{"id":"W3174656366","doi":"10.1038/s41545-021-00125-2","title":"Forecasting point-of-consumption chlorine residual in refugee settlements using ensembles of artificial neural networks","year":2021,"lang":"en","type":"article","venue":"npj Clean Water","topic":"Water Systems and Optimization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Global Health Research; York University","funders":"Achmea; York University; Natural Sciences and Engineering Research Council of Canada; Enhancing Learning and Research for Humanitarian Assistance; United States Agency for International Development","keywords":"Residual; Artificial neural network; Consumption (sociology); Human settlement; Point (geometry); Refugee; Chlorine; Point estimation; Environmental science; Water supply; Computer science; Environmental engineering; Engineering; Statistics; Artificial intelligence; Geography; Mathematics; Waste management; Materials science; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008436691,0.0006151997,0.0004934384,0.0004703016,0.000195568,0.0004852583,0.000496169,0.0005632822,0.0004512368],"category_scores_gemma":[0.001994528,0.0002263478,0.000547599,0.0004695191,0.0001944568,0.0005666658,0.000428463,0.0006796722,0.00009094008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007261265,"about_ca_system_score_gemma":0.0003689899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02977377,"about_ca_topic_score_gemma":0.02233389,"domain_scores_codex":[0.9998288,0.0000548404,0.00001053623,0.0000416048,0.00003122522,0.00003301234],"domain_scores_gemma":[0.999221,0.0003931037,0.00009185416,0.00005459677,0.0001959471,0.00004350178],"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":[0.00003029523,0.00002257988,0.008080524,0.00000614825,0.00003494637,0.0000239367,0.000009889834,0.9876438,0.0003178374,0.00005207932,0.0001112095,0.003666785],"study_design_scores_gemma":[0.000001105616,0.000009312908,0.0017651,0.0000015499,0.00000364494,0.000001703353,0.000008143387,0.9980063,0.0001061088,0.00006996215,0.00002419278,0.000002851138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754196,0.0001474526,0.02285754,0.0001469035,0.00003532257,0.0000147429,0.0002826322,0.00009843953,0.000997338],"genre_scores_gemma":[0.9975498,0.0000288107,0.002051847,0.000009881798,0.00000405123,0.000006867716,0.0001497317,0.000003445446,0.0001955288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02977377,"threshold_uncertainty_score":0.05920094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04224455865913453,"score_gpt":0.2397618572319719,"score_spread":0.1975172985728373,"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."}}