{"id":"W6904747124","doi":"10.14288/1.0041845","title":"Hydrometeorological Accuracy Enhancement via Postprocessing of Numerical Weather Forecasts in Complex Terrain.","year":2011,"lang":"en","type":"article","venue":"Open Collections","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Hydrometeorology; Quantitative precipitation forecast; Terrain; Precipitation; Numerical weather prediction; Model output statistics; Approximation error; Weather forecasting","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":[0.0005117955,0.0003917557,0.0002364895,0.0005746761,0.0001971754,0.0006550521,0.0003582712,0.0001687417,0.0008883156],"category_scores_gemma":[0.00338949,0.0001763193,0.0002897456,0.0005211932,0.0001443299,0.0006578226,0.000364234,0.0004014483,0.000289022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003180396,"about_ca_system_score_gemma":0.000556639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01224732,"about_ca_topic_score_gemma":0.02141705,"domain_scores_codex":[0.9998312,0.00002711763,0.00001625552,0.00003681057,0.00007377018,0.00001490991],"domain_scores_gemma":[0.9988874,0.0004240116,0.0001815714,0.0001923271,0.0002874539,0.00002726319],"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":[0.0004094056,0.0001652222,0.05557172,0.0001639495,0.0001239487,0.0001862132,0.0003119594,0.2463115,0.06504099,0.002358719,0.004011992,0.6253445],"study_design_scores_gemma":[0.00002803622,0.00008151636,0.04296219,0.00001326089,0.00003339355,0.00005944943,0.0000723355,0.917211,0.03416398,0.001647894,0.003700619,0.00002635272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5496662,0.0003106423,0.4414811,0.0003392379,0.0001670999,0.0001261072,0.001221213,0.0035379,0.003150498],"genre_scores_gemma":[0.7777408,0.0001352344,0.2196987,0.00004314987,0.00004225237,0.00005016454,0.001154831,0.0001664356,0.0009684609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01224732,"threshold_uncertainty_score":0.02435207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08247994078260983,"score_gpt":0.2782103914257354,"score_spread":0.1957304506431256,"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."}}