{"id":"W3092632667","doi":"10.1029/2020ms002221","title":"A Global Flood Risk Modeling Framework Built With Climate Models and Machine Learning","year":2021,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Mitacs; Marine Environmental Observation Prediction and Response Network","keywords":"Flood myth; Climate change; Climate model; Socioeconomic status; Natural hazard; Population; Scale (ratio); Environmental resource management; Climatology; Environmental science; Geography; Computer science; Meteorology; Cartography; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008878938,0.0001971628,0.0003938931,0.00006214053,0.000158834,0.0001213243,0.0001746907,0.00006968223,0.00001364533],"category_scores_gemma":[0.00004730138,0.0001533974,0.00006193019,0.0003107892,0.00003457779,0.001074333,0.0001806237,0.0004615372,0.000003077778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000902147,"about_ca_system_score_gemma":0.00002518685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003474476,"about_ca_topic_score_gemma":0.0008369833,"domain_scores_codex":[0.9979522,0.0001686892,0.0006272688,0.0003007642,0.0005986614,0.0003524479],"domain_scores_gemma":[0.9992461,0.00004312415,0.0003655246,0.0001772158,0.00005167479,0.000116421],"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":[0.0000669986,0.00004654297,0.06041116,0.00005068373,0.00002516049,0.00007918308,0.0001957943,0.9358983,0.00001098535,0.0003473569,0.000001231019,0.002866606],"study_design_scores_gemma":[0.0006778541,0.0001453633,0.0001186382,0.0005308919,0.00005167554,0.0001206326,0.0009174833,0.9930087,0.000003484434,0.004018771,0.0002213664,0.0001851623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4247764,0.01051985,0.5637034,0.00002937143,0.0001946231,0.00009266147,0.000003589373,0.00001327547,0.0006668319],"genre_scores_gemma":[0.9250791,0.0175151,0.05725862,0.00002154355,0.00008274285,0.000004130071,0.000001428974,0.00001656375,0.00002078319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5064448,"threshold_uncertainty_score":0.6255366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01409643553556023,"score_gpt":0.2609892673222705,"score_spread":0.2468928317867102,"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."}}