{"id":"W2125168427","doi":"10.1109/isspa.1999.815830","title":"Design of 1-D FIR filters with genetic algorithms","year":2003,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Finite impulse response; Minimax; Algorithm; Convergence (economics); Ternary operation; Mathematics; Rate of convergence; Filter (signal processing); Genetic algorithm; Computer science; Mathematical optimization; Telecommunications","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":[0.00003556675,0.00009893596,0.0001034289,0.00004207393,0.000009757407,0.000003738319,0.0000731284,0.00002664512,0.00008320177],"category_scores_gemma":[0.000006935111,0.00008232151,0.00001327978,0.0000926705,0.00002909797,0.00005058046,0.000006339314,0.0000487969,0.000005472641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002214482,"about_ca_system_score_gemma":0.000006318482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001950023,"about_ca_topic_score_gemma":4.583835e-7,"domain_scores_codex":[0.999592,0.00001214657,0.0001005173,0.00008897564,0.0000718355,0.0001345117],"domain_scores_gemma":[0.9997376,0.00002775629,0.00001389215,0.0001700597,0.00002092473,0.00002980216],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001417062,0.00002911602,0.0002526122,0.00007359328,0.00007576316,0.00002525391,0.0001288888,0.844051,0.140837,0.001781483,0.001217215,0.01151395],"study_design_scores_gemma":[0.0003139607,0.0002895485,0.0007968,0.00005601501,0.00001189885,0.00003471251,0.00003312797,0.06273568,0.9307005,0.001677138,0.002989003,0.0003616068],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004001311,0.0000718201,0.9915381,0.000001908645,0.00002648127,0.0001307324,0.000001428171,0.0004432339,0.003784994],"genre_scores_gemma":[0.3213224,0.00001952258,0.678489,0.000006931648,0.000004072881,0.00001553684,3.329419e-7,0.00002276803,0.000119457],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7898635,"threshold_uncertainty_score":0.3356974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01777514298197788,"score_gpt":0.2119892731898511,"score_spread":0.1942141302078732,"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."}}