{"id":"W1499009694","doi":"10.1109/aps.2005.1552248","title":"Causal Parameter Extractions by Vector Fitting for Use in Time-domain Numerical Modeling","year":2005,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Compatibility and Noise Suppression","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Time domain; Frequency domain; Convolution (computer science); Weighting; Computer science; Algorithm; Domain (mathematical analysis); Exponential function; Rational function; Curve fitting; Range (aeronautics); Applied mathematics; Mathematics; Mathematical analysis; Artificial intelligence; Machine learning; Artificial neural network; Engineering","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":[0.0009493007,0.0007067752,0.0004400826,0.0007354936,0.0003599507,0.0008350501,0.0007835743,0.0007598154,0.006351831],"category_scores_gemma":[0.004611036,0.0005781699,0.0005552745,0.001142493,0.0004248972,0.001788213,0.0006009844,0.0008974001,0.001647686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003932543,"about_ca_system_score_gemma":0.0007602996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00129847,"about_ca_topic_score_gemma":0.001443691,"domain_scores_codex":[0.9996282,0.0000908393,0.00003025782,0.00005512505,0.0001726054,0.00002295243],"domain_scores_gemma":[0.9989945,0.0004459006,0.00009996596,0.0002775492,0.0001655889,0.00001657037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001459261,0.0001582759,0.0010041,0.0002133986,0.0000470605,0.0001856201,0.0002928125,0.38168,0.05106402,0.09591106,0.003297491,0.4660002],"study_design_scores_gemma":[0.00001180954,0.00002491654,0.0001170988,0.000008797449,0.00000731818,0.00005479399,0.00001663536,0.9632811,0.0174167,0.01237673,0.006662024,0.00002205278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00157016,0.00001538991,0.9971657,0.00001683751,0.0000108798,0.0000135634,0.00001898324,0.0008193493,0.0003691007],"genre_scores_gemma":[0.1070416,0.0001820229,0.889637,0.00003712188,0.00001758102,0.0001439153,0.0002329903,0.0005523325,0.002155515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006351831,"threshold_uncertainty_score":0.021249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01683536751304624,"score_gpt":0.2417869941359621,"score_spread":0.2249516266229159,"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."}}