Spatiotemporal occupancy patterns of chronic wasting disease
Bibliographic record
Abstract
Introduction Chronic wasting disease (CWD) among cervids in Kansas has seen a consistent rise over the years, both in terms of the number of infections and its geographical spread. In this study, we assessed the occupancy patterns of CWD among white-tailed deer and mule deer across the state. Methods Using surveillance data collected since 2005, we applied a dynamic patch occupancy model within a Bayesian framework, incorporating various environmental covariates. Using principal components analysis, 13 fully orthogonal components representing cervid habitat, soil, and elevation were derived. Competing models with different temporal patterns were fit, and the best model selected based on Watanabe-AIC values and AUC value of 0.89. Results The occupancy pattern produced by this model revealed a steady progression of the disease toward the east and southeast of the state. A random forest analysis of covariates at annual intervals indicated that geographic location, elevation, areas occupied by mixed forests, and several soil attributes (pH, clay content, depth to restrictive layer, available water content, and bulk density) explained most of the variability in the surveillance data (R2 = 0.96). Discussion The findings reported in this study are the first for the state of Kansas but are consistent with previous findings from other geographic jurisdictions in the US and Canada. This consistency underscores their value in designing surveillance and management programs.
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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 | 0.002 |
| 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".