Assessment of Mammalian Scavenger and Wild White-Tailed Deer Activity at White-Tailed Deer Farms
Bibliographic record
Abstract
) in the wild and on cervid farms have drawn the attention of state wildlife agencies and animal health agencies as Chronic Wasting Disease (CWD) has spread across North America. Deer farm regulations have been implemented to reduce direct contact between wild and farmed cervids; however, evidence suggests that indirect contact to infectious prions passed through the alimentary tracts of scavengers may be an important transmission pathway. The objective of this study was to characterize mammalian scavenger and wild deer activities associated with deer farms and link these activities with site-specific spatial covariates utilizing a network of camera traps, mounted to farm perimeter fences. We monitored each of 14 farms in Minnesota, Wisconsin, and Pennsylvania for two weeks during the summer, with a subset of farms also monitored in the winter and fall. Across all sites and seasons, we captured 749 observations of wildlife. In total, nine species were captured, with wild white-tailed deer accounting for over three quarters of observations. Despite the large number of wild deer observed, we found that interactions between wild and farmed deer at the fence line were infrequent (six direct contacts observed). In contrast, mammalian scavengers were frequently observed inside and outside of the fence. Supplementary cameras placed on deer feeders revealed higher observation rates of scavengers than those placed along fence lines, highlighting the potential for transmission of CWD through indirect contact via scavenger excreta. To evaluate associations between the number of observations of focal species with land-cover characteristics, two mixed-effects regression models were fitted, one model for scavengers and one for wild deer. Contrary to our hypothesis, landscape context did not have a strong impact on wildlife visitation. This suggests that farm location is less important than management practices, highlighting the need for future research into how farming practices impact rates of wildlife visitation onto cervid farms.
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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 | 0.000 | 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) | 0.001 | 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".