Seasonality and zoonotic foodborne pathogens in Canada: relationships between climate and <i>Campylobacter, E</i>. <i>coli</i> and <i>Salmonella</i> in meat products
Bibliographic record
Abstract
Infections due to Campylobacter, Escherichia coli and Salmonella pose a significant health burden in Canada, resulting in major costs to the health care system and economic impacts due to lost productivity resulting from illness. Recent literature suggests that climate may play a role in the prevalence of these pathogens along the food chain. This study used integrated surveillance data to examine associations between weather variables, serving as a proxy for climate, in agricultural areas and Campylobacter, generic E. coli and Salmonella contamination on samples of beef, poultry and swine meat products in Canada. Various temperature metrics (average, maximum and variability) were correlated with Campylobacter prevalence along the food chain. The prevalence of E. coli and Salmonella was correlated with both precipitation and temperatures metrics; however, analysis for E. coli was limited to beef and swine meats at retail settings, because prevalence in other combinations approached 100%, which obviated further analysis. Campylobacter contamination in poultry and swine at abattoir and retail settings demonstrated a seasonal trend, with increased prevalence generally from June or July through November, compared to the baseline month of December. Based on these analyses, Campylobacter is the most likely foodborne bacteria studied whose occurrence in meat products is affected by climatic changes in Canada. An exploratory analysis of data at the provincial scale, using Ontario as an example, revealed similar directional relationships between climate and bacterial prevalence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".