{"id":"W2887084845","doi":"","title":"New Perspectives in Eddy Covariance Measurements Using Aircraft-Based Technology","year":2016,"lang":"en","type":"article","venue":"32nd Conf. on Agricultural and Forest Meteorology/22nd Symp. Boundary Layers and Turbulence/ Third Conf. on Atmospheric Biogeosciences (20 – 24 June, 2016)","topic":"Aerodynamics and Acoustics in Jet Flows","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Eddy covariance; Covariance; Environmental science; Computer science; Remote sensing; Meteorology; Mathematics; Geology; Geography; Statistics","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":[0.01002954,0.00168342,0.002208882,0.002633858,0.0005298523,0.005275035,0.002884634,0.004031637,0.006592726],"category_scores_gemma":[0.0142712,0.001262451,0.001026313,0.003741418,0.004052611,0.01457987,0.003382526,0.005143635,0.001894196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001368131,"about_ca_system_score_gemma":0.0008988185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002397534,"about_ca_topic_score_gemma":0.002818073,"domain_scores_codex":[0.9970413,0.001068299,0.000164855,0.0006076916,0.0009882537,0.0001295562],"domain_scores_gemma":[0.9851964,0.009175081,0.0004144989,0.002046888,0.002710036,0.0004572428],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005433111,0.0003084027,0.01383027,0.002128877,0.0005621529,0.0003022038,0.0005496939,0.02317109,0.02654763,0.3652541,0.03079429,0.536008],"study_design_scores_gemma":[0.0001833386,0.0004778192,0.01492356,0.001223215,0.0002844355,0.0009823067,0.0007654277,0.181363,0.01195328,0.5390341,0.2481392,0.0006701701],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02118712,0.1779519,0.729893,0.03442696,0.004512409,0.0001013303,0.002116647,0.0009883821,0.02882226],"genre_scores_gemma":[0.2979283,0.09454702,0.5739955,0.005848277,0.01232233,0.0003649877,0.002277782,0.0005488133,0.01216693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01002954,"threshold_uncertainty_score":0.05304193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322668473918131,"score_gpt":0.2089637894454109,"score_spread":0.1957371047062296,"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."}}