Determinants of Future Microbial Food Safety in <scp>C</scp>anada for risk communication
Bibliographic record
Abstract
Abstract This paper investigates the factors that are affecting food safety in Canada today, and those that will become increasingly important in the future. The tools used to complete this analysis are primarily the review of scientific and “gray” literature, and the analysis of multiple sources of data. We develop a methodology for ranking the factors and rank the factors according to their predicted effect on foodborne disease in Canada. The analysis reveals the top three factors that will be detrimental to food safety as pathogen evolution, increase in temperatures and increase in extreme weather events. Future studies may benefit from an analysis of factors by commodity. Practical Applications Food regulatory bodies, such as the Canadian Food Inspection Agency, have a finite number of resources to address emerging food safety risks. A framework for ranking factors effecting food safety discussed in this paper will help determine the optimal distribution of resources designated for preventative and mitigative food safety programs and can better assist food regulators in anticipating emerging systemic risks. Although the focus of this paper is on the Canadian context, many of the results may be applied to other Western countries.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".