Artificial Attractants: Implications for Disease Management in Deer
Bibliographic record
Abstract
ABSTRACT Chronic wasting disease (CWD) is a prion disease that infects cervid species by direct and environmental transmission and is invariably fatal. CWD spread can be promoted by the attraction of animals to “hotspots” such as hay bales and grain bags stored in fields and at farm sites. The density and location of hotspots may impact contact rates. We used an individual‐based movement model of mule deer ( Odocoileus hemionus ) to investigate the effects of density and configuration of hotspots (hereafter artificial attractants, AA) on contact rates at a constant density of 1 deer/km 2 during winter. The model tracks when two deer from the same or different groups come into contact under 6 AA densities (0–1 AA/km 2 ) and 6 AA configurations. We compared placing AA randomly versus clustered around farms, and removing them randomly versus biased by proximity to preferred habitat. Overall, the number of unique contacts per individual and the number of unique deer visiting an AA increased, and the number of AAs used by each deer decreased as AA density declined. Selectively removing field attractants near preferred habitat resulted in a larger increase in contacts per deer, with deer contacting more and different individuals, fewer deer using the remaining AA, and fewer visits per AA than random removal. There was a greater increase in contact rates when reducing AA density at farms by randomly removing all AA at a farm compared to randomly removing individual AA across farms. Deer responses to AA removal may not be as straightforward as originally believed. Deer contacts may increase, not decrease, with AA removal because deer are attracted to the remaining AA. Under moderate deer densities, AA removal may require a broad‐scale, “all or nothing” approach to prevent deer from concentrating at remaining AA, but concomitantly lowering deer density needs further assessment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".