{"id":"W2417168758","doi":"10.1038/srep40841","title":"Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks","year":2017,"lang":"en","type":"preprint","venue":"Scientific Reports","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Institute of Environmental Health Sciences; Intelligence Advanced Research Projects Activity; National Institutes of Health","keywords":"Outbreak; Infectious disease (medical specialty); Disease; Dengue fever; Disease surveillance; China; Geography; Incidence (geometry); Demography; Medicine; Environmental health; Pathology; Sociology","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001430872,0.0003737921,0.000856947,0.0005854599,0.0005931323,0.001067294,0.0002613561,0.0002232943,0.00006444865],"category_scores_gemma":[0.00167254,0.000365978,0.0002810268,0.0002597393,0.0001551924,0.0002001787,0.001088814,0.0004897655,0.00002206967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002657268,"about_ca_system_score_gemma":0.0009166314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008013314,"about_ca_topic_score_gemma":0.0004823403,"domain_scores_codex":[0.9958358,0.00009341195,0.0009011622,0.001762802,0.0009476271,0.0004591484],"domain_scores_gemma":[0.9944195,0.00003925544,0.0008331845,0.003225717,0.0004174096,0.0010649],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001090912,0.000093639,0.9713045,0.0001479357,0.0001360421,0.0007241191,0.0001476567,0.0006560056,0.0000289733,0.000008962678,0.01293339,0.01380784],"study_design_scores_gemma":[0.0004253833,0.00003151588,0.9589287,0.0005330066,0.0006745193,0.00004101171,0.00002097811,0.002712719,0.00002136045,0.007544978,0.02838843,0.0006774216],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9756573,0.0003077514,0.001789385,0.001719812,0.01038552,0.00100317,0.001052192,0.0003882782,0.007696544],"genre_scores_gemma":[0.9834182,0.000008936117,0.0005562869,0.00007506872,0.0006259495,0.00008972445,0.006567104,0.00004546965,0.0086133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01545504,"threshold_uncertainty_score":0.9999697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0714903096583521,"score_gpt":0.3560032230490909,"score_spread":0.2845129133907388,"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."}}