{"id":"W2089273041","doi":"10.1002/jnm.659","title":"Self‐adjoint sensitivity analysis of lossy dielectric structures with electromagnetic time‐domain simulators","year":2007,"lang":"en","type":"article","venue":"International Journal of Numerical Modelling Electronic Networks Devices and Fields","topic":"Electromagnetic Simulation and Numerical Methods","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Lossy compression; Finite-difference time-domain method; Solver; Computation; Time domain; Sensitivity (control systems); Computer science; Grid; Dielectric; Computational science; Transmission line; Microwave; Electromagnetic field; Software; Electronic engineering; Algorithm; Mathematics; Physics; Electrical engineering; Engineering; Geometry; Optics; Telecommunications","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":[0.0006628531,0.0002228266,0.0005427928,0.0004565178,0.00003951437,0.00004067042,0.0001889419,0.0001589006,0.00004174955],"category_scores_gemma":[0.00001710688,0.0001812221,0.0001953123,0.0008948303,0.00004107606,0.0001108022,0.00001977062,0.0006094903,3.288529e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001145549,"about_ca_system_score_gemma":0.0000420091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001713312,"about_ca_topic_score_gemma":0.00001882038,"domain_scores_codex":[0.9981727,0.00008868955,0.0006518594,0.0001841089,0.0004490663,0.0004535709],"domain_scores_gemma":[0.9984736,0.000706414,0.0002816484,0.0001166031,0.0002783696,0.0001433006],"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":[0.0002024566,0.00005096774,0.0008110038,0.00001060717,0.001731189,0.00002419684,0.00009797786,0.9866443,0.0001343071,0.001036066,0.000007687165,0.009249285],"study_design_scores_gemma":[0.000415061,0.0008532242,0.002205279,0.00001927994,0.0004478106,0.00008565134,0.00001036468,0.994035,0.0001288944,0.001256084,0.0003473691,0.000195983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2536153,0.001803608,0.7441645,0.00006805387,0.00008423583,0.00005662386,7.952361e-7,0.00003376891,0.0001730703],"genre_scores_gemma":[0.980235,0.0004897063,0.01893254,0.0001168077,0.0001887014,6.871573e-7,0.000004252059,0.00002419456,0.000008149702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7266197,"threshold_uncertainty_score":0.7390025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005136938957597017,"score_gpt":0.2369937582767065,"score_spread":0.2318568193191095,"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."}}