Concentrations of Campylobacter spp., Escherichia coli, Enterococci, and Yersinia spp. in the feces of farmed red deer in New Zealand
Bibliographic record
Abstract
Intensive deer farming can cause environmental issues, mainly by its impact on soils and water quality. In particular, there is a risk to the microbial quality of water, as high quantities of suspended sediment and fecal bacteria can enter into water systems. The feces of farmed red deer (Cervus elaphus, n = 206) from Canterbury and Southland, New Zealand, were analyzed with regard to the presence of Campylobacter spp., Escherichia coli, enterococci, and Yersinia spp. Enterococci and E. coli were isolated from all samples, with mean concentrations of 4.5 x 105 (95% CI 3.5 x 10(3), 5.6 10(7)) and 1.3 x 10(8) (95% CI 1.1 x 10(6), 1.5 x 10(10)) per gram of dry feces, respectively. Campylobacter spp. were isolated from 27 fecal samples, giving an overall prevalence of 13.1%. Campylobacter isolation rates were variable within and between regions (Canterbury 7.95% [95% CI 2-14%], Southland 16.95% [95% CI 10-24%]). Five out of 42 composite samples were positive for Yersinia enterocolitica, and one sample for Y. pseudotuberculosis. The overall prevalence ranges on a per-animal basis were therefore 2.43 to 11.17% and 0.49 to 2.91%, respectively. This study is the first to quantify the concentration of Campylobacter spp. present in healthy deer farmed in New Zealand. Deer feces are a potential source of human campylobacteriosis, with all genotypes isolated also previously observed among human cases. The fecal outputs from deer should be regarded as potentially pathogenic to humans and therefore be appropriately managed.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".