{"id":"W2320684931","doi":"10.1061/40569(2001)54","title":"Developing Runoff Hydrograph using Artificial Neural Networks","year":2001,"lang":"en","type":"article","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"U.S. Army Corps of Engineers","keywords":"Hydrograph; Surface runoff; Artificial neural network; Precipitation; Runoff model; Computer science; Flow (mathematics); Environmental science; Watershed; Hydrology (agriculture); Meteorology; Mathematics; Machine learning; Geology; Geography; Geotechnical engineering","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":[0.0002476781,0.0003919986,0.000221382,0.0006805249,0.0002019484,0.0004468405,0.0003452879,0.0003646066,0.001471251],"category_scores_gemma":[0.0009710777,0.0002571138,0.0003316047,0.0007611855,0.0001471385,0.0005080136,0.0002287294,0.0003696881,0.0002718476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008957526,"about_ca_system_score_gemma":0.0008991379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02349514,"about_ca_topic_score_gemma":0.02443173,"domain_scores_codex":[0.9998908,0.00001913997,0.00001079664,0.00002900422,0.00003776422,0.00001246016],"domain_scores_gemma":[0.999729,0.000130108,0.00003087072,0.00001036191,0.00009213069,0.000007495749],"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":[0.00001236345,0.00001470531,0.001130365,0.00002176971,0.00001008927,0.00003246187,0.00001683802,0.960677,0.001137211,0.0007137436,0.0002897857,0.03594367],"study_design_scores_gemma":[0.000001075098,0.000002946274,0.0002909614,0.000002407836,0.000001369489,0.000002763672,0.000003649585,0.9987257,0.0003708334,0.0004278788,0.000168667,0.00000181424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.108435,0.0001722765,0.8826382,0.0001897698,0.00003324203,0.000141108,0.0004543498,0.002111468,0.005824689],"genre_scores_gemma":[0.7437674,0.000301776,0.2504955,0.0000686718,0.00002473668,0.0002369899,0.0008821802,0.00008958738,0.004133197],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02349514,"threshold_uncertainty_score":0.04671675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.063358267916262,"score_gpt":0.2748341516413704,"score_spread":0.2114758837251084,"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."}}