{"id":"W4244794792","doi":"10.32920/ryerson.14653290","title":"Wind Gust Measuring at Low Altitude Using An Unmanned Aerial System","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Aerospace and Aviation Technology","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Molson Foundation; Ontario Centres of Excellence","keywords":"Environmental science; Remote sensing; Clear-air turbulence; GNSS applications; Altitude (triangle); Meteorology; Planetary boundary layer; Satellite; Wind speed; Inertial measurement unit; Turbulence; Aerospace engineering; Geology; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0000873053,0.0002312316,0.000193683,0.0003773774,0.0001630414,0.000167461,0.0001308486,0.0001438858,0.000720218],"category_scores_gemma":[0.0001511731,0.00007409503,0.0001047309,0.0002600776,0.0001278594,0.0002459082,0.0002069605,0.0001532044,0.0001327605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001071995,"about_ca_system_score_gemma":0.0001142794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001410736,"about_ca_topic_score_gemma":0.002582873,"domain_scores_codex":[0.999899,0.00001179075,0.000004003648,0.00002266686,0.00004887471,0.00001353051],"domain_scores_gemma":[0.999893,0.00002038273,0.0000174009,0.0000170727,0.00003873089,0.00001342527],"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":[0.0003829956,0.0001701652,0.04389966,0.0001968041,0.000044863,0.0004287257,0.0004290404,0.01766226,0.8391575,0.000454054,0.00075136,0.09642264],"study_design_scores_gemma":[0.0001055523,0.002001593,0.3684613,0.00004416554,0.0001162041,0.0005331581,0.0006761962,0.2629071,0.3599452,0.0007286934,0.004392087,0.00008879615],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975462,0.00007266854,0.02261984,0.00001768544,0.00001639202,0.00003347578,0.0002775977,0.0002805157,0.001219967],"genre_scores_gemma":[0.9868607,0.00004984491,0.01230461,0.00001283411,0.000004003369,0.00001551427,0.0002361515,0.00001054102,0.0005059112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001410736,"threshold_uncertainty_score":0.002804995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02715333672091712,"score_gpt":0.2258816506652841,"score_spread":0.198728313944367,"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."}}