{"id":"W2462978922","doi":"10.1371/journal.pone.0172355","title":"Cholera forecast for Dhaka, Bangladesh, with the 2015-2016 El Niño: Lessons learned","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Vibrio bacteria research studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Science and Technology Directorate; International Centre for Diarrhoeal Disease Research, Bangladesh; Global Affairs Canada; Fogarty International Center; Department for International Development; National Oceanic and Atmospheric Administration; Styrelsen för Internationellt Utvecklingssamarbete; National Institute of General Medical Sciences; U.S. Department of Homeland Security; National Aeronautics and Space Administration; National Institutes of Health; National Science Foundation","keywords":"Cholera; Climatology; Predictability; Geography; Teleconnection; Outbreak; Monsoon; El Niño Southern Oscillation; Environmental science; Precipitation; Demography; Statistics; Meteorology; Mathematics; Geology; Medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.000191719,0.0001528093,0.0001843576,0.00001835655,0.0006556964,0.000141973,0.000515896,0.00008479598,0.00002229081],"category_scores_gemma":[0.0002869384,0.0000956888,0.00006572218,0.00002042323,0.0002872013,0.000009158312,0.000298938,0.0001061633,0.00002540469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001354621,"about_ca_system_score_gemma":0.00008175523,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003233308,"about_ca_topic_score_gemma":0.000127707,"domain_scores_codex":[0.9989742,0.00002669655,0.0000959536,0.0003286217,0.0001997582,0.0003747481],"domain_scores_gemma":[0.998742,0.00002183542,0.0001081623,0.0008558439,0.0001970576,0.00007512975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003871867,0.0003265463,0.0009026972,0.0000681121,0.00073059,0.000002433216,0.0001724263,0.000001193918,0.9812567,0.00007373947,0.01383026,0.00224817],"study_design_scores_gemma":[0.003346033,0.001526533,0.02410014,0.0001815105,0.000202616,0.000004227574,0.0001669095,0.0001211721,0.8096454,0.0001367097,0.16007,0.00049871],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9374826,0.0005644872,0.0002951295,0.058157,0.00004100014,0.0007943338,0.000190654,0.00001901312,0.002455783],"genre_scores_gemma":[0.9870861,0.0004750333,0.0009810692,0.0002989064,0.0005958587,0.0003465867,0.00008739168,0.00004221898,0.0100868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1716112,"threshold_uncertainty_score":0.5043153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09807495171215826,"score_gpt":0.335910583341187,"score_spread":0.2378356316290288,"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."}}