{"id":"W3036565276","doi":"10.5694/mja2.50680","title":"Tracking, tracing, trust: contemplating mitigating the impact of <scp>COVID</scp> ‐19 through technological interventions","year":2020,"lang":"en","type":"letter","venue":"The Medical Journal of Australia","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Contact tracing; Internet privacy; Business; Computer security; Computer science; Medicine; Coronavirus disease 2019 (COVID-19); Disease; Infectious disease (medical specialty)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01139697,0.0007952729,0.001101781,0.0006153451,0.003441486,0.00522423,0.003834809,0.01978252,0.01035565],"category_scores_gemma":[0.09736844,0.0005894593,0.0008035646,0.000404176,0.00538202,0.005768374,0.002081401,0.02688319,0.004868387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001804396,"about_ca_system_score_gemma":0.003805414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001814961,"about_ca_topic_score_gemma":0.003875498,"domain_scores_codex":[0.9927782,0.003373486,0.0006201302,0.0008158216,0.001786238,0.0006260955],"domain_scores_gemma":[0.9242529,0.05674857,0.003305266,0.001513869,0.01006339,0.004116033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003760937,0.00002739197,0.0003021394,0.000289489,0.0000214534,0.0004164788,0.0002943996,0.00004044126,0.0000591301,0.001841749,0.9756748,0.02099494],"study_design_scores_gemma":[0.00005835486,0.0001039021,0.0006983557,0.001417868,0.00007478307,0.001316217,0.000981845,0.0005262552,0.0003552768,0.009122975,0.9852802,0.00006407501],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.000389922,0.004523833,0.0006665063,0.8766883,0.1161389,0.00001590185,0.00003699987,0.00006551092,0.001474053],"genre_scores_gemma":[0.01086773,0.01104804,0.001168465,0.664153,0.3060311,0.00007170776,0.00003156271,0.0001228424,0.006505535],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01978252,"threshold_uncertainty_score":0.06027371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1450379480688028,"score_gpt":0.3929819720785408,"score_spread":0.247944024009738,"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."}}