{"id":"W2156844335","doi":"10.1029/2004gl022305","title":"A simple method to improve ensemble‐based ozone forecasts","year":2005,"lang":"en","type":"article","venue":"Geophysical Research Letters","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of Science; National Oceanic and Atmospheric Administration","keywords":"Ozone; Ensemble forecasting; Ensemble average; Mean squared error; Environmental science; Statistics; Meteorology; Ensemble learning; Mean square; Simple (philosophy); Mathematics; Computer science; Climatology; Geography; Machine learning; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001409339,0.001026591,0.0008626148,0.00160024,0.0004382467,0.0005248977,0.000857011,0.0005745676,0.002794099],"category_scores_gemma":[0.007337952,0.0005068117,0.0008979748,0.001084973,0.0001773908,0.0008413029,0.0011797,0.0009873873,0.001018268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002983069,"about_ca_system_score_gemma":0.0006436577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003727928,"about_ca_topic_score_gemma":0.006387501,"domain_scores_codex":[0.9989449,0.0002395398,0.00006172083,0.0001774123,0.0005151532,0.00006131782],"domain_scores_gemma":[0.9976854,0.0007690102,0.0001783109,0.0004158707,0.000879797,0.00007160786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001729146,0.0001401905,0.003621848,0.0001176778,0.000340413,0.0001002569,0.0001095043,0.1794471,0.02615977,0.002577209,0.009004404,0.7782088],"study_design_scores_gemma":[0.00007371762,0.0001115725,0.003388678,0.0000126398,0.00009195932,0.00006787061,0.00001157983,0.9773303,0.009297431,0.002610914,0.006960168,0.00004317728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04058335,0.0002193696,0.9536752,0.0001183192,0.0002612914,0.0001274392,0.0003253628,0.003149554,0.001540137],"genre_scores_gemma":[0.2059436,0.0001611845,0.7895756,0.00008550872,0.0002287525,0.0002812113,0.0008526073,0.0003506715,0.002520877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003727928,"threshold_uncertainty_score":0.009347141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02271086886443073,"score_gpt":0.3155255497391128,"score_spread":0.2928146808746821,"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."}}