{"id":"W2066064049","doi":"10.1109/icmmt.2007.381409","title":"Microwave Modeling Using Artificial Neural Networks and Applications to Embedded Passive Modeling","year":2007,"lang":"en","type":"article","venue":"","topic":"Microwave Engineering and Waveguides","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Artificial neural network; Resistor; Capacitor; Computer science; Component (thermodynamics); Electronic engineering; Solid modeling; CAD; Microwave; Electronic circuit; Artificial intelligence; Engineering; Electrical engineering; Engineering drawing; Voltage; Telecommunications","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":[0.0001662317,0.0002028167,0.0001735528,0.0001508579,0.0001076017,0.00006830074,0.00008877425,0.0001046331,0.000006319321],"category_scores_gemma":[0.000009099905,0.0002178994,0.00004485503,0.0002287064,0.00001082769,0.00008501646,0.0000425712,0.0001871553,0.000005317554],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005891504,"about_ca_system_score_gemma":0.000005806193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002588824,"about_ca_topic_score_gemma":0.00004121804,"domain_scores_codex":[0.9988922,0.00000510094,0.0003390322,0.0002402471,0.00008569202,0.0004376934],"domain_scores_gemma":[0.9995199,0.0000296726,0.00001265012,0.0001929764,0.00005664955,0.0001881834],"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":[0.000003911133,0.000004737082,0.000003101179,0.0000143562,0.00001536652,0.000002233362,0.00009421859,0.9505442,0.03840594,0.0008386124,0.00002054658,0.01005273],"study_design_scores_gemma":[0.00006561015,0.000006466298,0.000001362604,0.00001587269,0.00001602098,0.00001759564,0.0002220865,0.9955156,0.003549458,0.0002890454,0.00003650628,0.0002643104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2465744,0.0002147643,0.752105,0.00000897029,0.0001260363,0.000182881,0.000001700265,0.0002910937,0.0004951897],"genre_scores_gemma":[0.9689359,0.000009885518,0.03043622,0.00006357479,0.0004661775,0.00001164206,0.000006551552,0.0000564539,0.00001357852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7223615,"threshold_uncertainty_score":0.8885682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02591908562525426,"score_gpt":0.2495112190185686,"score_spread":0.2235921333933144,"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."}}