{"id":"W2748598378","doi":"10.5194/acp-17-14239-2017","title":"Spatiotemporal distribution of nitrogen dioxide within and around a large-scale wind farm – a numerical case study","year":2017,"lang":"en","type":"article","venue":"Atmospheric chemistry and physics","topic":"Wind and Air Flow Studies","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Environment and Climate Change Canada; National Natural Science Foundation of China","keywords":"Environmental science; Wind speed; Maximum sustained wind; Wind power; Turbulence kinetic energy; Roughness length; Meteorology; Atmospheric sciences; Planetary boundary layer; Wind direction; Wind profile power law; Weather Research and Forecasting Model; Renewable energy; Boundary layer; Turbine; Wind shear; Wind gradient; Turbulence; Geography; Geology; Mechanics; Physics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008357933,0.0001313753,0.0001845794,4.264868e-8,0.0004198966,0.00003748667,0.00008668702,0.00003653765,0.00004026867],"category_scores_gemma":[0.00001314568,0.0001158516,0.00003140835,0.00004179245,0.0002472896,0.0001114968,0.0001955592,0.00008608345,0.000002374991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002204436,"about_ca_system_score_gemma":0.000006068171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001028303,"about_ca_topic_score_gemma":0.00004662764,"domain_scores_codex":[0.9993346,0.00001210016,0.0001380562,0.0002447651,0.0001260148,0.0001444635],"domain_scores_gemma":[0.9995399,0.0000163498,0.000125146,0.0002393845,0.000007487614,0.00007173919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002543202,0.0004216769,0.9750426,0.00003588271,0.00004636513,0.000115596,0.002089449,0.0001055385,0.00264672,0.000004122104,0.00003762889,0.01942893],"study_design_scores_gemma":[0.005738473,0.0006103253,0.8703679,0.00009925129,0.0004602224,0.0007812384,0.02992298,0.05292202,0.03160505,0.003453005,0.002482292,0.001557254],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987714,0.00003804657,0.0005116579,0.00002806441,0.00001623534,0.0001015828,0.00002870797,0.000009860749,0.0004944604],"genre_scores_gemma":[0.9989845,0.000007079116,0.0006861467,0.000009313729,0.00007208331,0.000005202948,0.000007994197,0.00000657668,0.0002210911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1046748,"threshold_uncertainty_score":0.4724292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008084567224124106,"score_gpt":0.2289013877152251,"score_spread":0.220816820491101,"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."}}