{"id":"W3208641363","doi":"10.1175/jhm-d-21-0111.1","title":"Hydrologic Model Sensitivity to Temporal Aggregation of Meteorological Forcing Data: A Case Study for the Contiguous United States","year":2021,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canmore Museum and Geoscience Centre; University of Saskatchewan","funders":"National Science Foundation","keywords":"Forcing (mathematics); Environmental science; Sensitivity (control systems); Climatology; Radiative forcing; Hydrological modelling; Shortwave radiation; Longwave; Downscaling; Wind speed; Precipitation; Meteorology; Geography; Radiation; Aerosol; Geology; Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0033643,0.0001834101,0.0005610402,0.0001545482,0.0002727784,0.0000156747,0.0003670156,0.00009987399,0.00005290621],"category_scores_gemma":[0.0007065596,0.0001164068,0.0001124868,0.0004093948,0.0002945956,0.0002310539,0.0008169502,0.0002503957,0.000004953372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004868174,"about_ca_system_score_gemma":0.00001891263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004899033,"about_ca_topic_score_gemma":0.001272303,"domain_scores_codex":[0.9978231,0.0005824942,0.000650457,0.0003515583,0.000245788,0.000346636],"domain_scores_gemma":[0.997916,0.000965365,0.0004756014,0.0004693622,0.0000893299,0.00008429568],"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":[0.0008898347,0.0009050228,0.09108986,0.00001902949,0.0009441267,0.00710739,0.003016919,0.8877984,0.003304701,0.00005331205,0.00287518,0.001996217],"study_design_scores_gemma":[0.00352672,0.006183643,0.007109844,0.00001175053,0.001239526,0.007820616,0.005248657,0.9595684,0.001072139,0.005110308,0.002749477,0.0003589437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.956228,0.00009812669,0.03901275,0.003905312,0.0001926877,0.0004873318,0.00002822824,0.00001138466,0.00003617783],"genre_scores_gemma":[0.9946058,0.00004440872,0.003824448,0.001374925,0.00003762739,0.00001621241,0.0000176844,0.00001060254,0.00006829409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08398002,"threshold_uncertainty_score":0.4746931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05528582049824591,"score_gpt":0.3024126438456902,"score_spread":0.2471268233474443,"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."}}