L’expérience au Yukon avec la COVID-19 : restrictions de voyage, variants et propagation entre les non vaccinés
Bibliographic record
Abstract
L’expérience du Yukon concernant la maladie à coronavirus 2019 (COVID-19) a été intéressante; le territoire a mis en place avec succès des restrictions de voyage pour limiter l’importation du virus et a déployé les vaccins rapidement par rapport à la plupart des administrations canadiennes. Cependant, la première vague de COVID-19 du Yukon en juin et juillet 2021 a fait déborder le système de santé en raison de la transmission généralisée parmi les enfants non-vaccinés, les jeunes et les adultes, malgré une forte participation à la vaccination et le port du masque obligatoire. Cette expérience met en lumière l’importance du soutien continu aux programmes de vaccination publique, de l’adoption généralisée de vaccins dans les populations pédiatriques, et l’assouplissement judicieux des interventions non pharmaceutiques dans toutes les administrations canadiennes à mesure qu’elles rouvrent alors que des variants plus contagieux apparaissent
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".