{"id":"W4408436387","doi":"10.5194/egusphere-egu25-7330","title":"Climate-Resilience of Dams: Canadian Perspectives and Design Flood Estimation Guidelines","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Dam Engineering and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Resilience (materials science); Flood myth; Estimation; Climate change; Environmental science; Environmental resource management; Climate resilience; Geography; Environmental planning; Water resource management; Engineering; Geology; Oceanography; Systems engineering; Archaeology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002100412,0.0002045923,0.0002570993,0.0002473833,0.00003210726,0.00003001652,0.0001647011,0.0001702746,0.00002177372],"category_scores_gemma":[0.0001432113,0.0002079205,0.00004109772,0.0001021245,0.00002902779,0.00004868148,0.00008086526,0.0001958087,0.000002836229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000104961,"about_ca_system_score_gemma":0.0001359272,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0104661,"about_ca_topic_score_gemma":0.004084896,"domain_scores_codex":[0.9991567,0.00001350553,0.0003002149,0.0002232386,0.00009266349,0.0002136969],"domain_scores_gemma":[0.9994158,0.00005479329,0.00002388409,0.000283485,0.0001221296,0.00009996101],"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":[0.000001524601,0.000003088982,0.00004697613,0.0003923773,0.00003302516,0.00000121108,0.0004002065,0.9908092,0.0000632384,0.001275764,0.0009666315,0.006006748],"study_design_scores_gemma":[0.00007468189,0.000008458309,0.001101693,0.0003378533,0.00003063905,0.000001778068,0.0002735131,0.9966798,0.0008077951,0.0003075903,0.0001658354,0.000210299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01230363,0.005503045,0.9546376,0.0002987366,0.0008169202,0.0005593068,0.0002932559,0.0006799564,0.02490758],"genre_scores_gemma":[0.820594,0.001680676,0.1773241,0.00001502581,0.00004468775,0.00003144034,0.00002817892,0.00002408953,0.0002577357],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8082904,"threshold_uncertainty_score":0.9961233,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02072791052174344,"score_gpt":0.2721691738066681,"score_spread":0.2514412632849247,"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."}}