Detection and Control of Prion Diseases in Food Animals
Bibliographic record
Abstract
Transmissible spongiform encephalopathies (TSEs), or prion diseases, represent a unique form of infectious disease based on misfolding of a self-protein (PrP(C)) into a pathological, infectious conformation (PrP(Sc)). Prion diseases of food animals gained notoriety during the bovine spongiform encephalopathy (BSE) outbreak of the 1980s. In particular, disease transmission to humans, to the generation of a fatal, untreatable disease, elevated the perspective on livestock prion diseases from food production to food safety. While the immediate threat posed by BSE has been successfully addressed through surveillance and improved management practices, another prion disease is rapidly spreading. Chronic wasting disease (CWD), a prion disease of cervids, has been confirmed in wild and captive populations with devastating impact on the farmed cervid industries. Furthermore, the unabated spread of this disease through wild populations threatens a natural resource that is a source of considerable economic benefit and national pride. In a worst-case scenario, CWD may represent a zoonotic threat either through direct transmission via consumption of infected cervids or through a secondary food animal, such as cattle. This has energized efforts to understand prion diseases as well as to develop tools for disease detection, prevention, and management. Progress in each of these areas is discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".