{"id":"W3005797502","doi":"10.3390/w12020483","title":"Machine Learning to Evaluate Impacts of Flood Protection in Bangladesh, 1983–2014","year":2020,"lang":"en","type":"article","venue":"Water","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Global Affairs Canada; Department for International Development; Department for International Development, UK Government; International Centre for Diarrhoeal Disease Research, Bangladesh; Styrelsen för Internationellt Utvecklingssamarbete","keywords":"Flood myth; Welfare; Climate change; Futures contract; Impact assessment; Environmental resource management; Environmental planning; Geography; Business; Economics; Ecology; Political science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003826117,0.0006151302,0.0004655198,0.001473154,0.0003419959,0.0009887471,0.0006536772,0.0006445309,0.001576104],"category_scores_gemma":[0.01144354,0.0001929144,0.0007606075,0.001779133,0.0004748362,0.001034342,0.0009655581,0.001416366,0.0005075047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002007343,"about_ca_system_score_gemma":0.0008345147,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02400036,"about_ca_topic_score_gemma":0.01782228,"domain_scores_codex":[0.9989341,0.0005462403,0.00009732954,0.000141933,0.0001710113,0.0001094257],"domain_scores_gemma":[0.9940078,0.004163529,0.0006600445,0.0002863292,0.0006826433,0.0001995896],"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":[0.0002853561,0.0002436632,0.4276425,0.0001811317,0.0005378802,0.0001230429,0.0001644434,0.4763948,0.0004247956,0.002576212,0.00601241,0.08541375],"study_design_scores_gemma":[0.00002532858,0.0002434513,0.1658611,0.0000671618,0.00005329804,0.00004974694,0.0005588491,0.8243682,0.0008718517,0.005378888,0.002483884,0.00003824972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9618989,0.0007385082,0.02023307,0.002469406,0.0001212977,0.0001261024,0.006058409,0.0002415466,0.008112725],"genre_scores_gemma":[0.9910739,0.0002137306,0.004289615,0.00007158424,0.00002481669,0.00007972748,0.003382181,0.0000131803,0.0008513065],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02400036,"threshold_uncertainty_score":0.04772133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01395380244048691,"score_gpt":0.2386744543670629,"score_spread":0.224720651926576,"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."}}