{"id":"W1827452565","doi":"10.1109/icassp.1985.1168203","title":"Effective multifrequency receiver design","year":2005,"lang":"en","type":"article","venue":"","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Aliasing; Computer science; Reduction (mathematics); Decoding methods; Algorithm; Anti-aliasing; Electronic engineering; Computer engineering; Speech recognition; Speech coding; Mathematics; Telecommunications; Engineering; Digital audio","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":[0.0001448709,0.00007009404,0.00005442712,0.00005229905,0.00003978712,0.0001455374,0.0002934561,0.00001700917,0.00009197494],"category_scores_gemma":[0.00001749808,0.0000585936,0.00002594679,0.0001558354,0.00001061243,0.001486433,0.00004561646,0.00003151751,0.0007577838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004549857,"about_ca_system_score_gemma":0.00001646918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001009259,"about_ca_topic_score_gemma":0.000002879669,"domain_scores_codex":[0.999382,0.00005253407,0.0001029228,0.000191938,0.0001198256,0.0001507971],"domain_scores_gemma":[0.9996066,0.00008592103,0.00002406699,0.0001951654,0.00003780733,0.00005043421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002781284,0.00003515447,0.00005616147,0.000001270804,0.000006851929,0.000002019546,0.0003559458,0.00006187638,0.004587668,0.04008824,0.008813899,0.9459881],"study_design_scores_gemma":[0.002508106,0.0009381105,0.0138093,0.00002641881,0.00001194572,0.00004748325,0.00005273729,0.2602468,0.5916997,0.03741454,0.09227779,0.000967078],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007062649,0.00001326213,0.956827,0.0005112787,0.00008789459,0.000264081,4.437121e-7,0.0001709472,0.04141878],"genre_scores_gemma":[0.5830374,0.000002561927,0.41416,0.0008619301,0.0000434273,0.00002873814,0.00000122449,0.00000388768,0.001860815],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.945021,"threshold_uncertainty_score":0.974003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02724193831103008,"score_gpt":0.2700410168206039,"score_spread":0.2427990785095738,"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."}}