Hiding in plain sight—wildlife as a neglected reservoir and pathway for the spread of antimicrobial resistance: a narrative review
Bibliographic record
Abstract
Antimicrobial resistance represents a global health problem, with infections due to pathogenic antimicrobial resistant bacteria (ARB) predicted to be the most frequent cause of human mortality by 2050. The phenomenon of antimicrobial resistance has spread to and across all ecological niches, and particularly in livestock used for food production with antimicrobials consumed in high volumes. Similarly, hospitals and other healthcare facilities are recognized as significant 'hotspots' of ARB and antimicrobial resistance genes (ARGs); however, over the past decade, new and previously overlooked ecological niches are emerging as hidden reservoirs of ARB/ARGs. Increasingly extensive and intensive industrial activities, degradation of natural environments, burgeoning food requirements, urbanization, and global climatic change have all dramatically affected the evolution and proliferation of ARB/ARGs, which now stand at extremely concerning ecological levels. While antimicrobial resistant bacteria and genes as they originate and emanate from livestock and human hosts have been extensively studied over the past 30 years, numerous ecological niches have received considerably less attention. In the current descriptive review, the authors have sought to highlight the importance of wildlife as sources/reservoirs, pathways and receptors of ARB/ARGs in the environment, thus paving the way for future primary research in these areas.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".