{"id":"W4399722722","doi":"10.32920/26052370.v1","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; Aircraft noise; Active noise control; Noise control; Control (management); Computer science; Automotive engineering; Aerospace engineering; Acoustics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003411945,0.0005099849,0.0003099047,0.0002524855,0.0003947263,0.001141805,0.0006401318,0.0006992455,0.002202338],"category_scores_gemma":[0.0006111705,0.0002391614,0.0004414317,0.0001697183,0.0005153366,0.0006845995,0.0006756773,0.0006457256,0.0005391681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005521694,"about_ca_system_score_gemma":0.0008303402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004013612,"about_ca_topic_score_gemma":0.003794187,"domain_scores_codex":[0.9996924,0.00005466401,0.00001294674,0.00005114028,0.0001578311,0.00003103202],"domain_scores_gemma":[0.9997997,0.0000580681,0.00003432088,0.00002700271,0.00007015,0.00001077047],"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":[0.00008738502,0.00008043386,0.001098723,0.000281402,0.00005160714,0.0001503525,0.0002918358,0.7772016,0.05184679,0.07009993,0.001602219,0.09720771],"study_design_scores_gemma":[0.00001420014,0.000169855,0.0006226011,0.00003445113,0.00002549348,0.00005417608,0.00005124369,0.9729987,0.006528551,0.007669483,0.01181372,0.00001747119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01499205,0.0004803954,0.9725495,0.0001154169,0.00006629238,0.00005933814,0.00002720312,0.0007241106,0.01098565],"genre_scores_gemma":[0.8441636,0.0008077252,0.1413407,0.00008731832,0.00005268673,0.0002193246,0.0001198782,0.00008057845,0.01312818],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004013612,"threshold_uncertainty_score":0.007980466,"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."}}