The impacts of animal agriculture on One Health—Bacterial zoonosis, antimicrobial resistance, and beyond
Bibliographic record
Abstract
The industrialization of animal agriculture has undoubtedly contributed to the improvement of human well-being by increasing the efficiency of food animal production. At the same time, it has also drastically impacted the natural environment and human society. The One Health initiative emphasizes the interdependency of the health of ecosystems, animals, and humans. In this paper, we discuss some of the most profound consequences of animal agriculture practices from a One Health perspective. More specifically, we focus on impacts to host-microbe interactions by elaborating on how modern animal agriculture affects zoonotic infections, specifically those of bacterial origin, and the concomitant emergence of antimicrobial resistance (AMR). A key question underlying these deeply interconnected issues is how to better prevent, monitor, and manage infections in animal agriculture. To address this, we outline approaches to mitigate the impacts of agricultural bacterial zoonoses and AMR, including the development of novel treatments as well as non-drug approaches comprising integrated surveillance programs and policy and education regarding agricultural practices and antimicrobial stewardship. Finally, we touch upon additional major environmental and health factors impacted by animal agriculture within the One Health context, including animal welfare, food security, food safety, and climate change. Charting how these issues are interwoven to comprise the complex web of animal agriculture's broad impacts on One Health will allow for the development of concerted, multidisciplinary interventions which are truly necessary to tackle these issues from a One Health perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".