Chronic wasting disease in white‐tailed deer: infection, mortality, and implications for heterogeneous transmission
Bibliographic record
Abstract
Chronic wasting disease (CWD) is a fatal neurodegenerative disease affecting free-ranging and captive cervids that now occurs in 24 U.S. states and two Canadian provinces. Despite the potential threat of CWD to deer populations, little is known about the rates of infection and mortality caused by this disease. We used epidemiological models to estimate the force of infection and disease-associated mortality for white-tailed deer in the Wisconsin and Illinois CWD outbreaks. Models were based on age-prevalence data corrected for bias in aging deer using the tooth wear and replacement method. Both male and female deer in the Illinois outbreak had higher corrected age-specific prevalence with slightly higher female infection than deer in the Wisconsin outbreak. Corrected ages produced more complex models with different infection and mortality parameters than those based on apparent prevalence. We found that adult male deer have a more than threefold higher risk of CWD infection than female deer. Males also had higher disease mortality than female deer. As a result, CWD prevalence was twofold higher in adult males than females. We also evaluated the potential impacts of alternative contact structures on transmission dynamics in Wisconsin deer. Results suggested that transmission of CWD among male deer during the nonbreeding season may be a potential mechanism for producing higher rates of infection and prevalence characteristically found in males. However, alternatives based on high environmental transmission and transmission from females to males during the breeding season may also play a role.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".