Climate-sensitive biological and chemical preharvest food safety hazards in Canadian agriculture: A scoping review
Bibliographic record
Abstract
Climate change poses risks to food safety at the preharvest level. Synthesized high-quality evidence on the impacts of meteorological variables —temperature, precipitation, humidity, and extreme weather—on food contamination is essential for informing food safety policy and interventions. This scoping review aimed to synthesize peer-reviewed and grey literature on these effects and identify knowledge gaps. Using a registered a priori protocol, searches were conducted in MEDLINE via Ovid, Web of Science, AGRICOLA, and CAB International and grey literature sources. Two independent reviewers conducted a two-phase screening process on retrieved literature to identify eligible studies that examined meteorological variable impacts on preharvest food contamination specifically in Canada, the United States, or Europe. A total of 45 studies were included, with data extracted and synthesized. This review identified the impacts of meteorological variables on food safety hazards in grains (16/45), livestock (12/45), produce (10/45), and irrigation water (8/45). In grains, changes in precipitation, temperature, and humidity were strongly interconnected and linked with increased mycotoxin contamination. Seasonal changes and higher temperatures elevated biological hazards among livestock. Produce contamination, notably in leafy green vegetables , increased with higher temperatures, precipitation, and flood events. Irrigation water sources demonstrated increased contamination following increased precipitation, primarily. These findings highlight the critical influence of meteorological variables on preharvest food safety , underscoring the need for targeted mitigation and adaptation strategies to safeguard food systems in the face of climate change.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.029 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".