Identifying patterns of chronic wasting disease prevalence in Missouri's Odocoileus virginianus population
Bibliographic record
Abstract
[EMBARGOED UNTIL 12/1/2023] Chronic Wasting Disease (CWD) is a deadly and infectious neurodegenerative disease caused by a normal host protein, the cellular prion protein (PrPC), that takes on an abnormal complexity and is found in white-tailed deer and other members of Family Cervidae in the United States, Canada, and now Europe. With the potential to cause irreversible population declines, understanding how population dynamics or landscape composition plays a role in the transmission and dispersal of CWD is detrimental. We created generalized linear models (GLM) and principal component analysis (PCA), using a 10-year dataset created and provided by the Missouri Department of Conservation (MDC), to investigate how sex, life stage, and landcover type affects CWD prevalence in two landscape-varying zones in Missouri. Our results showed that fawns and male white-tailed deer were the best predictors of CWD prevalence in both zones and the state of Missouri as a whole. Our landcover analysis of the state of Missouri also displayed cropland and wetland as the two most significant landcover types affecting CWD prevalence. With help from Missouri private landowners, MDC staff, and other stakeholders, a larger data set and further investigation into the effects of landscape composition, fawns, and male white-tailed deer on CWD prevalence could make ground-breaking discoveries and help protect an abundant Cervid species that is the states' most valuable natural resource.
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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 | 0.000 |
| 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".