{"id":"W2945224547","doi":"10.1175/jhm-d-18-0251.1","title":"Probabilistic Flood Forecasting Using Hydrologic Uncertainty Processor with Ensemble Weather Forecasts","year":2019,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Environment and Climate Change Canada; China Scholarship Council; Università della Calabria; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Ensemble forecasting; Probabilistic logic; Probabilistic forecasting; Computer science; Range (aeronautics); Flood myth; Flood forecasting; Data assimilation; Forecast verification; Forecast skill; Environmental science; Meteorology; Consensus forecast; Econometrics; Machine learning; Artificial intelligence; Mathematics","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":[0.0008129868,0.0002423871,0.0005234533,0.0001498545,0.0001460042,0.00001598806,0.0003454525,0.0001301925,0.0008976406],"category_scores_gemma":[0.000095815,0.0001586679,0.0001012957,0.0003079114,0.0003080699,0.0003114349,0.0002118358,0.0003227194,0.0001236845],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001309816,"about_ca_system_score_gemma":0.00002282664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004310542,"about_ca_topic_score_gemma":0.00006584203,"domain_scores_codex":[0.9982193,0.0001386522,0.0004735198,0.0003220907,0.0003006285,0.0005458141],"domain_scores_gemma":[0.99899,0.0001249705,0.0005502092,0.0002062964,0.00003618274,0.00009231515],"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":[0.001157861,0.0003465719,0.3483705,0.00009682104,0.0004898034,0.0004321922,0.001061376,0.6372548,0.007956265,0.0001056423,0.0003656292,0.002362581],"study_design_scores_gemma":[0.01846455,0.03662479,0.05083455,0.0004134469,0.002372382,0.01613316,0.001039255,0.780273,0.003632649,0.06829366,0.01893952,0.002979007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929799,0.00006770595,0.0005775664,0.000472471,0.0002474691,0.0003339482,9.452555e-7,0.00001863264,0.005301342],"genre_scores_gemma":[0.996509,0.000008996815,0.002575644,0.000388487,0.00005846616,0.00000755058,8.970322e-7,0.00002237737,0.0004286485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.297536,"threshold_uncertainty_score":0.9828535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01922093778379347,"score_gpt":0.2204410956220641,"score_spread":0.2012201578382706,"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."}}