{"id":"W2130685946","doi":"10.1175/mwr-d-15-0232.1","title":"The Case-to-Case Variability of the Predictability of Precipitation by a Storm-Scale Ensemble Forecasting System","year":2015,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Predictability; Climatology; Forcing (mathematics); Precipitation; Quantitative precipitation forecast; Data assimilation; Environmental science; Forecast skill; Radar; Meteorology; Storm; Ensemble forecasting; Computer science; Mathematics; Statistics; Geography; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003515439,0.0001043029,0.0002926963,0.000009594799,0.0001792003,0.00001246311,0.000203405,0.00004030089,0.00007649305],"category_scores_gemma":[0.0009923502,0.00004940081,0.0001032199,0.0002694165,0.00009326069,0.00007190165,0.00002486057,0.00008445883,0.000007576452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001633191,"about_ca_system_score_gemma":0.0000399171,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001367636,"about_ca_topic_score_gemma":0.00230378,"domain_scores_codex":[0.998006,0.0008807086,0.0005369825,0.0001942766,0.0002195014,0.0001625273],"domain_scores_gemma":[0.9981416,0.0008668245,0.000236598,0.0004824387,0.0001534702,0.000119045],"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":[0.0001743335,0.0002291854,0.7574701,0.004934469,0.0001135605,0.00007478875,0.004940812,0.02415646,0.000052376,0.0004657384,0.004155184,0.2032329],"study_design_scores_gemma":[0.003188479,0.004164774,0.2870865,0.007316072,0.001959797,0.002132628,0.008793416,0.5566671,0.0002974772,0.01591449,0.1106206,0.001858702],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9708595,0.01995689,0.0003351777,0.0002466017,0.0001642961,0.001340514,0.0002534431,0.00001872976,0.006824845],"genre_scores_gemma":[0.9994281,0.00005820363,0.0003616021,0.00005839666,0.00001924601,0.00001405098,0.000008644216,0.000002540339,0.0000491961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5325106,"threshold_uncertainty_score":0.2067466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04587040256708786,"score_gpt":0.2437198384935495,"score_spread":0.1978494359264617,"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."}}