{"id":"W3198340544","doi":"10.1101/2021.08.27.21261857","title":"LESSONS FROM THE COVID-19 THIRD WAVE IN CANADA: THE IMPACT OF VARIANTS OF CONCERN AND SHIFTING DEMOGRAPHICS","year":2021,"lang":"en","type":"preprint","venue":"medRxiv","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Innovates; Institute for Clinical Evaluative Sciences; University Health Network; University of Toronto; Canadian VIGOUR Centre; University of Alberta","funders":"Canadian Institutes of Health Research; University of Toronto; Ontario Ministry of Health and Long-Term Care; Alberta Health Services","keywords":"Medicine; Demography; Transmission (telecommunications); Demographics; Retrospective cohort study; Pandemic; Coronavirus disease 2019 (COVID-19); Population; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Cohort; Cohort study; Disease; Environmental health; Internal medicine; Infectious disease (medical specialty)","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":[0.001801016,0.0003272983,0.0005064466,0.0009712804,0.00454144,0.002174096,0.001634481,0.001093062,0.00259674],"category_scores_gemma":[0.004900606,0.000268459,0.0007481395,0.002841247,0.001572482,0.0009790717,0.001568204,0.002297118,0.0002339544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04557289,"about_ca_system_score_gemma":0.08584062,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9963689,"about_ca_topic_score_gemma":0.9976937,"domain_scores_codex":[0.9976156,0.0001867794,0.00008346381,0.0003030666,0.0006045548,0.001206643],"domain_scores_gemma":[0.9960163,0.0002418165,0.0005149601,0.0001794028,0.001703604,0.001343905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000219317,0.00005501425,0.921465,0.0001291995,0.0001101306,0.0008866708,0.006380341,0.0004603321,0.0004197784,0.002332646,0.030815,0.03672654],"study_design_scores_gemma":[0.00002283495,0.00004414525,0.9709555,0.0003318578,0.00005614546,0.000298768,0.01311089,0.0007037666,0.0001272795,0.0007471461,0.01354321,0.00005844142],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8917436,0.006085043,0.0008800478,0.07541009,0.0004601325,0.0001878289,0.01075453,0.00006364912,0.0144152],"genre_scores_gemma":[0.9860306,0.002785214,0.000590287,0.006923272,0.00008885065,0.00004548937,0.001845769,0.00002782542,0.001662602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04557289,"threshold_uncertainty_score":0.330656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.123192714496202,"score_gpt":0.3847377355423566,"score_spread":0.2615450210461546,"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."}}