Evaluation of spatial variation in chronic wasting disease risk with Bayesian Poisson log-Gaussian model
Bibliographic record
Abstract
Chronic wasting disease (CWD) is a fatal neurodegenerative disease among cervids that has steadily spread across the United States and Canada. The year-to-year increase in the geographic spread of this disease among white-tailed deer and mule deer have raised concerns about conserving these species and sustainable big-game hunting. Knowledge of the spatial variation in CWD risk in Kansas, a state that attracts big game hunters nationwide is not fully understood. We explored the spatial variation in CWD risk and the potential effects of habitat-level covariates using surveillance data collected in Kansas from 2005 to 2023, with a Poisson log-Gaussian model in a Bayesian framework. Two models were considered; Model 1 included only spatial random effects and Model 2 included spatial random effects plus non-linear effects of habitat-level covariate. Following satisfactory convergence of model parameters, choropleth maps of posterior mean estimates for the risk of CWD presence, and measures of spatial heterogeneities were plotted. The impacts of the habitat-level covariates were deemed important predictors of CWD as Model 2 outperformed Model 1. The risk of CWD in the northwestern and southcentral portions of the state is likely driven by similar underlying spatial processes; however, no global smoothing effect was observed. The northwestern region is at higher risk for CWD presence but a gradual increase in risk toward the south and eastern sides of the state is apparent. We conclude that the data-driven Poisson log-Gaussian model is useful in assessing CWD and potentially other wildlife diseases from surveillance sources, and the different spatial patterns and habitat-level covariate association have relevance to CWD management in Kansas.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".