{"id":"W1497229066","doi":"10.1139/l09-173","title":"Development and application of efficient methods for the forward propagation of epistemic uncertainty and sensitivity analysis within complex broad-scale flood risk system modelsThis article is one of a selection of papers published in this Special Issue on Hydrotechnical Engineering.","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Flood myth; Risk analysis (engineering); Scale (ratio); Sensitivity (control systems); Computer science; Flood risk management; Uncertainty analysis; Selection (genetic algorithm); Operations research; Risk management; Uncertainty quantification; Management science; Environmental resource management; Environmental science; Engineering; Business; Simulation; Machine learning; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001595828,0.00008694734,0.0002841458,0.0002418724,0.00003393136,0.000009300177,0.00006910237,0.00005200181,0.00005547046],"category_scores_gemma":[0.00008588572,0.00007301755,0.0000552857,0.000420251,0.0000535149,0.000063962,0.00001873361,0.0001448759,5.44261e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001556102,"about_ca_system_score_gemma":0.0000434232,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003367964,"about_ca_topic_score_gemma":0.0527193,"domain_scores_codex":[0.9990734,0.00003292582,0.0004787429,0.0001188472,0.0001780133,0.0001180777],"domain_scores_gemma":[0.999319,0.0001038598,0.0003350558,0.0001080672,0.00004485278,0.0000892237],"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":[0.00002113211,0.00002514765,0.002875515,0.0001192166,0.0001035896,2.269902e-7,0.0009446635,0.8944205,0.09614218,0.0001569045,0.000006805611,0.005184107],"study_design_scores_gemma":[0.0002652341,0.00005770979,0.03596731,0.00004950051,0.0001543077,0.000002941743,0.00009947684,0.9232122,0.03998225,0.000009139305,0.000137054,0.00006286935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7438868,0.00001301015,0.2555955,0.00007212388,0.00004524847,0.0003070248,0.000006988542,0.00000306619,0.00007021742],"genre_scores_gemma":[0.9684316,0.000003801777,0.03151631,0.000002320674,0.00002604872,0.000009756332,0.000001219125,0.000006555991,0.000002386633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2245448,"threshold_uncertainty_score":0.9645661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005635214118184171,"score_gpt":0.2146146241788416,"score_spread":0.2089794100606574,"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."}}