It's time to apply outbreak response best practices to avian influenza: A national call to action.
Bibliographic record
Abstract
Cases of high pathogenicity avian influenza (HPAI) in Canada are upon us again and with reports of infection in US dairy cattle and a dairy farmer in the United States, concern has been raised. Although panic isn't helpful, this heightened level of concern is appropriate, given that reports of human infections with the H5N1 virus often indicate high mortality rates. These can range from 14 to 50%. The current devastating impact of the virus on the poultry industry, as well as its propensity to mutate are also reasons for concern. At the same time, HPAI provides an opportunity for the poultry and livestock industries to align and organize coherently for the management of all zoonotic diseases and other industry issues. To manage HPAI more effectively, it is essential to align all stakeholders under Outbreak Response Best Practices using a formal Quality Management System (QMS). The objective of this article is to describe this approach with examples drawn from management of the Walkerton waterborne disease crisis. We urge the veterinary profession to rise to the challenge of HPAI and use it as a context in which to align more coherently with national stakeholders for the prevention and management of all priority issues within the areas of Agri-food and Public Health.
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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 | 0.111 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.023 | 0.031 |
| Open science | 0.009 | 0.020 |
| Research integrity | 0.031 | 0.042 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".