{"id":"W3135360189","doi":"10.1049/iet-spr.2019.0587","title":"Design of <i>p</i> ‐norm linear phase FIR differentiators using adaptive modification rate artificial bee colony algorithm","year":2020,"lang":"en","type":"article","venue":"IET Signal Processing","topic":"Digital Filter Design and Implementation","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Differentiator; Finite impulse response; Algorithm; Linear phase; Norm (philosophy); Adaptive filter; Computer science; Mathematics; Mathematical optimization; Filter (signal processing)","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":[0.0004954058,0.0004177733,0.0003544936,0.0002849496,0.0001829079,0.0003832302,0.0007616832,0.0006907149,0.001080452],"category_scores_gemma":[0.001367642,0.0002049162,0.0003092619,0.0002403542,0.0002863142,0.0003681245,0.0002321689,0.0004819735,0.0002876395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003092958,"about_ca_system_score_gemma":0.0004425512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009454812,"about_ca_topic_score_gemma":0.001034326,"domain_scores_codex":[0.9997792,0.00004739166,0.00001711724,0.00004297057,0.00009632742,0.00001700557],"domain_scores_gemma":[0.9996058,0.0001405319,0.0000606716,0.00002722334,0.0001513336,0.00001448191],"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":[0.0001258244,0.0000876798,0.001037825,0.0002660759,0.0000634817,0.0001302267,0.000163575,0.606001,0.06610192,0.01478232,0.001410072,0.3098301],"study_design_scores_gemma":[0.00001364264,0.00007022996,0.0001226606,0.000007618531,0.000007899446,0.00003967944,0.000006289849,0.9921314,0.00559243,0.0006443054,0.001357672,0.00000612811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01163905,0.0001034142,0.9859293,0.00003957885,0.0000210859,0.00003415628,0.000005809256,0.0001382764,0.002089304],"genre_scores_gemma":[0.3806587,0.000145051,0.6155599,0.0000945982,0.00001823349,0.0001753112,0.00003594931,0.00004473401,0.003267381],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001080452,"threshold_uncertainty_score":0.003614485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1444829949938323,"score_gpt":0.3316646171062672,"score_spread":0.1871816221124349,"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."}}