{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002864982,0.0006654318,0.0005811733,0.002028517,0.0002983234,0.001067795,0.0006879521,0.0008148856,0.0007781327],"category_scores_gemma":[0.009146063,0.0002191755,0.0005132393,0.0009812509,0.0002179517,0.0009912754,0.0004498514,0.0006696354,0.0002152267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008938956,"about_ca_system_score_gemma":0.0009077729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01065765,"about_ca_topic_score_gemma":0.007634558,"domain_scores_codex":[0.9991155,0.0004657535,0.00007163436,0.0001561066,0.0001071884,0.00008381289],"domain_scores_gemma":[0.9933957,0.005321713,0.0005369963,0.0001497497,0.0004551227,0.0001406352],"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":[0.0002045593,0.0004381127,0.1285479,0.0001511048,0.0002642288,0.000192446,0.00006950551,0.7964396,0.0006565051,0.0007065372,0.001835371,0.07049423],"study_design_scores_gemma":[0.000005307551,0.00005177178,0.004140156,0.00001669303,0.00001100569,0.00002140794,0.00003417246,0.9946561,0.0002049229,0.0007267865,0.0001262189,0.000005427051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8482085,0.001952053,0.1423197,0.001692193,0.0001177016,0.0001565695,0.001489489,0.00101368,0.00305023],"genre_scores_gemma":[0.9820728,0.0001929506,0.01684234,0.00006643972,0.00003389922,0.00004010037,0.000436397,0.000006983747,0.0003080197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01065765,"threshold_uncertainty_score":0.02119118,"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."}}