{"id":"W2897704647","doi":"10.48550/arxiv.1810.04030","title":"A Complete Surface Integral Method for Broadband Modeling of 3D Interconnects in Stratified Media","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Electromagnetic Scattering and Analysis","field":"Physics and Astronomy","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Integral equation; Electric-field integral equation; Solver; Method of moments (probability theory); Computation; Electrical conductor; Computer science; Mathematical analysis; Range (aeronautics); Bessel function; Admittance; Finite element method; Electronic engineering; Mathematics; Mathematical optimization; Algorithm; Physics; Electrical engineering; Engineering; Electrical impedance","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002924213,0.0002632129,0.0005549323,0.0001823135,0.00004651104,0.00003013144,0.0004426738,0.0001195887,0.0001280282],"category_scores_gemma":[0.00001414647,0.000292099,0.0002688316,0.000252508,0.00006661758,0.00005871322,0.0002389898,0.0003359085,0.00000410224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004981294,"about_ca_system_score_gemma":0.00009065874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00292537,"about_ca_topic_score_gemma":0.0003597793,"domain_scores_codex":[0.9986143,0.0001194477,0.0002982451,0.0006228748,0.0000509373,0.0002942715],"domain_scores_gemma":[0.9989869,0.0002199649,0.0002152187,0.000362607,0.0001419961,0.00007329605],"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":[0.0002009703,0.000201596,0.006975937,0.000182087,0.0005560397,0.000007353925,0.001242161,0.9661203,0.006556772,0.01646385,0.00007996616,0.001412951],"study_design_scores_gemma":[0.0006785631,0.00007384193,0.00006649982,0.0001936871,0.0001994814,1.738492e-7,0.0005752764,0.9543811,0.0008515006,0.04269629,0.000007472433,0.0002760835],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5713758,0.00001278728,0.4277547,0.00001429756,0.00005857435,0.0001306461,0.00008936163,0.00001251166,0.0005513021],"genre_scores_gemma":[0.9879422,0.00000528668,0.01161474,0.000007089503,0.00009747652,0.000001469669,0.0001216316,0.00002074535,0.0001893444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4165664,"threshold_uncertainty_score":0.9999531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07494189249973175,"score_gpt":0.2280222683551231,"score_spread":0.1530803758553913,"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."}}