{"id":"W2013691681","doi":"10.1115/1.1636772","title":"Multi-Channel Adaptive Feedforward Control of Noise in an Acoustic Duct","year":2004,"lang":"en","type":"article","venue":"Journal of Dynamic Systems Measurement and Control","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Feed forward; Noise control; Duct (anatomy); Computer science; Acoustics; Control channel; Channel (broadcasting); Active noise control; Noise (video); Adaptive control; Control (management); Control theory (sociology); Telecommunications; Engineering; Physics; Noise reduction; Control engineering; Artificial intelligence; Biology","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":[0.0003620293,0.0005667469,0.000497766,0.0002667369,0.0004029167,0.0004379335,0.0007574463,0.00060488,0.001171621],"category_scores_gemma":[0.0005856226,0.0001766592,0.0002297539,0.0002085601,0.0003785805,0.0004398396,0.0005776334,0.0004264068,0.0001646624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003080439,"about_ca_system_score_gemma":0.0003649293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001128723,"about_ca_topic_score_gemma":0.002011332,"domain_scores_codex":[0.9998006,0.00003350292,0.00001239129,0.00004187605,0.00008867596,0.00002299039],"domain_scores_gemma":[0.9997496,0.0001013128,0.00003568842,0.00001207713,0.00008409145,0.00001723505],"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":[0.0007173418,0.000206622,0.001129055,0.0006091277,0.00007498427,0.0005849315,0.000350757,0.5427756,0.2160638,0.01067232,0.001624963,0.2251906],"study_design_scores_gemma":[0.00002360982,0.0002833015,0.0003829269,0.00002044133,0.00001811243,0.00006629233,0.00001642561,0.983313,0.01282549,0.001365857,0.00166808,0.00001642691],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0933413,0.000771067,0.8990438,0.0002057084,0.0003345163,0.00005378473,0.0000330495,0.0008650097,0.005351832],"genre_scores_gemma":[0.9585499,0.0003026775,0.03584921,0.0000542819,0.00005753917,0.00006655732,0.00002213067,0.00001967967,0.005077929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001171621,"threshold_uncertainty_score":0.003919482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02231515680589487,"score_gpt":0.2311683794503706,"score_spread":0.2088532226444758,"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."}}