{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002317318,0.0002742019,0.001051751,0.0002489506,0.0001310844,0.0001064878,0.0004774945,0.00007041179,0.00006674493],"category_scores_gemma":[0.003249657,0.0002599208,0.00005633027,0.001602255,0.0001104186,0.0002569651,0.0004706282,0.0003075456,0.00001318711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003382826,"about_ca_system_score_gemma":0.002582361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003428015,"about_ca_topic_score_gemma":0.001004001,"domain_scores_codex":[0.9962191,0.0009072192,0.000897826,0.000800298,0.0004674219,0.0007081436],"domain_scores_gemma":[0.995941,0.0002383562,0.0002943555,0.000500523,0.0001713314,0.002854435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001028245,0.0003746903,0.9517578,0.0005667061,0.00004575224,0.00006829559,0.01547807,0.0001258018,0.0001401686,0.00003879595,0.0007649202,0.02961073],"study_design_scores_gemma":[0.002587025,0.004012283,0.9561128,0.00009195464,0.000002765762,0.0000262043,0.007088218,0.002374689,0.00001797993,0.00005541434,0.02714935,0.0004813271],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9682741,0.0007526602,0.01629109,0.01022417,0.00005651051,0.003927175,0.0001671414,0.0001135174,0.0001936596],"genre_scores_gemma":[0.9890693,0.00006479095,0.001345141,0.00924327,0.00006286649,0.0001201094,0.00002907619,0.00003783266,0.00002756708],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0291294,"threshold_uncertainty_score":0.9999853,"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."}}