{"id":"W2001991385","doi":"10.1016/j.jhydrol.2011.10.039","title":"Daily streamflow forecasting by machine learning methods with weather and climate inputs","year":2011,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":301,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Weather Service; Canadian Foundation for Climate and Atmospheric Sciences","keywords":"North Atlantic oscillation; Climatology; Streamflow; Teleconnection; Environmental science; Atlantic multidecadal oscillation; Lead time; Arctic oscillation; Watershed; Pacific decadal oscillation; Meteorology; Sea surface temperature; Computer science; Machine learning; El Niño Southern Oscillation; Geography; Geology; Drainage basin","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":[0.0008416246,0.0001159101,0.0002316661,0.00005023561,0.0001511648,0.000005819649,0.0001188331,0.0000563439,0.0004521857],"category_scores_gemma":[0.00003129601,0.00007642704,0.00002935739,0.00006111614,0.0002475189,0.0001624854,0.0001807666,0.0002629147,0.00001319416],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001612705,"about_ca_system_score_gemma":0.000001766001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004754859,"about_ca_topic_score_gemma":0.00004571374,"domain_scores_codex":[0.9990931,0.0001963937,0.0002213914,0.0001425826,0.00008514428,0.0002613761],"domain_scores_gemma":[0.9995552,0.00006583898,0.0002461924,0.00006584741,0.000006318571,0.000060623],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003453033,0.0000731723,0.9825094,0.000008590091,0.000160986,0.0001431085,0.002054044,0.0009251604,0.00196435,0.00002070878,0.0004372494,0.01135798],"study_design_scores_gemma":[0.02245057,0.04103868,0.4809805,0.0002535903,0.002951417,0.01177717,0.001872213,0.1316623,0.02636138,0.01668208,0.2605,0.003470188],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850282,0.0002469726,0.004082483,0.0005156716,0.000065135,0.00004982906,8.19358e-7,0.00001105482,0.009999853],"genre_scores_gemma":[0.9809985,0.000194847,0.01818603,0.0004029172,0.00001840819,0.000001667188,6.05853e-7,0.00001072865,0.0001863161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5015289,"threshold_uncertainty_score":0.4951116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01935362725891015,"score_gpt":0.2402349520282999,"score_spread":0.2208813247693898,"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."}}