MERS-CoV– Low risk to Canadians
Bibliographic record
Abstract
Middle East respiratory syndrome - Coronavirus (MERS-CoV) -- is a novel coronavirus that has caused a number of community-acquired cases and health care associated outbreaks in Saudi Arabia and the United Arab Emirates (UAE) as well as sporadic cases in other countries, especially in the Middle East. The evidence to date links MERS-CoV cases with exposure to camels, including camel products or to probable or confirmed human cases of MERS-CoV. It typically presents as an acute respiratory illness and is associated with a 35% mortality rate. Based on available information at this time, the current risk to Canadians for acquiring MERS-CoV infections is considered low. However, the International Health Regulations Committee concerning MERS-CoV has cautioned that the upsurge of cases seen this past spring (2014) may be predictive of an increase in cases related to the Hajj - an annual pilgrimage to Mecca in Saudi Arabia that took place in early October 2014. Although the overall risk is low, the Public Health Agency of Canada and its National Microbiology Laboratory (NML) in close collaboration with provincial and territorial partners, the Canadian Public Health Laboratory Network (CPHLN) and infection prevention and control experts have developed a number of preparedness guidance documents and protocols to address the risk of an imported case of MERS-CoV in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".