{"id":"W2887646441","doi":"","title":"Air quality ensemble forecasting: an experimental ensemble design and a Kalman filter correction","year":2005,"lang":"en","type":"article","venue":"Atmospheric Sciences and Air Quality Conference","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ensemble Kalman filter; Kalman filter; Computer science; Ensemble learning; Ensemble forecasting; Quality (philosophy); Artificial intelligence; Extended Kalman filter; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002909689,0.0003281375,0.000358228,0.00000866555,0.00107672,0.000188531,0.0003366162,0.0001232985,0.0003105709],"category_scores_gemma":[0.0001851846,0.0002910732,0.00005725016,0.0003644282,0.001097867,0.001146796,0.000291687,0.0002093495,0.00003302023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001206973,"about_ca_system_score_gemma":0.00004699344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002708112,"about_ca_topic_score_gemma":0.000235597,"domain_scores_codex":[0.996645,0.000544302,0.0005764234,0.0009839867,0.000628909,0.0006213892],"domain_scores_gemma":[0.9986075,0.0004192727,0.0002906054,0.0003111859,0.0000328334,0.0003386447],"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":[0.0002244033,0.0006904805,0.08975485,0.00005929508,0.00002712252,0.000008528349,0.02154151,0.02073228,0.053161,0.003121475,0.001245834,0.8094332],"study_design_scores_gemma":[0.001351643,0.002087203,0.1381654,0.0001522695,0.00003597523,0.0001393689,0.0207723,0.7853713,0.043271,0.003154099,0.003627812,0.001871601],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9600504,0.0001374353,0.0349916,0.0005268609,0.0003044736,0.0002732535,0.00000205814,0.0001446946,0.003569216],"genre_scores_gemma":[0.9407882,0.0000238481,0.05752651,0.0004839846,0.0001183117,0.00002918121,0.000002433987,0.00001416404,0.001013347],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8075616,"threshold_uncertainty_score":0.9999542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1142995303241717,"score_gpt":0.3245480981929631,"score_spread":0.2102485678687914,"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."}}