{"id":"W3088943773","doi":"10.1371/journal.pntd.0008056","title":"Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia","year":2020,"lang":"en","type":"article","venue":"PLoS neglected tropical diseases","topic":"Mosquito-borne diseases and control","field":"Medicine","cited_by":123,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Agency of Canada; The Quebec Population Health Research Network; McGill University; Université de Montréal; McGill University Health Centre","funders":"Canadian Institutes of Health Research","keywords":"Random forest; Dengue fever; Artificial neural network; Statistics; Ensemble forecasting; Population; Machine learning; Econometrics; Artificial intelligence; Computer science; Mathematics; Environmental health; Medicine","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":[0.00001516223,0.0002151586,0.00044934,0.00007896365,0.0002674671,0.00008844028,0.00004802092,0.00008551115,0.00001830147],"category_scores_gemma":[0.002452821,0.0002013209,0.0000784497,0.0001549279,0.0001213695,0.00009796237,0.0001031207,0.000233423,4.197827e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005772163,"about_ca_system_score_gemma":0.00005765972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001910956,"about_ca_topic_score_gemma":0.0003122361,"domain_scores_codex":[0.9983924,0.00009642145,0.0003694163,0.0004537023,0.0003595327,0.0003285088],"domain_scores_gemma":[0.9983754,0.0008419628,0.00009731454,0.00004276893,0.0001806728,0.0004618874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005267354,0.0001591257,0.9861931,0.0002799602,0.0001350493,0.00003179832,0.00009793945,0.002389068,0.002055083,0.0001721016,0.000153409,0.003066027],"study_design_scores_gemma":[0.004007928,0.000135716,0.4781167,0.00006505827,0.0001525551,0.00001106591,0.00002127994,0.5172513,0.00001409837,0.0001037774,0.00002617027,0.00009434015],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928318,0.004385813,0.0006609113,0.001046531,0.0000274869,0.0008396572,0.0001000253,0.00008831499,0.00001948734],"genre_scores_gemma":[0.9983729,0.00008261686,0.00009711006,0.0002734998,0.0006660381,0.0001469466,0.0003247465,0.00003018567,0.000005997598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5148623,"threshold_uncertainty_score":0.820963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03275037840547024,"score_gpt":0.2587592723223544,"score_spread":0.2260088939168842,"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."}}