Persistent contamination of <i>Salmonella</i>, <i>Campylobacter</i>, <i>Escherichia coli</i>, and <i>Staphylococcus aureus</i> at a broiler farm in New Zealand
Bibliographic record
Abstract
Intensive poultry production due to public demand raises the risk of contamination, creating potential foodborne hazards to consumers. The prevalence and microbial load of the pathogens Campylobacter, Salmonella, Staphylococcus aureus, and Escherichia coli was determined by standard methods at the farm level. After disinfection, swab samples collected from wall crevices, drinkers, and vents were heavily contaminated, as accumulated organic matter and dust likely protected the pathogens from the disinfectants used. The annex floor also showed high microbial concentrations, suggesting the introduction of pathogens from external environments, highlighting the importance of erecting hygiene barriers at the entrance of the main shed. Therefore, pathogen control measures and proper application of disinfectants are recommended as intervention strategies. Additionally, quantitative polymerase chain reaction (qPCR) was evaluated as a quantification tool. qPCR showed limitations with samples containing low microbial counts because of the low detection limit of the method. Thus, bacterial pre-enrichment of test samples may be necessary to improve the detection of pathogens by qPCR.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".