{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0002962069,0.00007688546,0.00009282416,0.00003181431,0.00002736569,0.00001361652,0.00009413903,0.00002165527,0.001758791],"category_scores_gemma":[0.00001541505,0.00005274357,0.00002423873,0.0001088644,0.00001405891,0.0001118923,0.0001935108,0.00009213847,0.001183168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004604323,"about_ca_system_score_gemma":0.000001643731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001739445,"about_ca_topic_score_gemma":0.0004126408,"domain_scores_codex":[0.9992371,0.00006835933,0.0001327218,0.000178672,0.0001989477,0.0001842637],"domain_scores_gemma":[0.9998257,0.000003298294,0.00002099903,0.0000796588,0.000002554744,0.00006780503],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002184607,0.0001113805,0.2721468,0.00007668419,0.00002447142,0.00001490495,0.006320926,0.05516818,0.6506666,0.00001898062,0.002440971,0.01279163],"study_design_scores_gemma":[0.002920158,0.001826503,0.4305783,0.00006012773,0.00006259973,0.000002253902,0.0003333274,0.0844126,0.3742871,0.0004671091,0.1043988,0.0006510615],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9929485,0.000008472103,0.000390244,0.002502805,0.00004235617,0.0003958934,7.524528e-7,0.00002695458,0.003684052],"genre_scores_gemma":[0.9988027,0.00000742253,0.0004955691,0.0002442601,0.00002134152,0.00002496278,0.000005122401,0.000009044052,0.000389616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2763795,"threshold_uncertainty_score":0.9995945,"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."}}