Surveillance and Monitoring of White-Tailed Deer for Chronic Wasting Disease in the Northeastern United States
Bibliographic record
Abstract
Abstract Chronic wasting disease (CWD) is a prion disease that affects both wild and captive cervid populations. In the past 45 y, CWD has spread from northern Colorado to all bordering states, as well as the midwestern United States (Midwest) and northeastern United States (Northeast), Canada, and South Korea. Because CWD is a relatively new issue for wildlife management agencies in the Northeast, we surveyed a representative (e.g., cervid biologist, wildlife veterinarian) from 14 states to gain a better understanding of state-specific surveillance measures. Between 2002 and 2012, New York (37,093) and Pennsylvania (35,324) tested the greatest number of harvested white-tailed deer Odocoileus virginianus in the Northeast. Additionally, the 14 states surveyed have tested 121,730 harvested deer, or approximately 15,216/y, since CWD was first detected in 2005. The most common tissues used by agencies in the Northeast for testing were retropharyngeal lymph nodes, which have been determined to be the most reliable in detecting CWD in cervids. Understanding CWD surveillance efforts at a regional scale can help to provide guidance for the development of new surveillance plans or the improvement of existing ones. Furthermore, collaborations among state and regional agencies in the Northeast may attempt to identify deficiencies in surveillance by state or subregion.
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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 | 0.001 |
| 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".