Predicting fungal infection rate and severity with skin‐associated microbial communities on amphibians
Bibliographic record
Abstract
Pathogen success (risk and severity) is influenced by host-associated microbiota, but the degree to which variation in microbial community traits predict future infection presence/absence (risk) and load (severity) for the host is unknown. We conducted a time-series experiment by sampling the skin-associated bacterial communities of five amphibian species before and after exposure to the fungal pathogen, Batrachochytrium dendrobaditis (Bd). We sought to determine whether microbial community traits are predictors of, or are affected by, Bd infection risk and intensity. Our results show that richness of putative Bd-inhibitory bacteria strongly predicts infection risk, while the proportion of putative Bd-inhibitory bacteria predicts future infection intensity. Variation in microbial community composition is high across time and individual, and bacterial prevalence is low. Our findings demonstrate how ecological community traits of host-associated microbiota may be used to predict infection risk by pathogenic microbes.
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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 | 0.001 |
| 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".