Oral vaccination as a potential strategy to manage chronic wasting disease in wild cervid populations
Bibliographic record
Abstract
). These fatal, untreatable neurodegenerative disorders affect a variety of species causing scrapie in sheep and goats, bovine spongiform encephalopathy (BSE) in cattle, chronic wasting disease (CWD) in cervids, and Creutzfeldt-Jacob disease (CJD) in humans. Of the animal prion diseases, CWD is currently regarded as the most significant threat due its ongoing geographical spread, environmental persistence, uptake into plants, unpredictable evolution, and emerging evidence of zoonotic potential. The extensive efforts to manage CWD have been largely ineffective, highlighting the need for new disease management tools, including vaccines. Development of an effective CWD vaccine is challenged by the unique biology of these diseases, including the necessity, and associated dangers, of overcoming immune tolerance, as well the logistical challenges of vaccinating wild animals. Despite these obstacles, there has been encouraging progress towards the identification of safe, protective antigens as well as effective strategies of formulation and delivery that would enable oral delivery to wild cervids. In this review we highlight recent strategies for antigen selection and optimization, as well as considerations of various platforms for oral delivery, that will enable researchers to accelerate the rate at which candidate CWD vaccines are developed and evaluated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".