{"id":"W4236290647","doi":"10.1002/mmce.20276","title":"Automated time domain modeling of linear and nonlinear microwave circuits using recurrent neural networks","year":2008,"lang":"en","type":"article","venue":"International Journal of RF and Microwave Computer-Aided Engineering","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Time domain; Computer science; Waveform; Transient (computer programming); Recurrent neural network; Frequency domain; Nonlinear system; Envelope (radar); Artificial neural network; Electronic engineering; Amplifier; Microwave; Computational electromagnetics; Algorithm; Control theory (sociology); Artificial intelligence; Engineering; Telecommunications; Physics; Bandwidth (computing)","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":[0.0002353594,0.0003243285,0.0004891832,0.000433246,0.00005198557,0.00004601208,0.0002565359,0.0001311613,0.000002904065],"category_scores_gemma":[0.0000243895,0.0003394409,0.0001432054,0.0001453204,0.00005096169,0.0002362906,0.0001065002,0.0003879312,8.478348e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009363829,"about_ca_system_score_gemma":0.00002711383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005533675,"about_ca_topic_score_gemma":4.165618e-7,"domain_scores_codex":[0.9984129,0.00001821577,0.0008285368,0.0001942193,0.000242772,0.0003033331],"domain_scores_gemma":[0.9991577,0.00008315672,0.0001615005,0.000121676,0.0003074639,0.0001684914],"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":[0.00001161854,0.00002068901,0.00005210419,0.00006189947,0.0002337273,0.0001083945,0.0002831766,0.8141251,0.1817609,0.0000166979,0.00007636455,0.003249334],"study_design_scores_gemma":[0.0007688578,0.00007562822,0.0001054014,0.0004165882,0.0000319767,0.003313916,0.00001208217,0.9883767,0.006455157,0.00001228953,0.0001381961,0.0002932022],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5419967,0.00166943,0.4553397,0.00001155795,0.0008098826,0.00004953262,0.000008455054,0.0001012762,0.00001337506],"genre_scores_gemma":[0.913001,0.0006325346,0.08558029,0.00001593112,0.0006934698,6.237111e-7,0.00001109529,0.00006113818,0.000003907767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3710043,"threshold_uncertainty_score":0.9999058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01397523714680029,"score_gpt":0.2201689766460807,"score_spread":0.2061937394992804,"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."}}