{"id":"W7101394031","doi":"10.1093/eurpub/ckaf161.963","title":"Machine learning for predicting measles outbreaks in resource-limited settings","year":2025,"lang":"en","type":"article","venue":"European Journal of Public Health","topic":"Indian History and Philosophy","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Logistic regression; Decision tree; Measles; Outbreak; Predictive modelling; Support vector machine; Public health; Epidemiology; Vaccination","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01027362,0.0001161857,0.0002612192,0.0005543716,0.0005627096,0.0001454522,0.0002393513,0.00001585022,0.00009964452],"category_scores_gemma":[0.00060406,0.0001025739,0.0001101902,0.00009303208,0.0000770004,0.0002204755,0.0000322925,0.0005692852,0.000007165316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001480498,"about_ca_system_score_gemma":0.0002531908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002241628,"about_ca_topic_score_gemma":0.00005257019,"domain_scores_codex":[0.9967895,0.001798441,0.0007846591,0.0001281515,0.0001622952,0.0003369581],"domain_scores_gemma":[0.9988086,0.0001940005,0.0005635861,0.00009553666,0.0001845665,0.0001537748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001692156,0.0004009752,0.008572438,0.0009070218,0.0002096089,0.0001316132,0.4744049,0.000233375,0.00001727013,0.1477742,0.04855274,0.3186266],"study_design_scores_gemma":[0.0007823928,0.0003326189,0.0007462447,0.0002476844,0.000006244974,0.0000105851,0.002274974,0.0001743035,9.307101e-7,0.0002755393,0.9950703,0.00007816351],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1298346,0.0070476,0.005529856,0.1274812,0.003107021,0.0008144009,0.00009751113,0.0002070616,0.7258807],"genre_scores_gemma":[0.9904317,0.00002397336,0.0002297867,0.004373102,0.001237411,0.000001273938,0.00001654015,0.00002974937,0.003656416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9465176,"threshold_uncertainty_score":0.4327964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06934428388901823,"score_gpt":0.2599254403808036,"score_spread":0.1905811564917854,"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."}}