<i>Pseudogymnoascus destructans</i> invasion stage impacts the skin microbial functions of highly vulnerable <i>Myotis lucifugus</i>
Bibliographic record
Abstract
The role of the skin microbiome in resistance and susceptibility of wildlife to fungal pathogens has been examined from a taxonomic perspective but skin microbial function, in the context of fungal infection, has yet to be studied. Our objective was to understand effects of a bat fungal pathogen site infection status and course of invasion on skin microbial function. We sampled seven hibernating colonies of Myotis lucifugus covering three-time points over the course of Pseudogymnoascus destructans (Pd) invasion and white nose syndrome (pre-invasion, epidemic, and established). Our results support three new hypotheses about Pd and skin functional microbiome: (1) there is an important effect of Pd invasion stage, especially at the epidemic stage; (2) disruption by the fungus at the epidemic stage could decrease anti-fungal functions with potential negative effects on the microbiome and bat health; (3) the collection site might have a larger influence on microbiomes at the pre-invasion stage rather than at epidemic and established stages. Future studies with larger sample sizes and using meta-omics approaches will help confirm these hypotheses, and determine the influence of the microbiome on wildlife survival to fungal disease.
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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".