Chronic wasting disease: a jurisdictional scan of advice for hunters and cervid meat-processors in CWD affected areas
Bibliographic record
Abstract
Chronic wasting disease (CWD), a prion disease affecting wild and farmed cervids such as deer, elk, moose, and reindeer, has increased significantly in some areas of North America over the past two decades. Many jurisdictions have now developed wildlife management strategies for controlling the spread and advice on minimizing human exposures to infected wild game. While there have been no known cases of CWD causing prion disease in humans, CWD has the potential to infect humans, warranting caution in handling and consumption of CWD-infected meat. This paper presents the findings of a jurisdictional scan of North America for advice on handling and processing wild cervid meat in jurisdictions affected by CWD. We reviewed publicly available state, provincial, territorial, and federal agency guidance on identifying sick animals, precautions during the processing of cervid meat, and best practices for cleaning and disinfection of meat processing tools and surfaces. Advice was found to vary widely across jurisdictions in the level of detail and in the application of some practices.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".