Food risk assessment in the farm-to-table continuum: report from the conference on good hygiene practices to ensure food safety
Bibliographic record
Abstract
Abstract Foodborne diseases (FBDs) are a major worldwide public health concern. In the current context of globalization, it has become crucial to establish effective collaboration between countries to reduce the incidence of FBDs, by creating knowledge-sharing activities to address this challenge. However, despite the importance of this subject, there are limited opportunities for researchers from French-speaking countries to meet and exchange expertise in this field. Researchers from the Faculty of Veterinary Medicine of the Université de Montréal (Canada) and from the Faculty of Science, University of Abdelmalek Essaadi (Morocco) took the initiative to organize the first French-speaking edition of the conference on Good Hygiene Practices to Ensure Food Safety, that was held virtually on May 25 and 26, 2022. Attendees (n = 122) came from academic, food processing and government sectors. The conference was a great opportunity to showcase the practical application of the risk analysis paradigm, with concrete examples of food hazards, as well as the use of the latest high-throughput sequencing technologies as a tool for source attribution and molecular typing of some of the most important foodborne pathogens. In addition, the conference created a valuable forum for the exchange of knowledge between international food safety experts, particularly with respect to Canadian regulations compared with those of other countries. Interestingly, following the success of this first edition, the conference’s scientific committee has decided to continue organizing this event on a biannual basis, to provide a unique forum for French-speaking researchers to learn about the latest advances in food safety.
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 | 0.029 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".