{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005626491,0.0003690893,0.0002944531,0.002329267,0.0004879422,0.0009095849,0.0007401874,0.0003087087,0.0005961822],"category_scores_gemma":[0.01518456,0.0003166666,0.0003852199,0.001965249,0.0006517411,0.0009120145,0.001086648,0.0003684528,0.0001425543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006961374,"about_ca_system_score_gemma":0.0006572535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01030644,"about_ca_topic_score_gemma":0.006899344,"domain_scores_codex":[0.995242,0.001706315,0.0009564034,0.0008148741,0.0008393254,0.0004412111],"domain_scores_gemma":[0.9866781,0.002973204,0.006170279,0.001804986,0.001774558,0.0005988989],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000278699,0.00001477635,0.9987224,0.00001038182,0.00001421046,0.00005110244,0.000200301,0.00003858666,0.0001246948,0.00002447156,0.00003603277,0.0007351229],"study_design_scores_gemma":[0.000003639099,0.00009975697,0.9974557,0.00002637409,0.00002188523,0.0004286974,0.000674235,0.0006721345,0.0002883025,0.00003224707,0.000291996,0.000005071969],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977579,0.0001709242,0.0009454975,0.00001696886,0.00000365286,0.00008929591,0.0006134682,0.000006706883,0.000395618],"genre_scores_gemma":[0.9980783,0.0001050944,0.0005661917,0.00002018579,0.000004113318,0.00006776876,0.001089056,0.000003668859,0.00006552539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01030644,"threshold_uncertainty_score":0.02975607,"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."}}