Identifying climate mitigation and adaptation strategies for protection of preharvest food safety: A rapid review protocol
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Climate change is predicted to impact the occurrence, range, and survival of biological and chemical contaminants of preharvest foods. Consequently, this projection can drive increases in food- and water-borne disease outbreaks globally. Climate-sensitive contaminants frequently occur at the preharvest stage, where foods experience higher environmental exposure and vulnerability to extreme weather events. Prevention and reduction of negative climate-related preharvest food safety risks can be facilitated through climate-specific mitigation and adaptation strategies, allowing agri-food industries globally to build climate resilience and support the production of safe foods. To our knowledge, no review has been executed to collect and synthesize global methods to mitigate and adapt to projected climate-vulnerable preharvest food safety risks. Objectives: The objective of this rapid review is to identify the extent and types of climate mitigation and adaptation strategies described in the literature and discuss how these strategies can protect preharvest foods from climate-sensitive food safety risks. Methods: Preliminary searches of CAB International (CABI), Web of Science, and Google were completed to develop a list of relevant search terms and CABI thesaurus index terms. The final list of search terms will be used to search two databases: CAB Abstracts and Web of Science. A grey literature search will also be conducted by searching Google, ProQuest Dissertations & Theses, and various relevant international and intergovernmental organizations. All retrieved articles will be uploaded and managed using Covidence. Two independent reviewers will screen the titles and abstracts of all articles, followed by a full-text screening of the selected articles. Data from the articles that pass both levels of screening will be extracted using a data extraction form. Data will be synthesized and presented using graphical and tabular formats. Discussion: This rapid review protocol describes the developed review methods utilized to ensure the comprehensive retrieval of relevant sources that identify climate mitigation and adaptation strategies proposed or implemented to prevent biological and chemical contamination of preharvest foods. Publication retrieval occurs on a global scale in this rapid review protocol; however, the results will serve as a framework for decision-makers in Canadian agri-food and public health industries to protect relevant preharvest foods in Canada, as well as the health and safety of Canadians.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it