White-nose syndrome increases torpid metabolic rate and evaporative water loss in hibernating bats
Bibliographic record
Abstract
Fungal diseases of wildlife typically manifest as superficial skin infections but can have devastating consequences for host physiology and survival. White-nose syndrome (WNS) is a fungal skin disease that has killed millions of hibernating bats in North America since 2007. Infection with the fungus Pseudogymnoascus destructans causes bats to rewarm too often during hibernation, but the cause of increased arousal rates remains unknown. On the basis of data from studies of captive and free-living bats, two mechanistic models have been proposed to explain disease processes in WNS. Key predictions of both models are that WNS-affected bats will show 1) higher metabolic rates during torpor (TMR) and 2) higher rates of evaporative water loss (EWL). We collected bats from a WNS-negative hibernaculum, inoculated one group with P. destructans, and sham-inoculated a second group as controls. After 4 mo of hibernation, TMR and EWL were measured using respirometry. Both predictions were supported, and our data suggest that infected bats were more affected by variation in ambient humidity than controls. Furthermore, disease severity, as indicated by the area of the wing with UV fluorescence, was positively correlated with EWL, but not TMR. Our results provide the first direct evidence that heightened energy expenditure during torpor and higher EWL independently contribute to WNS pathophysiology, with implications for the design of potential treatments for the disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".