{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003503675,0.001111297,0.000658883,0.0004972765,0.0003387484,0.0008686226,0.000446807,0.0009790064,0.002482578],"category_scores_gemma":[0.0009105331,0.0003666434,0.0005360955,0.0003502248,0.000387899,0.0006078132,0.0004094092,0.0004349726,0.0006233937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004698654,"about_ca_system_score_gemma":0.000697786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001520507,"about_ca_topic_score_gemma":0.002111819,"domain_scores_codex":[0.999817,0.0000395003,0.000007119721,0.00003324141,0.00006248595,0.00004073273],"domain_scores_gemma":[0.9998152,0.00008075903,0.00002821953,0.00001230138,0.00005313179,0.00001034728],"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":[0.0000935364,0.00009520802,0.0001674245,0.0001057819,0.0000218157,0.00005551746,0.00003592026,0.9327756,0.02786584,0.006803666,0.0007638352,0.03121591],"study_design_scores_gemma":[0.000006282843,0.00003783438,0.00005869009,0.000005980158,0.000008408302,0.00001359619,0.00001132186,0.9961126,0.002887777,0.0004361359,0.0004171558,0.000004251427],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07254829,0.0007541926,0.9009593,0.0002004365,0.00009868223,0.00007463794,0.00005289197,0.0003853378,0.02492619],"genre_scores_gemma":[0.8423859,0.0004917331,0.1500716,0.00008480403,0.00003937526,0.00009415542,0.00005416883,0.0001461959,0.006631969],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002482578,"threshold_uncertainty_score":0.008305013,"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."}}