{"id":"W1995127795","doi":"10.1049/ip-map:20040703","title":"Electromagnetic optimisation of microwave filters using an efficient model parameter extraction technique","year":2004,"lang":"en","type":"article","venue":"IEE Proceedings - Microwaves Antennas and Propagation","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Space mapping; Computer science; Microwave; Filter (signal processing); Reflection (computer programming); Calibration; Algorithm; Mathematical optimization; Mathematics","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":[0.0002367656,0.0002703514,0.0002354561,0.0002429167,0.00008851276,0.00008891318,0.00009297179,0.0001596706,0.000002610538],"category_scores_gemma":[0.00002472614,0.0002759326,0.00006070455,0.0002221804,0.00007277539,0.0003416913,0.0000180363,0.0001966012,0.000001032591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001239637,"about_ca_system_score_gemma":0.0000290212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001987071,"about_ca_topic_score_gemma":0.000001570235,"domain_scores_codex":[0.998813,0.000006281938,0.0004103904,0.0003149961,0.0001428571,0.0003124441],"domain_scores_gemma":[0.999507,0.00001172324,0.000120219,0.0001073684,0.0001653939,0.00008827989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001808159,0.0000413904,0.0000115621,0.0002459657,0.00001589409,7.17666e-7,0.0005942389,0.1582698,0.8396854,0.0002352921,0.000007521011,0.0008741487],"study_design_scores_gemma":[0.0002064261,0.0001109473,0.00006946433,0.0001352004,0.00002771767,0.00008579767,0.00008513967,0.4618379,0.5366376,0.0006056895,0.000005295632,0.000192765],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6587055,0.0002160928,0.3404298,0.00001587211,0.00005461986,0.0003358132,0.000005180567,0.0001394964,0.00009756471],"genre_scores_gemma":[0.8952265,0.0001225524,0.1044787,0.00001254633,0.00005093208,0.00002414584,0.0000154718,0.00005499909,0.00001410993],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3035682,"threshold_uncertainty_score":0.9999693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01499136164371913,"score_gpt":0.2284644261751804,"score_spread":0.2134730645314613,"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."}}