The influence of bat ecology on viral diversity and reservoir status
Bibliographic record
Abstract
Bats host a diversity of viruses, some zoonotic. Repeated emergence of diseases that jump into humans from bat reservoirs highlights a need for predictive approaches to pre-emptively identify virus-carrying species. We use a machine learning approach to examine drivers of viral diversity in bats, and differences in those drivers between RNA and DNA viruses. We find bat species with longer lifespans, broad geographic distributions in the eastern hemisphere, and large group sizes carry more viruses. Lifespan was a stronger predictor of DNA viral diversity, while group size and family were more important for RNA viruses, patterns that may reflect broad differences in infection duration. Finally, we identify 55 bat species not currently considered reservoirs that are most likely to carry viruses. Mapping these predictions highlights global regions that could be targeted for disease surveillance, including those with few bat species but a large proportion of predicted carriers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".