Enterococci as a One Health indicator of antimicrobial resistance
Bibliographic record
Abstract
The rapid increase of antimicrobial-resistant bacteria in humans and livestock is concerning. Antimicrobials are essential for the treatment of disease in modern day medicine, and their misuse in humans and food animals has contributed to an increase in the prevalence of antimicrobial-resistant bacteria. Globally, antimicrobial resistance is recognized as a One Health problem affecting humans, animals, and environment. Enterococcal species are Gram-positive bacteria that are widely distributed in nature. Their occurrence, prevalence, and persistence across the One Health continuum make them an ideal candidate to study antimicrobial resistance from a One Health perspective. The objective of this review was to summarize the role of enterococci as an indicator of antimicrobial resistance across One Health sectors. We also briefly address the prevalence of enterococci in human, animal, and environmental settings. In addition, a 16S RNA gene-based phylogenetic tree was constructed to visualize the evolutionary relationship among enterococcal species and whether they segregate based on host environment. We also review the genomic basis of antimicrobial resistance in enterococcal species across the One Health continuum.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".