{"id":"W3140533167","doi":"10.18280/ts.380110","title":"Sampling Rate Optimization for Improving the Cascaded Integrator Comb Filter Characteristics","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Integrator; Stopband; Cascaded integrator–comb filter; Finite impulse response; Ripple; Computer science; Sampling (signal processing); Control theory (sociology); Electronic engineering; Low-pass filter; Filter (signal processing); Engineering; Algorithm; Bandwidth (computing); Band-pass filter; Root-raised-cosine filter; Telecommunications; Electrical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003744816,0.0001348634,0.000122542,0.00004010441,0.000211791,0.0006861052,0.0003258269,0.00002837375,0.0001111748],"category_scores_gemma":[0.00005791086,0.0001038605,0.0000751617,0.0001799449,0.00001893018,0.0005759124,0.00009589932,0.00007917433,0.00000689498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004610474,"about_ca_system_score_gemma":0.00008222477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000362502,"about_ca_topic_score_gemma":0.000001721601,"domain_scores_codex":[0.998895,0.00008186928,0.0003270301,0.0002811605,0.0001728036,0.0002421676],"domain_scores_gemma":[0.999237,0.0001907627,0.0001266648,0.0001929042,0.0001974591,0.00005520654],"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":[0.000174928,0.0006353948,0.0008085223,0.0003896213,0.000287627,0.0000414761,0.006612258,0.01408034,0.2451751,0.09306803,0.006807174,0.6319195],"study_design_scores_gemma":[0.001014329,0.0001888859,0.0009995811,0.00002812994,0.00003094452,0.00001229515,0.0002037826,0.9520482,0.03871259,0.0006593338,0.00583218,0.0002697035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009547675,0.0000150221,0.9886587,0.0009261901,0.0002883785,0.000348175,0.00003472748,0.00007384177,0.0001073393],"genre_scores_gemma":[0.9043265,0.000005588892,0.09274989,0.002118821,0.0002006614,0.0001189878,0.0002654256,0.00001719378,0.0001969252],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9379679,"threshold_uncertainty_score":0.6616129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04720030940592498,"score_gpt":0.2701120401994213,"score_spread":0.2229117307934963,"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."}}