Modeling the suitability of Texas karst regions for infection by <i>Pseudogymnoascus destructans</i> in bats
Bibliographic record
Abstract
Abstract White-nose syndrome (WNS) is caused by the fungus Pseudogymnoascus destructans and has led to the deaths of millions of North American bats since it was first documented in New York in 2006. Since the first cases were recorded, WNS has spread rapidly across North America and is now confirmed or suspected in 40 US states and seven Canadian provinces. Often, the presence of P. destructans is detected in a cave or hibernaculum before signs of WNS manifest in the resident bat population, making presence of the fungus a more reliable assessment of potential epidemic spread than expansion of manifested WNS. An analysis of 43 cave internal climates across the state of Texas revealed a pattern of thermal suitability for P. destructans that correlated significantly with landscape (elevation, lithology) and external climate (mean surface temperature and precipitation). We generated a predictive model to assess the potential spread of P. destructans through Texas karst systems based on external features that correlate with suitable internal climates for fungal growth. Applications of this model to external climatic variables from 2019 showed seasonally varying patterns of suitability for fungal growth in select regions of Texas karst systems. Results from these surveys and models showed that internal climates of Texas caves are likely able to sustain the growth of P. destructans and could cause disease and resulting declines in Texas bats, and act as stepping-stones for the fungus, allowing it to travel southward into Mexican and Central American cave systems. The resulting work will inform researchers and natural resource managers of areas of significant concern to monitor for the spread of WNS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".