Multiscale population genetic analysis of mule deer (Odocoileus hemionus hemionus) in western Canada sheds new light on the spread of chronic wasting disease
Bibliographic record
Abstract
To successfully manage wildlife diseases, it is necessary to understand factors that influence spread. One approach is to analyze host movement and social structure, as these behaviors can be associated with the probability of transmission. Some populations of mule deer ( Odocoileus hemionus hemionus (Rafinesque, 1817)) in western Canada are infected with chronic wasting disease (CWD), a transmissible and fatal neurodegenerative disease. We used population analysis of spatial genetic structure of mule deer at broad and local scales to understand factors that influence spread. We genotyped 2535 mule deer sampled from Alberta, Saskatchewan, and portions of British Columbia using 16 microsatellite loci. We found weak genetic structure at broad spatial scales (overall FST = 0.008) that was well defined by geographic distance, indicating the risk of CWD spread from the focus of infection will decline gradually with increasing distance, but there are no barriers to the spread over time. At the local scale of approximately 2 km, elevated relatedness among CWD-infected individuals suggests transmission rates within social groups. Sex-biased spatial autocorrelation in genetic relatedness also indicates that female philopatry underlies the social structure, and therefore transmission among relatives is potentially driving local disease persistence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".