Using the microbiota to study connectivity at human–animal interfaces
Bibliographic record
Abstract
Interfaces between humans, livestock, and wildlife, mediated by the environment, are critical points for the transmission and emergence of infectious pathogens and call for leveraging the One Health approach to understanding disease transmission. Current research on pathogen transmission often focuses on single-pathogen systems, providing a limited understanding of the broader microbial interactions occurring at these interfaces. In this review, we make a case for the study of host-associated microbiota for understanding connectivity between host populations at human-animal interfaces. First, we emphasize the need to understand changes in microbiota composition dynamics from interspecies contact. Then, we explore the potential for microbiota monitoring at such interfaces as a predictive tool for infectious disease transmission and as an early-warning system to inform public health interventions. We discuss the methodological challenges and gaps in knowledge in analyzing microbiota composition dynamics, the functional meaning of these changes, and how to establish causality between microbiota changes and health outcomes. We posit that integrating microbiota science with social-ecological systems modeling is essential for advancing our ability to manage health risks and harness opportunities arising from interspecies interactions.
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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 | 0.001 | 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".