{"id":"W2044061235","doi":"10.1111/j.1600-0889.2007.00332.x","title":"A Kalman-filter bias correction method applied to deterministic, ensemble averaged and probabilistic forecasts of surface ozone","year":2008,"lang":"en","type":"article","venue":"Tellus B","topic":"Atmospheric chemistry and aerosols","field":"Earth and Planetary Sciences","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Office of Science; National Oceanic and Atmospheric Administration","keywords":"Ozone; Ensemble Kalman filter; Probabilistic logic; Environmental science; Kalman filter; Mean squared error; Statistics; Ensemble forecasting; Meteorology; Mathematics; Atmospheric sciences; Extended Kalman filter; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001722115,0.0001316248,0.0002253583,0.000005800066,0.0001268858,0.00001261359,0.00008925868,0.00006131487,0.0005726716],"category_scores_gemma":[0.00009223563,0.0001131523,0.00003091021,0.0001603561,0.00007768062,0.00004526711,0.00001544877,0.00009156275,0.00007603658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000435725,"about_ca_system_score_gemma":0.00003488288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002691253,"about_ca_topic_score_gemma":0.0001319183,"domain_scores_codex":[0.9991375,0.00003612016,0.0002138836,0.0002548604,0.0001511598,0.000206492],"domain_scores_gemma":[0.9992883,0.0003142738,0.00008037877,0.0001613891,0.00002691446,0.0001287413],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002260178,0.0003645619,0.1713546,0.0007550456,0.0001728938,0.0003096559,0.01321421,0.1973136,0.1617105,0.00007497437,0.01063614,0.4418336],"study_design_scores_gemma":[0.003151081,0.001804033,0.3853613,0.0002093027,0.0001986136,0.001941525,0.0008327842,0.2661683,0.3265776,0.001814181,0.009796229,0.00214503],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785623,0.0000540549,0.009009262,0.00001932646,0.0001596226,0.0002398209,0.00001529654,0.00003528369,0.01190506],"genre_scores_gemma":[0.9815742,0.00001293576,0.01590441,0.00006525683,0.00004909147,0.000001626176,0.00001864037,0.000004319058,0.002369501],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4396886,"threshold_uncertainty_score":0.6270353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03806206941086308,"score_gpt":0.2366314231559469,"score_spread":0.1985693537450838,"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."}}