Identifying priority chronic wasting disease surveillance areas for mule deer in Montana
Bibliographic record
Abstract
ABSTRACT Chronic wasting disease (CWD) is a fatal prion disease that affects a variety of ungulate species including mule deer (Odocoileus hemionus). As of 2014, no CWD cases had been reported in free‐ranging ungulates in Montana. However, nearby cases in Canada, Wyoming, and the Dakotas indicated that the disease was encroaching on Montana's borders. Mule deer are native and common throughout Montana, and they represent a significant portion of the total hunter‐harvested cervids in the state. The arrival of CWD in Montana may have significant ecosystem and socioeconomic impacts as well as potential consequences for wildlife management. We used 18,879 mule deer locations from 892 individual deer collected during 1975–2011 and modeled habitat selection for 7 herds in 5 of the 7 wildlife management regions in Montana. We estimated resource selection functions (RSF) in a Bayesian framework to predict summer and winter habitat preferences for mule deer. We estimated deer abundance from flyover counts for each region, and used the RSF predictions as weights to distribute the deer across the region. We then calculated the distance to the nearest known infected herds. We predicted areas of high risk of CWD infection in mule deer as areas with densities above the median density estimate and within the lowest quartile of distances to known infected herds. We identified these areas, the southeast corner of Montana and the north‐central border near Alberta and Saskatchewan, as priority areas for CWD surveillance and management efforts. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
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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 | 0.001 | 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".