{"id":"W4239632224","doi":"10.1121/1.4799926","title":"Upgrade of a multi-channel active noise control system for an industrial stack","year":2013,"lang":"en","type":"article","venue":"Proceedings of meetings on acoustics","topic":"Advanced Adaptive Filtering Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Soft dB (Canada)","funders":"","keywords":"Active noise control; Upgrade; Noise reduction; Noise (video); Noise control; Computer science; Controller (irrigation); Headphones; Digital signal processing; Channel (broadcasting); Electronic engineering; Control system; Stack (abstract data type); Engineering; Embedded system; Computer hardware; Electrical engineering; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002244833,0.0002861775,0.0004989628,0.0001815917,0.00004367205,0.00002702515,0.000310225,0.000226299,0.000002362039],"category_scores_gemma":[0.0005991105,0.0002884125,0.00008016466,0.0001586856,0.00007711819,0.0003262887,0.0000383545,0.0002497775,0.000001895259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001417766,"about_ca_system_score_gemma":0.00001547396,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001812304,"about_ca_topic_score_gemma":2.363205e-7,"domain_scores_codex":[0.9986251,0.000004172488,0.0005108853,0.0002600839,0.0002460126,0.0003537622],"domain_scores_gemma":[0.9983863,0.0001500063,0.0003564693,0.0001111832,0.0008750508,0.0001210327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002083965,0.0001277682,0.00005692517,0.001039964,0.00008409961,2.370173e-7,0.0004814816,0.01376865,0.9809055,0.0004801879,0.001199268,0.001647505],"study_design_scores_gemma":[0.00245351,0.0009927322,0.0002180164,0.0008103433,0.00009722328,0.000003114635,0.002073718,0.3078602,0.6846609,0.0003207194,0.000104805,0.0004047767],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8421834,0.00002701483,0.152475,0.00002732308,0.0002973157,0.002676693,0.0003630875,0.001179337,0.0007708572],"genre_scores_gemma":[0.9585844,0.000008736455,0.04082877,0.00001174004,0.0001713121,0.0002761029,0.000004344574,0.00009543902,0.00001909849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2962447,"threshold_uncertainty_score":0.9999568,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03312719628106945,"score_gpt":0.248306691038586,"score_spread":0.2151794947575166,"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."}}