{"id":"W4310584092","doi":"10.1109/ius54386.2022.9958166","title":"Optimized Transmission Electrical Broadband Impedance Matching for PolyCMUT","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Ultrasonics Symposium (IUS)","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Impedance matching; Bandwidth (computing); Electronic engineering; Broadband; Ringing; Chebyshev filter; Computer science; Electrical impedance; Electronic circuit; Electrical engineering; Filter (signal processing); Engineering; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004405886,0.0002922919,0.0002777535,0.0001962634,0.0003820439,0.0001273054,0.0005903873,0.00009322636,0.0004960323],"category_scores_gemma":[0.00003757418,0.0003262564,0.0002424754,0.0002656374,0.00002403238,0.0002161351,0.00003929097,0.0005389963,0.00001240082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005498516,"about_ca_system_score_gemma":0.00008201368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001985282,"about_ca_topic_score_gemma":0.000001457396,"domain_scores_codex":[0.9978384,0.00005038439,0.0005124228,0.000429797,0.0006946817,0.0004743199],"domain_scores_gemma":[0.9990742,0.0003719359,0.0001113968,0.0002166436,0.00009453843,0.0001312995],"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":[0.0001450031,0.00009688111,0.00002193083,0.00003125905,0.0001244213,0.000006046602,0.0003214511,0.5986525,0.3927148,0.001604679,0.003308509,0.00297251],"study_design_scores_gemma":[0.0016677,0.0001411099,0.00003095764,0.00002376069,0.00006089893,0.00009386351,0.00009194023,0.9233673,0.02397306,0.001443482,0.04864701,0.0004588864],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1412927,0.0008587334,0.844014,0.001688446,0.005390523,0.001026367,0.000656732,0.0005839675,0.004488516],"genre_scores_gemma":[0.9747234,0.0008876615,0.02143587,0.0002416141,0.0003100499,0.0005017224,0.0003664085,0.0001269256,0.001406372],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8334306,"threshold_uncertainty_score":0.9999189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008038783439497595,"score_gpt":0.2311006324373613,"score_spread":0.2230618489978637,"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."}}