The use of disinfectant in barn cleaning alters microbial composition and increases carriage of <i>Campylobacter jejuni</i> in broiler chickens
Bibliographic record
Abstract
Abstract To maintain food safety and flock health in broiler chicken production, biosecurity approaches to keep chicken barns free of pathogens are important. Canadian broiler chicken producers must deep clean their barns with chemical disinfectants at least once annually (full disinfection; FD) and may wash with water (water-wash; WW) throughout the year. However, many producers use FD after each flock, assuming a greater efficacy of more stringent cleaning protocols, although little information is known regarding how these two cleaning practices affect pathogen population and gut microbiota. In the current study, a cross-over experiment over four production cycles was conducted in seven commercial chicken barns to compare WW and FD. We evaluated the effects of barn cleaning method on the commercial broiler performance, cecal microbiota composition, pathogen occurrence and abundance, as well as short-chain fatty acid concentrations in the month-old broiler gut. The 30-day body weight and mortality rate were not affected by the barn cleaning methods. The WW resulted in a modest but significant effect on the structure of broiler cecal microbiota (weighted-UniFrac; adonis p = 0.05, and unweighted-UniFrac; adonis p = 0.01), with notable reductions in Campylobacter jejuni occurrence and abundance. In addition, the WW group had increased cecal acetate, butyrate and total short-chain fatty acid concentrations, which were negatively correlated with C. jejuni abundance. Our results support the use of WW over FD to enhance the activity of the gut microbiota and potentially reduce zoonotic transmission of C. jejuni in broiler production without previous disease challenges.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".