{"id":"W4412812552","doi":"10.3397/nc_2025_0038","title":"AI-Driven Optimization of Acoustic Metamaterials for Low-Frequency Noise Attenuation in Aerospace Applications","year":2025,"lang":"en","type":"article","venue":"NOISE-CON proceedings","topic":"Acoustic Wave Phenomena Research","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Acoustics; Attenuation; Metamaterial; Aerospace; Infrasound; Acoustic attenuation; Noise (video); Low frequency; Computer science; Aerospace engineering; Physics; Engineering; Telecommunications; Optics; Artificial intelligence","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":[0.0003524803,0.0004398884,0.000268012,0.0001997135,0.0001198269,0.0003811409,0.0004004213,0.0004593506,0.0007258799],"category_scores_gemma":[0.0007677916,0.0002579727,0.0002355977,0.0001359059,0.0004792082,0.000444724,0.0005031022,0.000587424,0.0001640237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005037517,"about_ca_system_score_gemma":0.0005594586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008102406,"about_ca_topic_score_gemma":0.001762289,"domain_scores_codex":[0.9998837,0.0000229809,0.000004571284,0.00002314127,0.00005059987,0.00001506871],"domain_scores_gemma":[0.9997776,0.0001132346,0.00003890611,0.00001750193,0.00004121648,0.00001153721],"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":[0.00002538812,0.00002552083,0.0002337981,0.00004963609,0.00001442784,0.00002209883,0.00001452356,0.9462593,0.03128093,0.004467208,0.0002202343,0.01738688],"study_design_scores_gemma":[0.000001744077,0.00001126551,0.00002885691,0.000002037447,0.000001778625,0.000003395986,0.000001838757,0.995507,0.00332372,0.0008717269,0.0002451161,0.000001471484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07966197,0.0003478482,0.9122041,0.0003051454,0.00003978983,0.0000302287,0.00004763604,0.0004064045,0.006956851],"genre_scores_gemma":[0.8303664,0.0002373866,0.1656156,0.0001402447,0.00002054397,0.00009162138,0.00007453711,0.0001036781,0.003349951],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008102406,"threshold_uncertainty_score":0.003654957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01141888078590877,"score_gpt":0.2663390810881489,"score_spread":0.2549202003022402,"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."}}