Studying bats using a One Health lens: bridging the gap between bat virology and disease ecology
Bibliographic record
Abstract
Accumulating data suggest that some bat species host emerging viruses that are highly pathogenic in humans and agricultural animals. Laboratory-based studies have highlighted important adaptations in bat immune systems that allow them to better tolerate viral infections compared to humans. Simultaneously, ecological studies have discovered critical extrinsic factors, such as nutritional stress, that correlate with virus shedding in wild-caught bats. Despite some progress in independently understanding the role of bats as reservoirs of emerging viruses, there remains a significant gap in the molecular understanding of factors that drive virus spillover from bats. Driven by a collective goal of bridging the gap between the fields of bat virology, immunology, and disease ecology, we hosted a satellite symposium at the 2024 American Society for Virology meeting. Bringing together virologists, immunologists, and disease ecologists, we discussed the intrinsic and extrinsic factors such as virus receptor engagement, adaptive immunity, and virus ecology that influence spillover from bat hosts. This article summarizes the topics discussed during the symposium and emphasizes the need for interdisciplinary collaborations and resource sharing.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".