{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","open_science","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002969999,0.0005994287,0.001232111,0.0002259358,0.0003614093,0.0005369794,0.00622028,0.0008207844,0.0001298354],"category_scores_gemma":[0.01925769,0.0003103817,0.002254671,0.0009147236,0.001004439,0.0008870214,0.0007046826,0.008235373,0.0000207842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003637308,"about_ca_system_score_gemma":0.001189964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002546132,"about_ca_topic_score_gemma":0.00001376334,"domain_scores_codex":[0.9929138,0.0008351748,0.002485252,0.0004994389,0.002436891,0.0008294078],"domain_scores_gemma":[0.9892286,0.006098981,0.003050031,0.0008826097,0.0003538814,0.0003859154],"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":[0.00002533023,0.0002822321,0.002573379,0.001616794,0.002016454,0.009982464,0.009728475,0.0005353534,0.0005772679,0.003130737,0.957612,0.01191949],"study_design_scores_gemma":[0.01709894,0.01989912,0.03206432,0.09035248,0.005098325,0.06285434,0.01048054,0.026235,0.006268056,0.2640713,0.4608386,0.004739],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.06751401,0.0009827893,0.2379694,0.6905335,0.001254159,0.0007983328,0.0000680493,0.0002635589,0.0006161829],"genre_scores_gemma":[0.9392915,0.00003497315,0.00167013,0.05690857,0.001770432,0.000008924644,0.000007051799,0.00004333943,0.0002651165],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8717775,"threshold_uncertainty_score":0.9999349,"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."}}