Risk-Based Disease Surveillance Towards the Eradication of Scrapie from the Canadian National Goat Herd and Sheep Flock
Bibliographic record
Abstract
Scrapie, a production limiting disease of sheep and goats, is part of a family of diseases known as transmissible spongiform encephalopathies (TSEs). Canada conducts active and passive scrapie surveillance of the national goat herd and sheep flock with the goal of achieving eradication of scrapie and of meeting World Organisation for Animal Health (OIE) requirements for freedom from disease. Risk-based disease surveillance is commonly used for surveillance of non-highly contagious animal diseases because it is more resource efficient than traditional random sampling methods. It was found that the surveillance system’s sensitivity trended upwards from 2013 to 2016. It was also found that risk-based sampling can be used to reduce required minimum sample sizes. Furthermore, by implementing a risk-based surveillance system, the sample size required to meet surveillance targets will decrease over time with the decrease in scrapie prevalence as a result of the success of scrapie eradication efforts in Canada. Canada’s scrapie surveillance efforts are sufficient but can be made more efficient by implementing risk-based surveillance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".