{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002296972,0.0001360785,0.0001682811,0.0002251061,0.00002610866,0.00003107701,0.00008464453,0.0001243458,0.0002252597],"category_scores_gemma":[0.00008815794,0.0001142445,0.0001038937,0.0003683452,0.00001341821,0.0001567519,0.00001605932,0.000176554,0.000008574836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003901192,"about_ca_system_score_gemma":0.00002160569,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003020892,"about_ca_topic_score_gemma":0.000009056705,"domain_scores_codex":[0.9991213,0.00001651284,0.000322187,0.0002094572,0.0001233408,0.0002071938],"domain_scores_gemma":[0.9993241,0.0003398147,0.00001224649,0.0002230121,0.00006385319,0.00003701732],"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":[0.00004317279,0.00008750952,0.00002234098,0.001391849,0.00009659799,0.000003429776,0.0002268136,0.621054,0.3449737,0.001321993,0.006614768,0.02416388],"study_design_scores_gemma":[0.00007234626,0.00008024517,0.0000137326,0.0000855109,0.00002335133,0.000006895054,0.000008676429,0.8676981,0.1267225,0.004338065,0.0008266326,0.000123948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09365089,0.0006955701,0.8990993,0.0000527578,0.0002391669,0.0004778586,0.000006841713,0.0004606128,0.005316938],"genre_scores_gemma":[0.8761693,0.00004354512,0.1231215,0.00001326193,0.0000397432,0.0001542509,0.000009113359,0.00002939774,0.0004199331],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7825183,"threshold_uncertainty_score":0.4658757,"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."}}