A FoodSafeR perspective on emerging food safety hazards and associated risks
Bibliographic record
Abstract
The recently launched FoodSafeR initiative is a cooperative and coordinated approach to the identification, assessment, and management of emerging food security challenges and associated risks—both chemical and microbial. The FoodSafeR consortium includes global stakeholders across governmental, inter-governmental, academic and industrial institutions involved in food safety, research, and production. Consortium members have led in-depth discussions on identifying, assessing and managing chemical and microbial food safety issues resulting from climate change, emerging microbial and chemical contaminants, and evolving dietary preferences. Food safety research often is episodic in nature, increasing after a crisis and then decreasing when there are no major problems. Timely communications about and a central source containing data on previous outbreaks were identified as crucial issues to reduce the harm that could result from a food safety issue. In the course of the discussions, both new and old microbial and chemical hazards were identified for inclusion in a central database. The database could be used to develop artificial intelligence (AI) models to explain existing and predict emerging food safety risks. The FoodSafeR hub continuously collects and merges government, academic and private sector data to enable all stakeholders to better understand emerging risks, both chemical and microbial, and where they are found. As the database expands, climate change impacts on food safety can be documented and then integrated with public health data to rigorously assess the contributions of food safety to public health risks. The overall goal is to enhance global data sharing, improve food safety standards, and ensure the production of safe, accessible food for all populations thereby reducing the economic burden of foodborne illnesses, enhancing food security, and promoting sustainable food systems. The goal of this paper is to alert the global food safety community of the availability of this new resource and to provide information on the types of data it contains while encouraging others to contribute data that would broaden the information available and enable more timely and accurate identification of potential food safety issues throughout the world.
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.
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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".