{"id":"W2146114563","doi":"10.1109/lmwc.2003.819375","title":"Numerical through-resistor (TR) calibration technique for modeling of microwave integrated circuits","year":2004,"lang":"en","type":"article","venue":"IEEE Microwave and Wireless Components Letters","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Microstrip; Resistor; Electronic engineering; Calibration; Classification of discontinuities; Method of moments (probability theory); Electronic circuit; Microwave; Scattering parameters; Planar; Computer science; Computational electromagnetics; Electromagnetic field; Engineering; Electrical engineering; Physics; Telecommunications; Mathematics; Voltage","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":[0.0001786304,0.0003485768,0.0004612908,0.0001864046,0.00009747386,0.00004134579,0.0002045897,0.000184424,0.000002489513],"category_scores_gemma":[0.000008906416,0.0003582405,0.0001426106,0.0002351619,0.00006827825,0.0002164444,0.00001660754,0.0002321588,0.000001805312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001720358,"about_ca_system_score_gemma":0.00003211127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001743026,"about_ca_topic_score_gemma":0.0000110186,"domain_scores_codex":[0.9984382,0.0000379631,0.0005600474,0.0003581035,0.0001875904,0.0004180529],"domain_scores_gemma":[0.9994417,0.00003919692,0.00009968068,0.0002466264,0.00009006654,0.00008266955],"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":[0.00003393094,0.00006595067,0.0000287949,0.0002384848,0.00008232929,0.000005068331,0.0002674386,0.004969629,0.9919858,0.000165052,0.001226773,0.0009307755],"study_design_scores_gemma":[0.0007647117,0.00006335084,0.0000115143,0.0002316034,0.00004086156,0.00001957547,0.00001563381,0.02325346,0.9744263,0.0006068579,0.0001886687,0.0003774051],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4414877,0.0002071317,0.5573351,0.00009668387,0.0001074955,0.0005073771,0.00001575749,0.0001588091,0.00008388735],"genre_scores_gemma":[0.9864308,0.0001381862,0.01261142,0.0004398134,0.00007817512,0.0001392369,0.0000837566,0.00007406009,0.000004579794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5449431,"threshold_uncertainty_score":0.9998869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02904805777705054,"score_gpt":0.2242601172135053,"score_spread":0.1952120594364548,"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."}}