{"id":"W2099003604","doi":"10.1109/icassp.1982.1171786","title":"Non-stationary learning characteristics of adaptive lattice filters","year":2005,"lang":"en","type":"article","venue":"","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Lattice phase equaliser; Adaptive filter; Lattice (music); Kernel adaptive filter; Filter (signal processing); Computer science; Control theory (sociology); Algorithm; Lattice constant; Mathematics; Filter design; Artificial intelligence; Physics; Acoustics; Optics; Diffraction; Computer vision","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":[0.001570759,0.0002690366,0.0003838234,0.0004450059,0.0003884077,0.0006140044,0.0005159626,0.0008444682,0.002217391],"category_scores_gemma":[0.01563163,0.0002942151,0.0002081332,0.0003219622,0.0008513518,0.001139506,0.0004500772,0.000934532,0.0005464884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000373664,"about_ca_system_score_gemma":0.0004124875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007189356,"about_ca_topic_score_gemma":0.0004028121,"domain_scores_codex":[0.999122,0.0001266933,0.00004193648,0.0001285856,0.0004854347,0.00009539798],"domain_scores_gemma":[0.9927996,0.00491223,0.0005391709,0.0004744139,0.001039399,0.0002351114],"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":[0.001104871,0.0002411722,0.001697788,0.0002132415,0.00002870267,0.0002254847,0.0005450191,0.02866493,0.873998,0.005135631,0.0003277443,0.08781748],"study_design_scores_gemma":[0.0002282839,0.001967034,0.01863946,0.0000457721,0.0000437565,0.0008509294,0.0002193853,0.4090945,0.5593752,0.007054618,0.002387095,0.00009400705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8156707,0.000361899,0.1763414,0.0003224022,0.00009095029,0.0001812529,0.00009640902,0.0005930371,0.006341887],"genre_scores_gemma":[0.987252,0.00008400156,0.01106288,0.00005318672,0.000012252,0.00005817863,0.00005263631,0.0000591574,0.00136563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002217391,"threshold_uncertainty_score":0.008307099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01145027782819668,"score_gpt":0.2282196617919801,"score_spread":0.2167693839637834,"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."}}