{"id":"W2101037287","doi":"10.1175/2007waf2006107.1","title":"Hydrometeorological Accuracy Enhancement via Postprocessing of Numerical Weather Forecasts in Complex Terrain","year":2008,"lang":"en","type":"article","venue":"Weather and Forecasting","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Hydro (Canada)","funders":"BC Hydro","keywords":"Quantitative precipitation forecast; Hydrometeorology; Terrain; Precipitation; Forecast verification; Statistics; Forecast skill; Computer science; Environmental science; Meteorology; Mathematics; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003204998,0.0001591036,0.0003244726,0.00009640811,0.0001784985,0.00001481565,0.0001227689,0.00007605719,0.002178167],"category_scores_gemma":[0.000152223,0.0001135438,0.00005640114,0.0002510342,0.000161153,0.00014395,0.00002397644,0.0001425882,0.00001391324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004726779,"about_ca_system_score_gemma":0.00001340821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003139132,"about_ca_topic_score_gemma":0.00006431722,"domain_scores_codex":[0.9986558,0.00009330161,0.0004315555,0.0002853189,0.0001907838,0.0003432209],"domain_scores_gemma":[0.9991956,0.0004354995,0.0001355717,0.0001035746,0.00002504271,0.0001046897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000105171,0.00008539869,0.7752106,0.00002540277,0.00001629414,0.00002340095,0.001637148,0.003034233,0.001494592,0.00003758507,0.00001120218,0.218319],"study_design_scores_gemma":[0.0005592456,0.0004531609,0.7449204,0.00001983665,0.00000748037,0.00006159123,0.00009136852,0.2503543,0.00007749947,0.003049488,0.0002054393,0.0002002062],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797789,0.000292848,0.005732007,0.00007733403,0.00003126101,0.0001586968,0.00001014051,0.00001984995,0.01389893],"genre_scores_gemma":[0.9946952,0.00001820882,0.004970594,0.0001649197,0.00004455245,0.000002393629,0.00003085617,0.000004556024,0.00006874609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2473201,"threshold_uncertainty_score":0.998734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07268140747512974,"score_gpt":0.2565000533654377,"score_spread":0.183818645890308,"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."}}