{"id":"W2099202453","doi":"10.1080/02626667.2014.944526","title":"Statistical seasonal rainfall and streamflow forecasting for the Sirba watershed, West Africa, using sea-surface temperatures","year":2014,"lang":"en","type":"article","venue":"Hydrological Sciences Journal","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Agriculture","keywords":"Streamflow; Precipitation; Watershed; Hydrograph; Environmental science; Climatology; Surface runoff; Sea surface temperature; Lag; Linear regression; Water year; Hydrology (agriculture); Meteorology; Geography; Mathematics; Geology; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001799032,0.0002908006,0.0002007054,0.0006331535,0.0001665076,0.0005339445,0.0002683925,0.0001992773,0.0005024421],"category_scores_gemma":[0.007523146,0.0001913337,0.0001565802,0.001175144,0.0001605593,0.0004144127,0.0001897357,0.0002487122,0.0001473937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003901357,"about_ca_system_score_gemma":0.0008706702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02174458,"about_ca_topic_score_gemma":0.02951794,"domain_scores_codex":[0.9997502,0.0001224881,0.00002256385,0.00004484024,0.00004444613,0.00001548108],"domain_scores_gemma":[0.998494,0.000917089,0.0001927046,0.00003744409,0.000302616,0.00005610719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00073702,0.0001573523,0.5423617,0.0001949682,0.0001722741,0.0009135727,0.0005683703,0.2138673,0.009816149,0.001121492,0.007422727,0.222667],"study_design_scores_gemma":[0.00008430811,0.0002825529,0.2506041,0.00004609933,0.00006546364,0.0001264099,0.0005726118,0.7378479,0.00367394,0.0008087332,0.00585265,0.00003525657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878022,0.0004260455,0.008685863,0.0008466484,0.00008233128,0.00006124967,0.001103037,0.00009655004,0.0008960817],"genre_scores_gemma":[0.9817259,0.0008706392,0.01525864,0.00003111632,0.0001052388,0.00004026052,0.0008274263,0.00002351809,0.001117258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02174458,"threshold_uncertainty_score":0.04323602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04406162686967166,"score_gpt":0.2571869135532758,"score_spread":0.2131252866836042,"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."}}