{"id":"W3037968044","doi":"10.2196/18939","title":"Using Open-Source Intelligence to Detect Early Signals of COVID-19 in China: Descriptive Study","year":2020,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Health and Medical Research Council","keywords":"Outbreak; China; Medicine; Pneumonia; Social media; Coronavirus disease 2019 (COVID-19); Family medicine; Environmental health; Disease; Pediatrics; Demography; Geography; Infectious disease (medical specialty); Virology; Internal medicine; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001748463,0.000686058,0.0007506906,0.00480044,0.00100115,0.001139144,0.0006781462,0.0005250573,0.0021373],"category_scores_gemma":[0.005140445,0.0003987255,0.0008773985,0.006520355,0.000546107,0.001235667,0.001421184,0.0006206041,0.0003605735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001291341,"about_ca_system_score_gemma":0.003612099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04413677,"about_ca_topic_score_gemma":0.03920655,"domain_scores_codex":[0.9981295,0.0002439839,0.0004477744,0.0003844858,0.0003999491,0.000394309],"domain_scores_gemma":[0.9943115,0.001152314,0.002426379,0.0004171664,0.001047067,0.0006456774],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007004867,0.00004520362,0.9946018,0.000231898,0.00006424895,0.0002962956,0.0006651515,0.00003877312,0.0001611758,0.00004914848,0.0004735666,0.003302729],"study_design_scores_gemma":[0.000009935575,0.00008582878,0.9955614,0.0001121301,0.0001164856,0.000363059,0.001774619,0.0003612259,0.0001702746,0.00005092316,0.001373008,0.0000211337],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9924132,0.001210819,0.0002235559,0.0001199937,0.00001001639,0.000161253,0.004876159,0.00001559257,0.0009693139],"genre_scores_gemma":[0.9936337,0.0006551081,0.0003171726,0.0001327068,0.00001674503,0.000175892,0.00466744,0.00001166941,0.0003896422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04413677,"threshold_uncertainty_score":0.08775973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1358785595902893,"score_gpt":0.3970299192291632,"score_spread":0.2611513596388739,"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."}}