{"id":"W4399722826","doi":"10.32920/26052370","title":"Intelligent Systems for Active Noise Control Within Aircraft Cabins","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Vehicle Noise and Vibration Control","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Noise (video); Aeronautics; Active noise control; Aircraft noise; Control (management); Computer science; Noise control; Acoustics; Aerospace engineering; Engineering; Artificial intelligence; Physics; Noise reduction","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":[0.0002069111,0.0004011042,0.000552564,0.0001768966,0.00004311611,0.0002579724,0.0002383244,0.0004074894,0.00005355118],"category_scores_gemma":[0.00003352585,0.0003568147,0.0002829167,0.00007283116,0.00001808209,0.00006054361,0.0001140905,0.00067191,0.0002233755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002293769,"about_ca_system_score_gemma":0.0001330327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001232328,"about_ca_topic_score_gemma":0.00008563108,"domain_scores_codex":[0.998499,0.00003055218,0.0005470971,0.0004267606,0.0001924125,0.0003042225],"domain_scores_gemma":[0.9990985,0.0001519223,0.00006433619,0.0004096823,0.0001378354,0.0001376885],"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":[0.00005707885,0.00002387062,0.000008372228,0.001640551,0.0008854202,0.000007626623,0.0006107271,0.9749143,0.001562817,0.006424278,0.01127013,0.002594887],"study_design_scores_gemma":[0.0005176533,0.00004037407,0.00000836873,0.0002486953,0.0002160375,0.000003281321,0.0002126698,0.9830095,0.005150519,0.001411268,0.008762308,0.0004193866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01157947,0.004927343,0.9517924,0.0007197723,0.0119082,0.004762974,0.001234007,0.002441241,0.01063461],"genre_scores_gemma":[0.9943743,0.00009847464,0.0003352719,0.0001284159,0.00098624,0.001624839,0.00007990697,0.0001263503,0.002246245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9827948,"threshold_uncertainty_score":0.9998884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01612580391835207,"score_gpt":0.2457828509597059,"score_spread":0.2296570470413538,"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."}}