{"id":"W2136054984","doi":"10.1061/(asce)1084-0699(2000)5:4(371)","title":"Improving Forecasts of Nile Flood Using SST Inputs in TFN Model","year":2000,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Climate variability and models","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Flood myth; Climatology; Environmental science; Flood forecasting; Sea surface temperature; Surface runoff; Streamflow; Meteorology; Drainage basin; Geology; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002944696,0.0003887665,0.0002714265,0.0001999707,0.0002066523,0.0003884718,0.0002841593,0.0003836981,0.0004927264],"category_scores_gemma":[0.001380637,0.0002318205,0.0002924435,0.0001645083,0.0001707161,0.000464215,0.0002444856,0.0003520784,0.0001138145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006202411,"about_ca_system_score_gemma":0.000615691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03072796,"about_ca_topic_score_gemma":0.01766324,"domain_scores_codex":[0.9999157,0.00002410072,0.000006484382,0.00002359169,0.0000150526,0.00001509998],"domain_scores_gemma":[0.9998104,0.00009122599,0.00002651753,0.0000104931,0.0000494175,0.00001203432],"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":[0.00001786856,0.000005879334,0.002255926,0.000004139561,0.000005789653,0.00001325076,0.000007422047,0.9938056,0.0004685189,0.00009421966,0.0000578647,0.00326338],"study_design_scores_gemma":[0.000002259885,0.000004732115,0.0004775593,6.340709e-7,0.000001838897,0.000001588897,0.000001822777,0.9992315,0.0001871949,0.00005736111,0.00003182759,0.000001610102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9441218,0.00008754009,0.05299104,0.0001821936,0.00003254561,0.00001644668,0.0002830935,0.0004514874,0.00183386],"genre_scores_gemma":[0.994609,0.00003584708,0.004825575,0.000008932249,0.000007016976,0.000009115209,0.0001199366,0.00001038116,0.0003741527],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03072796,"threshold_uncertainty_score":0.06109822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01391675566488775,"score_gpt":0.2078911230209383,"score_spread":0.1939743673560506,"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."}}