{"id":"W2094523746","doi":"10.1115/detc2014-34526","title":"Automotive Glass Exciter Technology for Acoustic Application","year":2014,"lang":"en","type":"article","venue":"","topic":"Acoustic Wave Phenomena Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Magna International (Canada); University of Waterloo","funders":"","keywords":"Automotive industry; Exciter; Windshield; Finite element method; Automotive engineering; Component (thermodynamics); Actuator; Harmonic; Toughened glass; Engineering; Computer science; Mechanical engineering; Electrical engineering; Acoustics; Structural engineering","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":[0.0001194829,0.0004143604,0.0002462681,0.0001974145,0.0002421633,0.0004613424,0.0004766108,0.0005834092,0.004540604],"category_scores_gemma":[0.0001504804,0.0002183166,0.000428105,0.0001608636,0.0001654039,0.0005513703,0.0003363821,0.0002859312,0.001496891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003680529,"about_ca_system_score_gemma":0.0004118577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001556999,"about_ca_topic_score_gemma":0.002049151,"domain_scores_codex":[0.9998512,0.00001141978,0.000004698422,0.00002624991,0.00009025573,0.00001620936],"domain_scores_gemma":[0.9999647,0.000009007804,0.000006645314,0.000005688019,0.00001168993,0.000002264837],"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":[0.0001307753,0.00004426694,0.001614917,0.0006367364,0.00003828866,0.0005374726,0.0004223882,0.1182347,0.7370501,0.02475346,0.003097591,0.1134394],"study_design_scores_gemma":[0.00002918547,0.0008175212,0.004562575,0.00008905086,0.0001020987,0.0009655522,0.0002915822,0.5659652,0.2489541,0.004071224,0.1740852,0.00006676672],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1569234,0.003064804,0.7904031,0.0003963859,0.000317786,0.0002319581,0.0003150241,0.001802853,0.04654476],"genre_scores_gemma":[0.8891225,0.003384397,0.0657439,0.0001011667,0.00006509879,0.0001764885,0.0002885835,0.000136449,0.04098141],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004540604,"threshold_uncertainty_score":0.01518977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006566971725187657,"score_gpt":0.2359467549235624,"score_spread":0.2293797831983748,"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."}}