{"id":"W4386440676","doi":"10.1029/2023ef003619","title":"Overrepresentation of Historically Underserved and Socially Vulnerable Communities Behind Levees in the United States","year":2023,"lang":"en","type":"article","venue":"Earth s Future","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"National Weather Service; National Oceanic and Atmospheric Administration","keywords":"Disadvantaged; Geography; Poverty; Socioeconomic status; Equity (law); Population; Socioeconomics; Levee; Economic growth; Political science; Environmental health; Medicine; Sociology; Cartography","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":[0.0002991203,0.000113374,0.0001349766,0.0009501418,0.0008064769,0.0006066854,0.0003249234,0.0002566411,0.003107299],"category_scores_gemma":[0.001031412,0.00008025092,0.0001746669,0.0007692109,0.0003464205,0.0004259124,0.001258207,0.0003610566,0.0001374783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003871534,"about_ca_system_score_gemma":0.0006437165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04784529,"about_ca_topic_score_gemma":0.1159933,"domain_scores_codex":[0.9996862,0.0000778583,0.00002665635,0.00004860446,0.00006123722,0.00009938759],"domain_scores_gemma":[0.999136,0.00008466175,0.0003554879,0.00003158888,0.0001489749,0.0002431834],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002259769,0.00003366088,0.9947091,0.00001180259,0.00001771801,0.00008759092,0.0009375668,0.00002933125,0.0002300304,0.0001130041,0.0005023478,0.003305168],"study_design_scores_gemma":[9.921719e-7,0.00002312103,0.9945073,0.0000206459,0.000006601355,0.00009787236,0.004696046,0.000108199,0.00006260934,0.00006695525,0.0004064389,0.000003295424],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998609,0.00005632694,0.00003609255,0.0001568487,0.000003850609,0.000005222749,0.0002860291,0.000002012094,0.0008446664],"genre_scores_gemma":[0.9996063,0.00004690008,0.00002704485,0.00004622839,0.000003033746,0.000005111049,0.0001383585,5.469231e-7,0.0001265848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04784529,"threshold_uncertainty_score":0.0951336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02380489516200217,"score_gpt":0.2553141331750731,"score_spread":0.2315092380130709,"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."}}