{"id":"W4376959668","doi":"10.1111/jfr3.12921","title":"A probabilistic approach to levee reliability based on sliding, backward erosion and overflowing mechanisms: Application to an inspired Canadian case study","year":2023,"lang":"en","type":"article","venue":"Journal of Flood Risk Management","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; York University; University of Toronto","funders":"Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement; York University","keywords":"Levee; Monte Carlo method; Probabilistic logic; Fragility; Flood myth; Erosion; Geology; Geotechnical engineering; Environmental science; Computer science; Mathematics; Statistics; Geomorphology; 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":[0.002652864,0.0002579742,0.0002863753,0.0005292388,0.0003890745,0.0001382121,0.0003871869,0.00004884591,0.00002875221],"category_scores_gemma":[0.00005166983,0.0002218959,0.00007449043,0.0009338182,0.00001900048,0.0002717226,0.0003310335,0.000199218,0.00008787858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007198973,"about_ca_system_score_gemma":0.00002672995,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02504382,"about_ca_topic_score_gemma":0.02547922,"domain_scores_codex":[0.9973725,0.0002272718,0.0005350921,0.0006215054,0.0008089794,0.000434625],"domain_scores_gemma":[0.9984984,0.0000378306,0.0001988965,0.0006001035,0.00003037467,0.0006344214],"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":[0.000214724,0.002496816,0.0324002,0.0001312915,0.0001334551,0.001083361,0.00362188,0.8970546,0.000261047,0.0007041549,0.005232711,0.05666576],"study_design_scores_gemma":[0.005371572,0.008701911,0.4423724,0.0001782223,0.001117966,0.0000795274,0.02610957,0.5016172,0.0001526749,0.002830162,0.01006452,0.001404265],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9537122,0.000002956327,0.04088464,0.0003553811,0.0001973704,0.002999695,0.000009043322,0.00005612384,0.001782579],"genre_scores_gemma":[0.9711672,0.00001475916,0.02808344,0.0003821588,0.00004302169,0.0001802863,0.000004863747,0.00002943111,0.00009484695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4099722,"threshold_uncertainty_score":0.9923033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411937183159689,"score_gpt":0.2549662426035187,"score_spread":0.2408468707719218,"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."}}