{"id":"W3204329563","doi":"10.1007/s41742-021-00370-w","title":"Robust Flood Risk Management Strategies Through Bayesian Estimation and Multi-objective Optimization","year":2021,"lang":"en","type":"article","venue":"International Journal of Environmental Research","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Flood myth; Estimation; Flood risk management; Risk management; Bayesian probability; Computer science; Risk analysis (engineering); Environmental science; Engineering; Business; Artificial intelligence; Geography; Systems engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003320303,0.001640543,0.002533331,0.001860848,0.0005545964,0.001816345,0.001788466,0.002053477,0.001808626],"category_scores_gemma":[0.008618994,0.001531917,0.001602536,0.001001987,0.001024868,0.002652226,0.002494355,0.001925365,0.0003259653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122397,"about_ca_system_score_gemma":0.001994911,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009290405,"about_ca_topic_score_gemma":0.007195004,"domain_scores_codex":[0.9989275,0.0004285323,0.00006530578,0.0001720952,0.0002816872,0.0001248495],"domain_scores_gemma":[0.9963073,0.002677318,0.0003911793,0.00009036345,0.0004446988,0.00008917289],"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":[0.00002405129,0.00002142535,0.0001385596,0.00002428757,0.00003605527,0.00001238661,0.00001493879,0.9889453,0.0002544196,0.002146609,0.0001641478,0.008217834],"study_design_scores_gemma":[0.000004138993,0.00001001857,0.00004083407,0.000003991074,0.000005179877,0.000002525434,0.00000212673,0.9986273,0.00006404569,0.001185845,0.0000502375,0.0000037621],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008132613,0.0002112119,0.9903845,0.0001447775,0.00001565565,0.00002942582,0.00003699949,0.0001101405,0.0009347962],"genre_scores_gemma":[0.665172,0.00061552,0.3294067,0.0002028993,0.0001107294,0.0004171608,0.0003279679,0.0001728859,0.003574163],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009290405,"threshold_uncertainty_score":0.01847267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03105528489837356,"score_gpt":0.3320672882590396,"score_spread":0.3010120033606661,"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."}}