{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002360312,0.0001843427,0.0002255723,0.00008414063,0.0001652248,0.0002494242,0.0003935637,0.00005316106,0.00001333717],"category_scores_gemma":[0.000017802,0.0001841756,0.00005584222,0.000573863,0.00005696647,0.001452854,0.0000871521,0.0001100078,0.00001071539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004350374,"about_ca_system_score_gemma":0.0002088028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007852126,"about_ca_topic_score_gemma":2.02964e-7,"domain_scores_codex":[0.9984166,0.0001174835,0.0004729218,0.000404982,0.0003256201,0.0002623655],"domain_scores_gemma":[0.99915,0.00007056214,0.0003440432,0.000108269,0.0001984007,0.0001287568],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001389707,0.0002241619,0.00000679407,0.00005848972,0.00002696952,0.000008556932,0.00214499,0.005311073,0.6024207,0.000417138,0.0001228128,0.3891193],"study_design_scores_gemma":[0.0004431478,0.0003621815,0.00000959315,0.00003485741,0.00001906242,0.000002018386,0.00007686219,0.7704779,0.2270419,0.001350109,0.0000254378,0.0001569388],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01624989,0.00005296965,0.9829705,0.000191707,0.00004746021,0.0003191886,0.00001783324,0.0001023781,0.00004809248],"genre_scores_gemma":[0.8547019,0.000001632119,0.1448569,0.0003008438,0.00009238763,0.0000105045,0.00001461202,0.00001532074,0.000005931007],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.838452,"threshold_uncertainty_score":0.7510465,"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."}}