{"id":"W2997226759","doi":"10.1109/lmwc.2019.2952977","title":"Microwave Characterization of Liquid Samples Through the Systematic Parameter Extraction of the Circuit Equivalence for the Debye Model","year":2019,"lang":"en","type":"article","venue":"IEEE Microwave and Wireless Components Letters","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Debye; Permittivity; Equivalence (formal languages); Microwave; Equivalent circuit; Extraction (chemistry); Scattering parameters; Electronic engineering; Characterization (materials science); Materials science; Computational physics; Physics; Mathematics; Engineering; Condensed matter physics; Electrical engineering; Optoelectronics; Optics; Dielectric; Chemistry; Quantum mechanics; Voltage; Chromatography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004583791,0.0005210864,0.0004599699,0.0006360178,0.0002937625,0.0006343531,0.0006496545,0.0003910131,0.001200833],"category_scores_gemma":[0.001524115,0.0002223551,0.0002423253,0.00053081,0.000449831,0.001022841,0.000458314,0.0008537406,0.0006637334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003271006,"about_ca_system_score_gemma":0.0003035383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004199446,"about_ca_topic_score_gemma":0.0006677945,"domain_scores_codex":[0.9996922,0.00005191768,0.00001798791,0.0000656525,0.0001522776,0.00001999214],"domain_scores_gemma":[0.9996339,0.0001586909,0.00004646326,0.00007873386,0.00007679164,0.000005483998],"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":[0.00004527731,0.00003871034,0.0003977126,0.0001977575,0.00001304849,0.00005696182,0.00009629671,0.002077169,0.9569946,0.00257282,0.0001722074,0.03733749],"study_design_scores_gemma":[0.000006383897,0.000136204,0.0007554622,0.00001577366,0.00001327,0.00009740934,0.00003719586,0.02113717,0.9712561,0.001035577,0.005495083,0.00001431999],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1665225,0.001023754,0.826825,0.0001325023,0.00004749401,0.0002656504,0.0004424594,0.000889274,0.003851235],"genre_scores_gemma":[0.7313147,0.001354879,0.2638051,0.000114442,0.00002235396,0.0004402866,0.0004941371,0.0002533082,0.00220075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001200833,"threshold_uncertainty_score":0.004017174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04199420537119938,"score_gpt":0.229555737814478,"score_spread":0.1875615324432786,"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."}}