{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002098044,0.0005549597,0.0002909209,0.0005572955,0.0001731872,0.0006450185,0.0003821797,0.0002459644,0.0004901939],"category_scores_gemma":[0.00463014,0.0001939518,0.0002394607,0.000446893,0.0001632578,0.0004340976,0.0003616695,0.0003002429,0.000117847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007941838,"about_ca_system_score_gemma":0.0006994061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02636518,"about_ca_topic_score_gemma":0.02116217,"domain_scores_codex":[0.9994161,0.0002155739,0.0000462611,0.00008558213,0.0002054638,0.00003095892],"domain_scores_gemma":[0.9984903,0.000491437,0.000172495,0.0001552521,0.0006507795,0.00003971447],"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":[0.0004690244,0.0003443351,0.2822393,0.0001708248,0.0002836087,0.0001105856,0.0001137091,0.5991896,0.02814371,0.0007306121,0.001613581,0.08659101],"study_design_scores_gemma":[0.00004321289,0.0001091495,0.06493301,0.00001256176,0.00003550942,0.00001349736,0.00004361324,0.9226922,0.01133471,0.0001279464,0.0006402492,0.00001437903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9520037,0.0001310626,0.04326131,0.0001021236,0.0000262034,0.0001157422,0.001563249,0.001043705,0.001752874],"genre_scores_gemma":[0.9869397,0.000037597,0.01213558,0.00001424349,0.000007101953,0.00002902562,0.0006301482,0.00002580564,0.000180831],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02636518,"threshold_uncertainty_score":0.05242348,"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."}}