{"id":"W3130511535","doi":"10.1007/s11069-021-04583-2","title":"Flood risk mapping using uncertainty propagation analysis on a peak discharge: case study of the Mille Iles River in Quebec","year":2021,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Police Service; École de Technologie Supérieure","funders":"Hydro-Québec; Compute Canada","keywords":"Flood myth; Riemann solver; Hydrology (agriculture); Shallow water equations; Natural hazard; Environmental science; Latin hypercube sampling; Finite volume method; Flooding (psychology); Geology; Meteorology; Statistics; Geography; Monte Carlo method; Mathematics; Geotechnical engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002641795,0.0001233034,0.0001839262,0.00008804924,0.0001790378,0.00002912184,0.0001464983,0.00003523948,0.0001012449],"category_scores_gemma":[0.00003933451,0.00008039079,0.0001130112,0.001245068,0.00006246186,0.0001508948,0.0002830178,0.0002049541,0.000005800222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003120156,"about_ca_system_score_gemma":0.00002301428,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1393489,"about_ca_topic_score_gemma":0.3709731,"domain_scores_codex":[0.9986012,0.0002431851,0.0002278021,0.0003103144,0.0004482102,0.0001693122],"domain_scores_gemma":[0.9994782,0.00002919563,0.0001376866,0.0003116462,0.00001754491,0.00002577978],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003545162,0.00118117,0.662353,0.00002487039,0.0005025186,0.0004213651,0.01164465,0.2877178,0.004044931,0.00006333765,0.0001813241,0.03182952],"study_design_scores_gemma":[0.00102607,0.00006343801,0.8147547,0.00003107526,0.0005409914,0.000009787715,0.02093161,0.1613858,0.0007877647,0.00004871739,0.0001942026,0.0002258536],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989708,0.00005984584,0.00004435426,0.0000713472,0.0001273534,0.0004075616,0.000007322182,0.00001039715,0.0003010196],"genre_scores_gemma":[0.9991599,0.000009160965,0.0003169941,0.00002991771,0.00001527914,0.00001175317,0.000008112663,0.00000599336,0.0004428797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2316241,"threshold_uncertainty_score":0.8663822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01077295132718211,"score_gpt":0.2588599175622152,"score_spread":0.248086966235033,"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."}}