{"id":"W1990849997","doi":"10.1175/jtech-d-12-00186.1","title":"Lagrangian Detection of Wind Shear for Landing Aircraft","year":2013,"lang":"en","type":"article","venue":"Journal of Atmospheric and Oceanic Technology","topic":"Fluid Dynamics and Turbulent Flows","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"U.S. Air Force; McGill University","keywords":"Turbulence; Lyapunov exponent; Lidar; Lagrangian; Wind shear; Upwelling; Meteorology; Geology; Intersection (aeronautics); Remote sensing; Computer science; Geodesy; Aerospace engineering; Physics; Nonlinear system; Wind speed; Mathematics; Applied mathematics; 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":[0.000320081,0.0002023574,0.0001258376,0.001247765,0.0001289046,0.0003745078,0.0001145555,0.0001043635,0.0006512834],"category_scores_gemma":[0.001301216,0.00007999061,0.00008767656,0.0003455626,0.0001105123,0.000239524,0.000216529,0.0001216533,0.0001743097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001989337,"about_ca_system_score_gemma":0.0001964297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004421994,"about_ca_topic_score_gemma":0.008178185,"domain_scores_codex":[0.9998869,0.00002191722,0.000009362064,0.00002285386,0.00004155067,0.00001737463],"domain_scores_gemma":[0.9992268,0.0001765979,0.0002800274,0.00004035901,0.0001779638,0.0000981692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003258961,0.0001158643,0.908322,0.00005164585,0.00004195607,0.0002057497,0.0002112776,0.008623578,0.0347165,0.0001174181,0.0004259357,0.04684211],"study_design_scores_gemma":[0.00001274504,0.000292836,0.9104549,0.00001736172,0.00002112505,0.00009542775,0.0002824787,0.07909523,0.009382549,0.00006229711,0.0002632396,0.00001982905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975147,0.00002037196,0.001719468,0.00001188139,0.000004922226,0.000008631027,0.0001618984,0.00005088702,0.000507226],"genre_scores_gemma":[0.998899,0.000009697676,0.0008585921,0.000002619056,0.000002207202,0.000001867611,0.0001387086,0.000001707613,0.00008556379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004421994,"threshold_uncertainty_score":0.00879246,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003108591846757885,"score_gpt":0.1770529780580505,"score_spread":0.1739443862112927,"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."}}