{"id":"W2096562224","doi":"10.1175/2009waf2222192.1","title":"Deterministic Ensemble Forecasts Using Gene-Expression Programming*","year":2009,"lang":"en","type":"article","venue":"Weather and Forecasting","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"British Columbia Knowledge Development Fund; Natural Sciences and Engineering Research Council of Canada; BC Hydro; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"Gene expression programming; Ensemble forecasting; Computer science; Range (aeronautics); Numerical weather prediction; Genetic programming; Ensemble learning; Population; Ensemble average; Statistics; Algorithm; Meteorology; Mathematics; Artificial intelligence; Climatology; Geography; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000891746,0.0004296254,0.000631808,0.0004073272,0.0002698329,0.0006420176,0.0006627246,0.000458877,0.0008649155],"category_scores_gemma":[0.002400688,0.0003274784,0.0004974949,0.000553478,0.0003052273,0.000511197,0.0003993261,0.0007550641,0.0001674117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007661832,"about_ca_system_score_gemma":0.0007302163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009171845,"about_ca_topic_score_gemma":0.007587251,"domain_scores_codex":[0.9997203,0.0001100542,0.000009733848,0.00005380627,0.00007667751,0.00002946898],"domain_scores_gemma":[0.9988841,0.0007419805,0.00009601859,0.00005902454,0.0001944396,0.00002443809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001089554,0.000007776733,0.0005368873,0.000006072199,0.00001262425,0.000007106313,0.00000669839,0.9889526,0.0002960222,0.001054693,0.0001663878,0.008942164],"study_design_scores_gemma":[9.483903e-7,0.000001806474,0.00004731398,5.776543e-7,8.559703e-7,5.490161e-7,8.350907e-7,0.99939,0.00008533239,0.0004144245,0.0000565627,8.600002e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1289536,0.0002116934,0.8653557,0.0003583721,0.00005939186,0.00005279037,0.0003313539,0.0007707498,0.003906397],"genre_scores_gemma":[0.8254383,0.000112015,0.1721276,0.00007683473,0.00003985567,0.0001139908,0.0004432544,0.0001115657,0.001536604],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009171845,"threshold_uncertainty_score":0.01823694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06700784248881043,"score_gpt":0.2567118239727634,"score_spread":0.189703981483953,"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."}}