{"id":"W2580250172","doi":"10.1109/epeps.2016.7835426","title":"Loewner Matrix interpolation for noisy S-parameter data","year":2016,"lang":"en","type":"article","venue":"","topic":"Lightning and Electromagnetic Phenomena","field":"Physics and Astronomy","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Matrix pencil; Interpolation (computer graphics); Computer science; Algorithm; Perturbation (astronomy); Stability (learning theory); Matrix (chemical analysis); Noise (video); Mathematical optimization; Mathematics; Artificial intelligence","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007986014,0.00007088221,0.00007624151,0.0000216929,0.00003722243,0.00002773027,0.0002026774,0.00001339095,0.0009638527],"category_scores_gemma":[0.000007448939,0.00003931566,0.00002790937,0.00002949095,0.00001415795,0.0001633818,0.0000683451,0.00002123288,0.0001349056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006446569,"about_ca_system_score_gemma":0.00001476106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003074,"about_ca_topic_score_gemma":0.000001296296,"domain_scores_codex":[0.999467,0.000008298148,0.0001091226,0.0001940709,0.00004701828,0.0001744911],"domain_scores_gemma":[0.9994241,0.0001122392,0.00003215819,0.000373927,0.0000210276,0.00003652658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001604873,0.0002309888,0.02398626,0.00001764133,0.0002442942,2.14279e-7,0.0002375848,0.000001080353,0.1348179,0.2561691,0.2351374,0.3489971],"study_design_scores_gemma":[0.006129524,0.0009813894,0.004170218,0.0001102735,0.0002150665,0.000002277936,0.0002225321,0.02551781,0.04758704,0.2656703,0.6480267,0.001366898],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1833978,0.00002515098,0.7741035,0.002050536,0.0002690098,0.0002932326,0.0001806831,0.00007934264,0.03960078],"genre_scores_gemma":[0.9703367,2.97918e-7,0.009319536,0.00003981676,0.000349351,0.00001770232,0.00007341487,0.00001057337,0.01985266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7869389,"threshold_uncertainty_score":0.9999494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02391553549592655,"score_gpt":0.2904446734487279,"score_spread":0.2665291379528013,"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."}}