Geographical Stability of Enterococcal Antibiotic Resistance Profiles in Europe and Its Implications for the Identification of Fecal Sources
Bibliographic record
Abstract
This manuscript describes the use of a recently developed microbial source tracking (MST) technique to determine sources of fecal bacteria (enterococci) from four separate European countries. The investigation aimed to evaluate whether the origin of bacterial populations from France, Spain, and Sweden (n = 456) could be predicted using a library of antibiotic resistance patterns of enterococci of known origin assembled in the United Kingdom (n = 2739). Bacterial isolates were exposed to a battery of 31 antibiotic tests and classified into source groups using discriminant function analysis (DFA). Results showed that while on average 72% of the U.K. isolates could be correctly classified as originating from either municipal wastewater (MW), livestock, or wild birds, only 43% of non-U.K. isolates could be successfully classified into the same source categories. The results suggested that patterns of resistance amongst isolates contained in the U.K. library were not representative of those found in the other locations and that it may not be possible to share libraries over large distances, such as those in this study. Future MST studies using antibiotic resistance analysis (ARA) in Europe may therefore require the assembly of watershed specific libraries, increasing the cost of such studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".