{"id":"W2048561665","doi":"10.1175/mwr-d-11-00301.1","title":"Intercomparison of Global Model Precipitation Forecast Skill in 2010/11 Using the SEEPS Score","year":2012,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Climate variability and models","field":"Environmental Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"National Oceanic and Atmospheric Administration","keywords":"Precipitation; Climatology; Forecast skill; Quantitative precipitation forecast; Environmental science; Numerical weather prediction; Meteorology; Drizzle; Tropics; Atmospheric sciences; Geography; Geology; Ecology","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":[],"consensus_categories":[],"category_scores_codex":[0.0008496544,0.0001238865,0.0002579548,0.000007935989,0.00003762823,0.000007610186,0.0002118642,0.00004682117,0.0002904311],"category_scores_gemma":[0.00004412845,0.00008309026,0.00009171402,0.0002040521,0.00008743834,0.0002989105,0.0001428235,0.00007661249,0.00003652604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002247436,"about_ca_system_score_gemma":0.000007958451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00107956,"about_ca_topic_score_gemma":0.001302785,"domain_scores_codex":[0.9989132,0.000122815,0.0003692034,0.0001684198,0.0001849409,0.0002414464],"domain_scores_gemma":[0.9994467,0.00003187738,0.0001344735,0.0003271678,0.000007804452,0.0000519452],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002096165,0.000622605,0.8607162,0.0007937857,0.00001557171,4.374337e-7,0.004766874,0.1073033,0.0003833226,0.0009378281,0.001573749,0.02286531],"study_design_scores_gemma":[0.0005549757,0.00006975759,0.1497777,0.004451482,0.0002315058,0.000008623352,0.0002927045,0.8314782,0.0001240647,0.005040192,0.007424096,0.0005467178],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771591,0.01551466,0.002744223,0.000294313,0.0001018638,0.0007431171,0.00002578582,0.00001169165,0.003405265],"genre_scores_gemma":[0.9961553,0.001120042,0.002353951,0.0002698873,0.00001389957,0.00003079065,0.000004834866,0.000009298864,0.0000419988],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7241749,"threshold_uncertainty_score":0.3388323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07556309289459483,"score_gpt":0.3109921675346322,"score_spread":0.2354290746400373,"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."}}