White-Nose Syndrome-Affected Litttle Brown Myotis (Myotis Lucifugus) Increase Grooming and Other Active Behaviors During Arousals from Hibernation
Bibliographic record
Abstract
White-nose syndrome (WNS) is an emerging infectious disease of hibernating bats linked to the death of an estimated 5.7 million or more bats in the northeastern United States and Canada. White-nose syndrome is caused by the cold-loving fungus Pseudogymnoascus destructans (Pd), which invades the skin of the muzzles, ears, and wings of hibernating bats. Previous work has shown that WNS-affected bats arouse to euthermic or near euthermic temperatures during hibernation significantly more frequently than normal and that these too-frequent arousals are tied to severity of infection and death date. We quantified the behavior of bats during these arousal bouts to understand better the causes and consequences of these arousals. We hypothesized that WNS-affected bats would display increased levels of activity (especially grooming) during their arousal bouts from hibernation compared to WNS-unaffected bats. Behavior of both affected and unaffected hibernating bats in captivity was monitored from December 2010 to March 2011 using temperature-sensitive dataloggers attached to the backs of bats and infrared motion-sensitive cameras. The WNS-affected bats exhibited significantly higher rates of grooming, relative to unaffected bats, at the expense of time that would otherwise be spent inactive. Increased self-grooming may be related to the presence of the fungus. Elevated activity levels in affected bats likely increase energetic stress, whereas the loss of rest (inactive periods when aroused from torpor) may jeopardize the ability of a bat to reestablish homeostasis in a number of physiologic systems.
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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".