Megatrends and emerging issues: Impacts on food safety
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
The world is changing at a pace, driven by global megatrends and their interactions. Megatrends, including climate change, the drive for sustainability, an aging population, urbanization, and geopolitical tensions, are producing an increasingly challenging environment for the provision of a safe and secure food supply. To ensure a robust, safe, and secure food supply for all, potential food safety impacts associated with these megatrends need to be understood, and mitigation and management plans must be implemented. This paper outlines the relevant megatrends, discusses their potential impact on food safety, and suggests steps to help ensure the production of safe food in the future. Megatrends are increasingly driving resource depletion, reducing the vitality of plants and animals, increasing the geographical spread of animal and plant pathogens, increasing the risk of mycotoxins, agrichemical residues, and antimicrobial-resistant pathogens contaminating foods, and threatening to destabilize food systems and the food regulatory network. Science-based actions, adopting continual and dynamic risk assessments, alongside the use of more sensitive and accurate methods for the detection of contaminants, may counter these challenges. The use of artificial intelligence, robotics and automation, the enhancement of food safety cultures, the continued education and training of workforces, and the implementation of risk-based food regulations will help ensure preventative controls are in place. As low-income countries and smallholder farmers are more likely to be exposed to the impact of these megatrends and less likely to have resources to counter them, geographical social inequality, unrest, and population migration are likely to be exacerbated unless urgent action is taken.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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