{"id":"W2944992650","doi":"10.2514/6.2019-2655","title":"Optimization of serrations for broadband trailing-edge noise reduction using an analytical model","year":2019,"lang":"en","type":"article","venue":"","topic":"Acoustic Wave Phenomena Research","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"University of Cambridge","keywords":"Broadband; Reduction (mathematics); Trailing edge; Noise reduction; Noise (video); Acoustics; Enhanced Data Rates for GSM Evolution; Computer science; Electronic engineering; Telecommunications; Physics; Mathematics; Engineering; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001221357,0.00007066498,0.0001123128,0.0001076002,0.00002982542,0.0000176961,0.00006336603,0.0000553023,0.0001013424],"category_scores_gemma":[0.00001986033,0.00007190727,0.000031436,0.000143365,0.00001961826,0.0002433649,0.000008901925,0.00005915486,0.000002520645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005407124,"about_ca_system_score_gemma":0.00004142076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006546499,"about_ca_topic_score_gemma":0.000001474768,"domain_scores_codex":[0.9994259,0.00000761201,0.0001781636,0.0001221421,0.0001171098,0.0001490437],"domain_scores_gemma":[0.9996213,0.00002040836,0.00001552491,0.0001633681,0.0001209548,0.00005847411],"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":[0.00001051781,0.00002866155,0.00001087757,0.00005754014,0.00001442907,2.278682e-8,0.0001450651,0.9360229,0.06281211,0.0007268059,0.00004958711,0.0001215065],"study_design_scores_gemma":[0.0002577733,0.00003782554,0.0000101926,0.000006377214,0.00002052823,0.00000132316,0.0001610247,0.9963341,0.002896541,0.0001916222,0.000002962769,0.00007972754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2741258,0.000006646512,0.7235658,0.00001145424,0.00005427437,0.0002476861,0.000009984451,0.00005218698,0.00192624],"genre_scores_gemma":[0.8879073,0.000002729341,0.1114902,0.000002178267,0.00004739054,0.000006783371,0.0000285286,0.00002369717,0.0004912161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6137816,"threshold_uncertainty_score":0.2932293,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0641264528280109,"score_gpt":0.3123813985989027,"score_spread":0.2482549457708919,"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."}}