{"id":"W2078491455","doi":"10.1371/journal.pone.0063584","title":"Developing a Vulnerability Mapping Methodology: Applying the Water-Associated Disease Index to Dengue in Malaysia","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Mosquito-borne diseases and control","field":"Medicine","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; McMaster University; United Nations University Institute for Water, Environment, and Health","funders":"Social Sciences and Humanities Research Council of Canada; International Development Research Centre","keywords":"Dengue fever; Vulnerability index; Vulnerability (computing); Environmental health; Vulnerability assessment; Geography; Composite index; Environmental resource management; Index (typography); Environmental science; Business; Climate change; Medicine; Psychological intervention; Computer science; Ecology; Biology; Composite indicator; Computer security","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":[0.0008903344,0.0001903112,0.0004805463,0.0001253152,0.0001257411,0.00004828511,0.0001937841,0.00008522176,0.0002977472],"category_scores_gemma":[0.001554709,0.0001109059,0.00009491367,0.0003059651,0.00004107541,0.00008465808,0.00014939,0.0003054262,0.0001465496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002237992,"about_ca_system_score_gemma":0.0001190057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004563341,"about_ca_topic_score_gemma":0.00005332154,"domain_scores_codex":[0.9979235,0.0003979379,0.0003938158,0.0003990328,0.0003462294,0.0005395011],"domain_scores_gemma":[0.9986821,0.0003030727,0.00005990706,0.000464833,0.0001802059,0.0003099269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003038945,0.001768437,0.9326055,0.0004638961,0.0005930438,0.00005881505,0.001408998,0.00009736011,0.0502325,0.0002508389,0.00009768604,0.01211901],"study_design_scores_gemma":[0.001600907,0.00004943491,0.9823656,0.0005910363,0.0002787359,0.00000112354,0.0006316706,0.009848788,0.002162599,0.002077761,0.0001313977,0.0002609515],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9816411,0.0001543021,0.002097149,0.01305005,0.00003459422,0.002851132,0.000006395926,0.00008562293,0.00007972035],"genre_scores_gemma":[0.99069,0.000009166846,0.002483219,0.003470472,0.0001222767,0.002934288,0.00002908467,0.00002785508,0.0002336349],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04976007,"threshold_uncertainty_score":0.4522611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1159575208219221,"score_gpt":0.3009224996672112,"score_spread":0.1849649788452891,"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."}}