0.4591233,0.001034003,0.00007988123,0.00003565172],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969216,0.0001752311,0.001799751,0.00009615853,0.0001628226,0.0001107101,0.00001025415,0.0001123735,0.0006111144],"genre_scores_gemma":[0.9996865,0.00001257124,0.000172397,0.000002734113,0.00001430958,0.000002880552,0.00001174609,0.000006134548,0.00009068404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4253859,"threshold_uncertainty_score":0.1416371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01041201590883705,"score_gpt":0.2405298842625892,"score_spread":0.2301178683537522,"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."}}