{"id":"W4401452657","doi":"10.1109/piers62282.2024.10618381","title":"Autoencoder Parameter Extraction Technique for Parametric Modeling of Third-order Waveguide Filter","year":2024,"lang":"en","type":"article","venue":"","topic":"Electromagnetic Compatibility and Noise Suppression","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Autoencoder; Parametric statistics; Extraction (chemistry); Filter (signal processing); Computer science; Parametric model; Biological system; Electronic engineering; Artificial intelligence; Artificial neural network; Mathematics; Engineering; Computer vision; Statistics; Chromatography; Chemistry","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":[0.00039771,0.0006165933,0.0003589181,0.0003288215,0.0001996478,0.0003437398,0.0005234313,0.0004766758,0.001130175],"category_scores_gemma":[0.0006805196,0.0003509001,0.0006510755,0.0002895705,0.000289443,0.000734848,0.0002818263,0.0009755893,0.0004326851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003468146,"about_ca_system_score_gemma":0.0005093606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002779747,"about_ca_topic_score_gemma":0.002744704,"domain_scores_codex":[0.9998196,0.00003033342,0.00001133022,0.00004638807,0.00007727877,0.00001518728],"domain_scores_gemma":[0.9998102,0.00006668134,0.00002837938,0.00002983895,0.00005976513,0.000005259805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009365321,0.00006109907,0.0008837728,0.0001241417,0.00009378665,0.0001106088,0.0001309491,0.5821681,0.1161492,0.01031864,0.0009982799,0.2888677],"study_design_scores_gemma":[0.000001900032,0.0000178446,0.000207707,0.000004500424,0.000008267417,0.00003895723,0.000003550475,0.9823352,0.01555606,0.0006478111,0.001171259,0.00000693127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00449656,0.00005854048,0.9947309,0.00001818886,0.000009315245,0.000009985531,0.00001523172,0.000235114,0.0004261202],"genre_scores_gemma":[0.4145032,0.0006007603,0.5775926,0.00007875164,0.00003771143,0.0001712831,0.0002959388,0.000238339,0.006481474],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002779747,"threshold_uncertainty_score":0.005527079,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0211839560128527,"score_gpt":0.2730704256817577,"score_spread":0.251886469668905,"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."}}