Bibliographic record
Abstract
TV shows such as ‘Dracula’ and the ‘Batman’ series have made bats famous over the years. We all know that they are the only mammals adapted for flight, but did you know that some plants depend solely on bats for pollination and seed dispersal; or that they consume so many insect pests in a few hours that they reduce the need for insecticides? Bats are true engineers of their environment, but the populations in North America are currently threatened by an outside enemy: the European fungus, Pseudogymnoascus destructans, which causes white nose syndrome, leading to death. How the fungus spreads and adapts to new environmental challenges (e.g. colder or warmer climate) has not been studied extensively.With this in mind, Adrian Forsythe, a PhD student at McMaster University in Hamilton, Canada, along with a team of colleagues from the same institution, have investigated how well the white-nose syndrome fungus adapts to varying temperatures, and how far and how fast it can spread in new regions that are warmer or cooler. Over the course of 5 years (2008–2013), the team collected samples of the fungus from bats in different locations across North America. Analysing the fungus colony samples, the authors were able to establish that they all varied in terms of colony size, colour and how the pigment was distributed. The group examined colony size because it is an indicator of the ability of the fungus to obtain nutrients from the environment and reproduce, but they also looked at pigmentation as an indicator of resistance to environmental challenges and the ability of the fungus to spread rapidly.Comparing the new samples with the very first sample that was collected when the infection was initially identified in North America, 10 years ago in New York State, it was clear that the fungus had changed (or had adapted) to each specific location. The authors found that the colony size and pigmentation varied significantly the further the sample was from the original infection site, meaning that the fungus was adapting to its new environment; it was coping well with new environmental challenges and it was spreading at an alarming rate. To further test the resilience of the fungus, the research team picked four samples representing the differences in colony size and pigmentation within all groups and exposed them to 4°C, 13°C and 23°C for varying lengths of time, after which they looked at growth and genetic differences in the fungi. The team found that different strains of white-nose syndrome fungus from different locations preferred different temperatures and grew at different rates, but the most interesting finding was the presence of genetic mutations among the fungal groups, suggesting that it is adapting rapidly to its environment.Bats are crucial for the health of ecosystems, from hunting harmful insects, such as mosquitos, to plant pollination and providing fertilizer for agriculture; a world without bats would be catastrophic. Through their work, Forsythe and his team have provided additional information on the spread of white-nose syndrome fungus and on the factors that influence and contribute to its rapid adaptation to the North American climate, but more research is needed to better understand and prevent its spread, which seems to have North American bats cornered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.278 | 0.082 |
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".