{"id":"W3049376599","doi":"10.1175/jhm-d-20-0033.1","title":"Evaluation of Radar Quantitative Precipitation Estimates (QPEs) as an Input of Hydrological Models for Hydrometeorological Applications","year":2020,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Environment and Climate Change Canada; McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; National Oceanic and Atmospheric Administration; Environment and Climate Change Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Hydrometeorology; Radar; Rain gauge; Environmental science; Weather radar; Precipitation; Meteorology; Remote sensing; Geology; Computer science; Geography; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.00399553,0.0001619038,0.0006553184,0.0003364723,0.00007659589,0.00001426154,0.000380096,0.0001568319,0.0006354369],"category_scores_gemma":[0.001627152,0.0001253096,0.0002477824,0.0005091038,0.0002025977,0.000550154,0.00001172977,0.0001714749,0.000009519975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001099473,"about_ca_system_score_gemma":0.0001724817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003403361,"about_ca_topic_score_gemma":0.00004068874,"domain_scores_codex":[0.9968737,0.0006680073,0.001048899,0.0002582605,0.0009422973,0.0002087931],"domain_scores_gemma":[0.9963918,0.0007865189,0.00125541,0.0001381212,0.001246443,0.0001817336],"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":[0.002679797,0.0005899493,0.03288489,0.0001488198,0.001313149,0.000004754011,0.003035136,0.8417242,0.04913192,0.004908663,0.000131464,0.0634473],"study_design_scores_gemma":[0.001584598,0.008470936,0.01922114,0.00001210842,0.00097253,0.00001834019,0.0002166416,0.8059162,0.002899251,0.1604732,0.00005937107,0.0001556465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9186412,0.0009301167,0.07858223,0.0007937509,0.00007469663,0.0004562462,0.0000590978,0.00001164078,0.0004509993],"genre_scores_gemma":[0.9610084,0.0000592029,0.03859458,0.0001617145,0.00007008458,0.00001039095,0.00008897864,0.000004847388,0.000001742105],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1555645,"threshold_uncertainty_score":0.6957588,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1133773568663827,"score_gpt":0.3262748613717217,"score_spread":0.212897504505339,"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."}}