{"id":"W2165804878","doi":"10.1109/lmwc.2002.805946","title":"Numerical TRL calibration technique for parameter extraction of planar integrated discontinuities in a deterministic MoM algorithm","year":2002,"lang":"en","type":"article","venue":"IEEE Microwave and Wireless Components Letters","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Classification of discontinuities; Discontinuity (linguistics); Microstrip; Method of moments (probability theory); Calibration; Planar; Electronic engineering; Capacitance; Equivalent circuit; Electronic circuit; Scattering parameters; Algorithm; Engineering; Computer science; Mathematics; Mathematical analysis; Electrical engineering; Voltage; Physics","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":[0.0001135408,0.000210121,0.0003172422,0.0002285922,0.00003397624,0.00003374332,0.0000930806,0.0001081908,0.000006353908],"category_scores_gemma":[0.00000776464,0.0002075155,0.00006586991,0.0001384867,0.00005088311,0.0001479798,0.000004941759,0.000165844,8.752036e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006085648,"about_ca_system_score_gemma":0.000003697843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007666613,"about_ca_topic_score_gemma":0.00001203341,"domain_scores_codex":[0.9989772,0.00004627582,0.0004037716,0.0002097361,0.0001063142,0.0002566922],"domain_scores_gemma":[0.9996271,0.0001053538,0.00007420788,0.0001268538,0.00002223855,0.00004421943],"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":[0.00002074258,0.00005737323,0.000181626,0.00009755993,0.00002640831,0.000009722886,0.0002016533,0.00003305526,0.9819869,0.000008862969,0.001860427,0.01551566],"study_design_scores_gemma":[0.0004126819,0.00006699514,0.0002217246,0.0001367088,0.00001892409,0.00002413151,0.00001699294,0.1063171,0.8922238,0.00008463408,0.0002441656,0.0002321654],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5058711,0.00009003007,0.4934347,0.00004403754,0.00007006055,0.0003758773,0.00001980035,0.00006856979,0.00002578422],"genre_scores_gemma":[0.9897389,0.0001069886,0.009735618,0.0001255251,0.00003665475,0.0001695423,0.00004093696,0.00003194291,0.00001383036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4838678,"threshold_uncertainty_score":0.8462237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0249499109715304,"score_gpt":0.2231362484039987,"score_spread":0.1981863374324683,"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."}}