{"id":"W1538595438","doi":"10.1109/iscas.2006.1693571","title":"A two-stage genetic algorithm for the design and optimization of resonator/integrator based sigma-delta A/D and D/A converters","year":2006,"lang":"en","type":"article","venue":"","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Integrator; Transfer function; Cascade; Oversampling; Delta-sigma modulation; Converters; Control theory (sociology); Mathematics; Noise shaping; Noise (video); Algorithm; Electronic engineering; Computer science; Engineering; Bandwidth (computing); CMOS; Telecommunications; Electrical engineering; Artificial intelligence","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":[0.0005968153,0.0007921839,0.0005933792,0.0004814408,0.0003222346,0.0004204341,0.000836838,0.001012787,0.0008931239],"category_scores_gemma":[0.001023266,0.0003920192,0.0005070625,0.0004241909,0.0004281968,0.0003259935,0.0003597155,0.0006351891,0.000190557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006529543,"about_ca_system_score_gemma":0.001071729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002949866,"about_ca_topic_score_gemma":0.003151161,"domain_scores_codex":[0.999804,0.00006252585,0.000009868048,0.00003681624,0.00006277866,0.00002404936],"domain_scores_gemma":[0.9997943,0.0001167288,0.00001681383,0.000009820738,0.00005359572,0.000008691993],"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":[0.00005135812,0.00006233553,0.0005658655,0.00005541952,0.00004022486,0.00005723995,0.00007648223,0.8916424,0.01074119,0.006706912,0.0004279064,0.08957271],"study_design_scores_gemma":[0.00002139444,0.00004398347,0.00009549318,0.000004840358,0.000008300695,0.00001561521,0.000006838783,0.9962475,0.001938356,0.001020087,0.0005927354,0.000004840644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02188429,0.0001301081,0.9762713,0.00005290338,0.0000173097,0.0000702453,0.00001629455,0.0002407955,0.001316795],"genre_scores_gemma":[0.2100198,0.0001622083,0.7874314,0.00007572478,0.00001349155,0.0003271468,0.00007102012,0.00004294253,0.001856344],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002949866,"threshold_uncertainty_score":0.005865395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01034114923591524,"score_gpt":0.1976067143209375,"score_spread":0.1872655650850223,"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."}}