{"id":"W3204258907","doi":"10.2196/30968","title":"Characterization of Unlinked Cases of COVID-19 and Implications for Contact Tracing Measures: Retrospective Analysis of Surveillance Data","year":2021,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Health and Medical Research Fund; Food and Health Bureau; Chinese University of Hong Kong","keywords":"Contact tracing; Epidemiology; Medicine; Outbreak; Demography; Coronavirus disease 2019 (COVID-19); Disease; Internal medicine; Pathology; Infectious disease (medical specialty)","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":[],"consensus_categories":[],"category_scores_codex":[0.001923523,0.0001546776,0.0008243656,0.0003404875,0.0001426166,0.00009534784,0.0004505855,0.00006840561,0.000001859428],"category_scores_gemma":[0.00395283,0.0001600995,0.00008048952,0.001921191,0.0000775414,0.0006778786,0.0002222669,0.0000818109,4.178715e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001170549,"about_ca_system_score_gemma":0.00213707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002778688,"about_ca_topic_score_gemma":0.002596629,"domain_scores_codex":[0.9976581,0.0002933251,0.0008116826,0.0006682681,0.0002500469,0.0003186192],"domain_scores_gemma":[0.9954264,0.001609505,0.0007503929,0.001057198,0.0007736356,0.0003828572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003529727,0.0001255344,0.967234,0.001131362,0.000237045,0.000001533585,0.001414025,0.00001069877,0.005591418,0.00989735,0.00002676119,0.01429504],"study_design_scores_gemma":[0.0005586708,0.0001597801,0.9890641,0.00002272311,0.000006240105,0.00001011202,0.0001053137,0.008174093,0.0000678284,0.000122659,0.00155736,0.0001511091],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5733595,0.00084752,0.4122179,0.01014572,0.00005642251,0.0006062621,0.00268495,0.00005283747,0.00002887336],"genre_scores_gemma":[0.9972109,0.0004327045,0.0009223527,0.000646207,0.00001596904,0.00003232423,0.0007234362,0.000008910794,0.000007205879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4238513,"threshold_uncertainty_score":0.6528669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1103392511750053,"score_gpt":0.3688937558166017,"score_spread":0.2585545046415963,"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."}}