{"id":"W2160802653","doi":"10.1109/apccas.2006.342112","title":"A Genetic Algorithm Employing Correlative Roulette Selection for Optimization of FRM Digital Filters over CSD Multiplier Coefficient Space","year":2006,"lang":"en","type":"article","venue":"","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Digital filter; Multiplier (economics); Finite impulse response; Algorithm; Crossover; Minification; Mathematics; Computer science; Filter design; Filter (signal processing); Mathematical optimization; 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":[0.0007090657,0.0004412281,0.0004885363,0.0006058358,0.0002662275,0.0003328327,0.0005222111,0.000748045,0.0006825429],"category_scores_gemma":[0.001539961,0.0002177492,0.0003800879,0.0004881951,0.0005282384,0.000245925,0.0002282693,0.0003553463,0.0001260139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005120534,"about_ca_system_score_gemma":0.0008232433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002994813,"about_ca_topic_score_gemma":0.003096415,"domain_scores_codex":[0.9997726,0.00007278405,0.000009900778,0.0000493338,0.00007333112,0.00002213081],"domain_scores_gemma":[0.9996347,0.0002120823,0.00005178731,0.00002251567,0.00006814387,0.00001059392],"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":[0.00006382083,0.00004806431,0.0006944414,0.00004303783,0.00004732266,0.00008521069,0.00009477489,0.8551248,0.01763884,0.0109111,0.000447332,0.1148013],"study_design_scores_gemma":[0.00001985726,0.00004799428,0.0001296581,0.000004626536,0.00001080869,0.00003174095,0.000004783734,0.9960622,0.002341577,0.0007623211,0.0005784618,0.000005911129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05433601,0.000159844,0.9435501,0.00008221771,0.00002492409,0.00005151966,0.00001275656,0.0002508657,0.001531941],"genre_scores_gemma":[0.3824628,0.0001777297,0.61454,0.0000764342,0.00002564047,0.0002117327,0.00005418451,0.00003943932,0.002412084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002994813,"threshold_uncertainty_score":0.005954742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01161475683458685,"score_gpt":0.2393623259794654,"score_spread":0.2277475691448785,"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."}}