{"id":"W4294844176","doi":"10.1371/journal.pdig.0000100","title":"Deploying wearable sensors for pandemic mitigation: A counterfactual modelling study of Canada’s second COVID-19 wave","year":2022,"lang":"en","type":"article","venue":"PLOS Digital Health","topic":"COVID-19 Digital Contact Tracing","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; McGill University","funders":"University of Oxford; McGill University; Princeton University","keywords":"Wearable computer; Pandemic; Software deployment; Coronavirus disease 2019 (COVID-19); Computer science; Population; Wearable technology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Counterfactual thinking; Medicine; Environmental health; Embedded system; Psychology; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.004817423,0.00119532,0.0009349808,0.0007897926,0.0009028853,0.001955912,0.003032104,0.00202435,0.002358957],"category_scores_gemma":[0.0123726,0.0007276093,0.001976951,0.0009392134,0.001702389,0.001167579,0.001243582,0.002017316,0.0001989935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01200363,"about_ca_system_score_gemma":0.008788981,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8178639,"about_ca_topic_score_gemma":0.6740046,"domain_scores_codex":[0.9981832,0.0009285531,0.00005395218,0.0002694837,0.000140778,0.0004241155],"domain_scores_gemma":[0.9864596,0.00990105,0.001051115,0.0006953203,0.001518572,0.0003742719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009494554,0.0004268414,0.03107276,0.0001726445,0.0002695083,0.0002788293,0.0002670476,0.9526555,0.0006722289,0.007528179,0.002106376,0.00360069],"study_design_scores_gemma":[0.0003648061,0.0004413283,0.01348963,0.00004721417,0.0002256285,0.00004306067,0.0005786114,0.980747,0.0004688749,0.001843714,0.001672608,0.00007746553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9770546,0.0004597217,0.01243895,0.001534084,0.00008985651,0.0004073587,0.002980902,0.0001013988,0.004933192],"genre_scores_gemma":[0.9924517,0.0001910171,0.004097011,0.0001560225,0.00001364189,0.000172883,0.0008045968,0.00001395507,0.00209912],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1821361,"threshold_uncertainty_score":0.3664175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08253434111203006,"score_gpt":0.2940167041544072,"score_spread":0.2114823630423771,"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."}}