{"id":"W2809636832","doi":"10.1088/1742-6596/1037/5/052023","title":"In-blade Load Sensing on 3D Printed Wind Turbine Blades Using Trailing Edge Flaps","year":2018,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Icing and De-icing Technologies","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trailing edge; Blade (archaeology); Turbine blade; Turbine; Marine engineering; Geology; Acoustics; Mechanical engineering; Materials science; Engineering; Aerospace engineering; Structural engineering; Physics","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":[0.0001530729,0.0003174313,0.0002239829,0.0002118239,0.0001142839,0.0003169478,0.0005065299,0.0004231974,0.001192062],"category_scores_gemma":[0.0003909837,0.0002297364,0.0002440731,0.0001356462,0.000358353,0.0002964903,0.0002438686,0.000229045,0.00028055],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002076767,"about_ca_system_score_gemma":0.0001030365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003095708,"about_ca_topic_score_gemma":0.0006237577,"domain_scores_codex":[0.9997583,0.00002052087,0.00001058756,0.00005495999,0.0001327121,0.00002289673],"domain_scores_gemma":[0.9995131,0.000115971,0.0001311815,0.0001183537,0.00009171206,0.00002955231],"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":[0.00005819142,0.00003443318,0.0005078361,0.00006785137,0.000006696459,0.000352474,0.00008483646,0.002889859,0.9823518,0.0001080961,0.0002426609,0.01329524],"study_design_scores_gemma":[0.00002455232,0.000384098,0.00722135,0.00001084949,0.00001166259,0.0004401023,0.00007239723,0.02999343,0.9598214,0.0001250152,0.001855698,0.00003937123],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9505392,0.0001654825,0.04550244,0.00009218908,0.0001270062,0.00003767812,0.0002109303,0.0008527563,0.002472398],"genre_scores_gemma":[0.9726728,0.00008098836,0.0256045,0.00004546806,0.00001298494,0.00002350935,0.00005409991,0.00002498647,0.001480716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001192062,"threshold_uncertainty_score":0.003987849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02992386084711508,"score_gpt":0.2538405497236963,"score_spread":0.2239166888765812,"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."}}