Broad and fine-scale genetic analysis of white-tailed deer populations: estimating the relative risk of chronic wasting disease spread
Bibliographic record
Abstract
Chronic wasting disease is a transmissible spongiform encephalopathy of cervids, similar to sheep scrapie that has only recently been detected in wild populations of white-tailed deer (Odocoileus virginianus) and mule deer (Odocoileus hemionus hemionus) in western Canada. Relatively little is known about local transmission dynamics of the disease or the potential for long-distance spread. We analysed the population genetic structure of over 2000 white-tailed deer sampled from Alberta, British Columbia, and Saskatchewan using microsatellite profiles and mtDNA sequencing to assess the relative risk of disease spread. There was very little differentiation among subpopulations and a weak trend of increasing differentiation with geographic distance. This suggests that the potential for long-distance disease spread through the dispersal of infected individuals is possible, yet the risk of spread should gradually diminish with distance from infection foci. Within subpopulations, females were more related than expected by chance (R > 0) within a radius of approximately 500 m. Sex-biased philopatry and social interactions among related females may facilitate local disease transmission within social groups. Local herd reduction may therefore be an effective tool for reducing the disease prevalence when implemented at the appropriate spatial scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".