Bibliographic record
Abstract
Small bats hibernating in humid areas of North America are in big trouble as a result of deadly white-nose syndrome. This fungal infection, caused by cold-growing fungus Pseudogymnoascus destructans, is now one of the fastest spreading wildlife diseases. Researchers have acted quickly to understand this disease and have already identified that the fungus infects the skin of bats during cold-weather hibernation. The infection causes bats to arouse from hibernation more frequently than uninfected bats. These metabolically costly arousals make bats consume their over-wintering energy stores more rapidly, causing them to die from emaciation and starvation. The true puzzle is that white-nose syndrome does not affect all bats equally. Certain bat species in North America are suffering high death rates, while others only experience mild or no mortality, and bat species from Europe appear to survive hibernation even with the infection.Pseudogymnoascus destructans has already killed over a million bats in North America since its accidental introduction in 2007, and researchers are struggling to identify what traits of the fungus (the pathogen), the affected bat species (the hosts) and the hibernaculum (the environment) create the ‘perfect storm’ of circumstances to increase bat mortality. David Hayman of the Hopkirk Research Institute at Massey University, New Zealand, and his colleagues wanted to identify the factors that leave some species unaffected, while others quickly perish.The team assembled information about the habitat and hibernation conditions of two North American bat species (the highly impacted little brown bat and the less impacted big brown bat) and two European bat species of similar sizes (the serotine bat and the greater mouse-eared bat), which they then incorporated into a computational model to predict fungal growth over a range of bat body temperatures in environments with different humidities. They added this information to models that calculate bat metabolism at various hibernating temperatures to ascertain how the bats consume their energy reserves. They were then able to predict bat survival over a range of hibernation durations and habitats that they are known to occupy.The models predicted that bats with small body sizes – similar to those of the little brown bat – that hibernate in more humid and warm caves would succumb to the disease faster and more often than larger bat species from drier caves. They revealed that the fungus grows fastest in humid conditions and that smaller bats, which have fewer energy reserves to waste on frequent arousals, will be most affected by the disease. In fact, the models were impressively accurate, reproducing the pattern of mortality seen in North America (high to low mortality) and Europe (low to no mortality).Together, Hayman and his colleagues have solved an important part of the white-nose syndrome puzzle, showing that the humidity of the hibernating environment is an important determinant of disease progression. They conclude that their results present ‘a bleak picture’ for small-bodied North American bats. More broadly, they show that understanding interactions between the disease triad – the pathogen, the host and the environment – will help us to quickly understand other deadly diseases that are spreading rapidly.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".