Temporal Study of Salmonella enterica Serovars Isolated from Environmental Samples from Ontario Poultry Breeder Flocks between 2009 and 2018
Bibliographic record
Abstract
This study’s goal was to determine the prevalence, temporal trends, seasonal patterns, and temporal clustering of Salmonella enterica isolated from environmental samples from Ontario’s poultry breeding flocks between 2009 and 2018. Clusters of common serovars and those of human health concern were identified using a scan statistic. The period prevalence of S. enterica was 25.3% in broiler breeders, 6.4% in layer breeders, and 28.6% in turkey breeders. An overall decreasing trend in S. enterica prevalence was identified in broiler breeders (from 27.8% in 2009 to 22.1% in 2018) and layer breeders (from 15.4% to 4.9%), while an increasing trend was identified in turkey breeders (from 12.0% to 24.5%). The most common serovars varied by commodity. Among broiler breeders, S. enterica serovars Kentucky (42.4% of 682 submissions), Heidelberg (19.2%), and Typhimurium (5.4%) were the most common. Salmonella enterica serovars Thompson (20.0% of 195 submissions) and Infantis (16.4%) were most common among layer breeders, and S. enterica serovars Schwarzengrund (23.6% of 1368 submissions), Senftenberg (12.9%), and Heidelberg and Uganda (9.6% each) were most common among turkey breeders. Salmonella enterica ser. Enteritidis prevalence was highest in submissions from broiler breeders (3.7% of 682 broiler breeder submissions). Temporal clusters of S. enterica serovars were identified for all poultry commodities. Seasonal effects varied by commodity, with most peaks occurring in the fall. Our study provides information on the prevalence and temporality of S. enterica serovars within Ontario’s poultry breeder flocks that might guide prevention and control programs at the breeder level.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".