Insights into antimicrobial resistance among long distance migratory East Canadian High Arctic light-bellied Brent geese (Branta bernicla hrota)
Bibliographic record
Abstract
BACKGROUND: Antimicrobial resistance (AMR) is the most significant threat to global public health and ascertaining the role wild birds play in the epidemiology of resistance is critically important. This study investigated the prevalence of AMR Gram-negative bacteria among long-distance migratory East Canadian High Arctic (ECHA) light-bellied Brent geese found wintering on the east coast of Ireland. FINDINGS: In this study a number of bacterial species were isolated from cloacal swabs taken from ECHA light-bellied Brent geese. Nucleotide sequence analysis identified five species of Gram-negative bacteria; the dominant isolated species were Pantoea spp. (n = 5) followed by Buttiauxella agrestis (n = 2). Antimicrobial susceptibility disk diffusion results identified four of the Pantoea spp. strains, and one of the Buttiauxella agrestis strains resistant to amoxicillin-clavulanic acid. CONCLUSION: To our knowledge this is the first record of AMR bacteria isolated from long distance migratory ECHA light-bellied Brent geese. This indicates that this species may act as reservoirs and potential disseminators of resistance genes into remote natural ecosystems across their migratory range. This population of geese frequently forage (and defecate) on public amenity areas during the winter months presenting a potential human health risk.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".