{"id":"W2000703932","doi":"10.2495/geo060281","title":"Efficient watershed modeling using a multi-site weather generator for meteorological data","year":2006,"lang":"en","type":"article","venue":"WIT transactions on ecology and the environment","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Precipitation; Watershed; Environmental science; Inflow; Meteorology; Autocorrelation; Automatic weather station; Hydrograph; Weather station; Surface runoff; Geography; Computer science; Mathematics","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":[0.0006407886,0.0003827653,0.0005224994,0.0004009026,0.000321062,0.0005185631,0.001101103,0.0005530216,0.001700677],"category_scores_gemma":[0.001428252,0.0003706279,0.0003977914,0.0006069277,0.0002209204,0.0007410252,0.0004544374,0.0005565672,0.0003458679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006680029,"about_ca_system_score_gemma":0.0008575714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01385952,"about_ca_topic_score_gemma":0.01700502,"domain_scores_codex":[0.9997972,0.00007072929,0.00001304769,0.0000427156,0.00005797959,0.00001849158],"domain_scores_gemma":[0.999549,0.0002449214,0.00003660595,0.00006162019,0.00008202781,0.00002591934],"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":[0.00002494666,0.00002646684,0.0008705002,0.0000115597,0.00001189589,0.00005500198,0.00002197975,0.9789889,0.001115159,0.001348378,0.0003799461,0.01714529],"study_design_scores_gemma":[0.000003370566,0.000002587607,0.0000612142,3.31678e-7,8.818081e-7,0.000003520955,0.000001038104,0.9995172,0.0001485133,0.0001642554,0.00009575124,0.000001323502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04338928,0.00003586461,0.9526812,0.00006229926,0.00001372921,0.00009909528,0.0002517416,0.00218019,0.001286542],"genre_scores_gemma":[0.5661525,0.00007867972,0.4304234,0.00002294432,0.00001925359,0.0003508195,0.0005446828,0.0002119979,0.002195761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01385952,"threshold_uncertainty_score":0.02755767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02970853224416307,"score_gpt":0.2266755617485349,"score_spread":0.1969670295043719,"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."}}