{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001467863,0.0002583003,0.0007462769,0.000116398,0.00008327839,0.0000322761,0.0004761432,0.0002220697,0.00002595318],"category_scores_gemma":[0.002785162,0.0001444218,0.0002164425,0.0004858231,0.0003769389,0.00002404583,0.0007224555,0.001325595,2.807093e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004222435,"about_ca_system_score_gemma":0.01435513,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9573223,"about_ca_topic_score_gemma":0.864251,"domain_scores_codex":[0.9974431,0.0004704084,0.0005559758,0.0004616359,0.0007092702,0.0003596577],"domain_scores_gemma":[0.9959292,0.002492097,0.0003140651,0.0009750818,0.0001962468,0.00009330414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001286936,0.00004550995,0.9901079,0.0002869524,0.0004081496,0.0002304771,0.002893324,0.00002149756,0.005041602,0.00005913738,0.000194805,0.0005819447],"study_design_scores_gemma":[0.0009667592,0.00004871103,0.9911132,0.000473051,0.0001333078,0.0000237507,0.002133366,0.001764207,0.002271456,0.0005240829,0.0004002531,0.000147859],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900233,0.004009702,0.00008018623,0.00464354,0.000132165,0.000548937,0.000236606,0.000009289738,0.0003162684],"genre_scores_gemma":[0.9916842,0.0001968893,0.00003808148,0.007914162,0.00009408544,0.00002749733,0.00001625639,0.00002618844,0.000002663057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09307127,"threshold_uncertainty_score":0.9912326,"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."}}