{"id":"W2566754188","doi":"10.1175/jhm-d-16-0088.1","title":"The Use of Reanalyses and Gridded Observations as Weather Input Data for a Hydrological Model: Comparison of Performances of Simulated River Flows Based on the Density of Weather Stations","year":2016,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Environmental science; Precipitation; Climatology; Forcing (mathematics); Weather station; Meteorology; Weather Research and Forecasting Model; Watershed; Automatic weather station; Streamflow; Drainage basin; Geology; Geography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.000998988,0.00063994,0.0004928236,0.0004478987,0.0004286303,0.0008477439,0.0006663917,0.0004010473,0.0005140317],"category_scores_gemma":[0.002569166,0.0003770863,0.00064574,0.0008148875,0.0003620081,0.0005437463,0.0003168707,0.0003130005,0.00008357179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002518272,"about_ca_system_score_gemma":0.002554017,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.5132605,"about_ca_topic_score_gemma":0.4081118,"domain_scores_codex":[0.9996176,0.0001151582,0.00002632824,0.0001025102,0.00008023898,0.00005817713],"domain_scores_gemma":[0.99885,0.0005666636,0.00009830921,0.0001228763,0.0002852133,0.00007697401],"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":[0.0007426429,0.0001887188,0.1272582,0.00007613312,0.0004466572,0.00007141868,0.0001504822,0.8461751,0.006501518,0.0003606827,0.0003998895,0.01762859],"study_design_scores_gemma":[0.00009357174,0.0001555533,0.1260654,0.00001171962,0.00009656698,0.00001574212,0.0001085611,0.8678172,0.005150757,0.0001094657,0.0003349211,0.00004051735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976816,0.00008811126,0.001047554,0.00002951527,0.000006785372,0.00001076969,0.0005248377,0.0001028003,0.0005080206],"genre_scores_gemma":[0.9976041,0.00006225437,0.001505651,0.000005863154,0.000002711083,0.000008296903,0.0006507816,0.00001104621,0.0001493969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5132605,"threshold_uncertainty_score":0.979212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1326022594808869,"score_gpt":0.3111578965434086,"score_spread":0.1785556370625218,"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."}}