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Record W2898706657 · doi:10.1242/jeb.170274

Enemy at the bat cave door

2018· article· en· W2898706657 on OpenAlexaffabout
Oana Birceanu

Bibliographic record

VenueJournal of Experimental Biology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsThreatened speciesBiological dispersalBiologyEcologyFungusDozenBeakZoologyBotanyPopulation

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.278
Threshold uncertainty score0.930

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0080.002
Scholarly communication0.0040.004
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.2780.082

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.

Opus teacher head0.026
GPT teacher head0.276
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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