Data from: Higher fat stores contribute to persistence of little brown bat populations with white-nose syndrome
Bibliographic record
Abstract
1. The persistence of populations declining from novel stressors depends, in part, on their ability to respond by trait change via evolution or plasticity. White-nose syndrome (WNS) has caused rapid declines in several North America bat species by disrupting hibernation behavior, leading to body fat depletion and starvation. However, some populations of Myotis lucifugus now persist with WNS by unknown mechanisms. 2. We examined whether persistence of M. lucifigus with WNS could be explained by increased body fat in early winter, which would allow bats to tolerate the increased energetic costs associated with WNS. We also investigated if bats were escaping infection or resistant to infection as an alternative mechanism explaining persistence. 3. We measured body fat in early and late winter during initial WNS invasion and eight years later at six sites where bats are now persisting. We also measured infection prevalence and intensity in persisting populations. 4. Infection prevalence was not significantly lower than observed in declining populations. However, at two sites, infection loads were lower than observed in declining populations. Body fat in early winter was significantly higher in four of the six persisting populations than during WNS invasion. 5. Physiological models of energy use indicated that these higher fat stores could reduce WNS mortality by 58 - 70%. These results suggest that differences in fat storage and infection dynamics have reduced the impacts of WNS in many populations. Increases in body fat provide a potential mechanism for management intervention to help conserve bat populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.230 | 0.072 |
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".