Identifying the impact of climate variables on biological and chemical contaminants of preharvest foods and their associated food safety risks: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Foodborne and waterborne illnesses affect over four million Canadians annually and pose a preventable burden on the nation's healthcare system. Climate change can increase the risk of such illnesses by increasing the likelihood of exposure to contaminants. As climate change progresses, it is imperative to better understand its impact on the dissemination of foodborne and waterborne contaminants throughout the food system. Currently, there is limited, synthesised evidence for how future changes in Canada's climate may affect the risk of contamination of preharvest foods. The aim of this research is to collate and describe available information on effects of climate variables on biological and chemical contamination of preharvest foods in Canada. This information will contribute to improved understanding of climate change impacts and potential adaptation and mitigation strategies to increase climate resiliency in Canada's food system. METHODS: A preliminary search of MEDLINE, Web of Science and Google was conducted to verify the absence of existing reviews and to inform the development of this review protocol. Information will be identified by searching four academic databases: MEDLINE via Ovid, AGRICultural OnLine Access (AGRICOLA), CAB International and Web of Science. This search will be supplemented by a targeted grey literature search. The search strategy includes index terms and keywords for Canada-relevant foodborne and waterborne pathogens and chemical contaminants, preharvest foods and climate change. Search results will be managed using Covidence during all phases of the review, conducted by two independent reviewers. Data will be extracted, synthesised and presented using graphical and tabular formats.This scoping review protocol describes the process for retrieving a comprehensive set of evidence for how climate change variables may increase risk of biological or chemical contamination of preharvest foods in Canada. This review will provide decision-makers with a detailed understanding of climate variable-preharvest food-contaminant combinations using the best available evidence. ETHICS AND DISSEMINATION: Ethical considerations are not applicable to this protocol as scoping reviews conduct secondary data analysis that synthesises data from publicly available sources. The results from this review will be disseminated through a peer-reviewed publication and conference presentation.
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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 | 0.093 | 0.109 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.051 | 0.011 |
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".