{"id":"W1989396668","doi":"10.4296/cwrj3002159","title":"Suitability of HEC-RAS for Flood Forecasting","year":2005,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":147,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Flood myth; Flood forecasting; Floodplain; HEC-HMS; Hydrology (agriculture); Environmental science; Flow (mathematics); Computer science; Routing (electronic design automation); Flow routing; Process (computing); Hydrological modelling; Meteorology; Geology; Climatology; Geotechnical engineering; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001315586,0.0003415668,0.0004889851,0.0003218382,0.001146947,0.00009893204,0.0007045542,0.0001623178,0.0006432504],"category_scores_gemma":[0.0002362735,0.00027278,0.0002392911,0.0001957866,0.0008144816,0.0004216084,0.0001369,0.0003219261,0.00003501033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000883336,"about_ca_system_score_gemma":0.000003819662,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.08129022,"about_ca_topic_score_gemma":0.7666792,"domain_scores_codex":[0.9970123,0.0001617364,0.0006920376,0.0004833548,0.0001425292,0.001508013],"domain_scores_gemma":[0.9982668,0.0001111777,0.0002262896,0.0003663086,0.00007930433,0.0009500793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002404758,0.00012165,0.2144171,0.0003694468,0.0004727945,0.0003980799,0.7089652,0.02988276,0.001919171,0.0000127192,0.0007154022,0.04248509],"study_design_scores_gemma":[0.0005512491,0.0003214626,0.005689943,0.0001081978,0.0001032548,0.0004341306,0.0007170191,0.002254444,0.005264354,0.002315859,0.9817718,0.0004682956],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922718,0.0003470423,0.00009748555,0.002070874,0.0001440742,0.0003960059,0.00004877377,0.00003285637,0.004591084],"genre_scores_gemma":[0.9962724,0.00007071578,0.001311369,0.000454124,0.0003829902,0.00003887387,0.00001467369,0.0000475256,0.0014073],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9810564,"threshold_uncertainty_score":0.9999725,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02032711628528936,"score_gpt":0.20397129504735,"score_spread":0.1836441787620607,"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."}}