{"id":"W2101350778","doi":"10.1109/icecs.2007.4510976","title":"Common Subexpression Elimination for Digital Filters Using Genetic Algorithm","year":2007,"lang":"en","type":"article","venue":"","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Travelling salesman problem; Algorithm; Simple (philosophy); Zero (linguistics); Finite impulse response; Genetic algorithm; Digital filter; Computer science; Position (finance); Mathematics; Arithmetic; Mathematical optimization; Filter (signal processing); Computer vision","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":[0.0001507253,0.00008939731,0.000072902,0.0001061512,0.0000822769,0.0004116357,0.0002651131,0.0000278723,0.000006068166],"category_scores_gemma":[0.00001100759,0.00008203633,0.00004565619,0.0001526377,0.00001313121,0.001464387,0.00008085582,0.00002374691,0.000008818747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000048232,"about_ca_system_score_gemma":0.00001528619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008422882,"about_ca_topic_score_gemma":0.000001323458,"domain_scores_codex":[0.999144,0.000007397933,0.0002211069,0.0002099563,0.0001844978,0.000232999],"domain_scores_gemma":[0.9995353,0.00008731916,0.00006336629,0.0001838141,0.00006279725,0.00006744592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005264587,0.00003510858,0.0001542511,0.000006012951,0.00000413165,0.000002447585,0.0001243751,0.00003233027,0.004318839,0.002250706,0.0006612487,0.9924053],"study_design_scores_gemma":[0.001040911,0.0004065974,0.009157505,0.0000344842,0.000008841078,0.00003691553,0.000162224,0.8538767,0.1200372,0.009981291,0.004833541,0.0004237735],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01761019,0.000006976104,0.9798964,0.00006845876,0.0002129182,0.0002524852,0.000008882952,0.00009364627,0.001850088],"genre_scores_gemma":[0.5170619,5.834713e-7,0.482457,0.0001667731,0.00004516145,0.000004493374,0.00002845861,0.00000699519,0.0002286076],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9919815,"threshold_uncertainty_score":0.3969412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03979874358163344,"score_gpt":0.3138405326702732,"score_spread":0.2740417890886397,"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."}}