{"id":"W3007323833","doi":"10.1080/07011784.2020.1729241","title":"Impact of the spatial density of weather stations on the performance of distributed and lumped hydrological models","year":2020,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Watershed; Environmental science; Precipitation; Distributed element model; Grid; Streamflow; Meteorology; Climate model; Hydrology (agriculture); Climate change; Geography; Computer science; Drainage basin; Geology; Cartography; Geodesy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.008274055,0.001231484,0.001027828,0.0008002609,0.0006596668,0.001852159,0.002527875,0.001015889,0.0008209164],"category_scores_gemma":[0.02116351,0.0008435819,0.00103952,0.000864488,0.0008096136,0.003031928,0.001696001,0.001075141,0.0002197961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001742362,"about_ca_system_score_gemma":0.001818822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08059645,"about_ca_topic_score_gemma":0.0463748,"domain_scores_codex":[0.9970093,0.001255645,0.0002628842,0.0008717669,0.0003903434,0.0002100622],"domain_scores_gemma":[0.984861,0.009690148,0.0009647969,0.00243159,0.001596274,0.0004561246],"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":[0.0003401942,0.0001889784,0.05421351,0.00007007475,0.0003457644,0.00007175,0.00009798275,0.9266672,0.001380765,0.0005855953,0.0003768932,0.0156613],"study_design_scores_gemma":[0.00006846354,0.0001014258,0.01211326,0.00002960896,0.00007163711,0.00002735071,0.0001021007,0.9853243,0.001355916,0.0004677333,0.0003077067,0.00003054815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9778961,0.000464769,0.01829771,0.0003360267,0.00006389918,0.00006790717,0.0007610419,0.0006581981,0.001454405],"genre_scores_gemma":[0.9932045,0.00008590606,0.005702454,0.00004668094,0.00001027791,0.00002272339,0.0006583912,0.00003687818,0.0002322295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08059645,"threshold_uncertainty_score":0.1602547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01720139918472008,"score_gpt":0.1928914147823245,"score_spread":0.1756900155976045,"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."}}