Summary of the AOAC-Sponsored Workshop Series Related to the Global Understanding of Food Fraud (GUFF): Mobilization of Resources for Food Authenticity Assurance and Food Fraud Prevention and Mitigation
Bibliographic record
Abstract
BACKGROUND: Reports of incidents associated with the misrepresentation of food products as well as the adulteration of their composition leading, at times, to significant public health impacts are being recorded. OBJECTIVE: This paper aims at summarizing the outputs of three workshops dedicated to the theme "Global Understanding of Food Fraud" (GUFF), held in Quebec City in Canada (April 2017), Beijing in the People's Republic of China (October 2017) and Dubai in the United Arab Emirates (October 2018). METHOD: Based on the contributions made at these workshops, the paper reviews current knowledge related to food fraud shared by experts and stakeholders representing the food industry sector, food regulators both domestically and internationally and scientists from Academia. It also discusses approaches available to the industry across the food supply chain to predict, prevent, and possibly mitigate food fraud, inclusive of targeted and non-targeted methods of analysis. RESULTS AND CONCLUSIONS: The paper offers a discussion on areas warranting the mobilization of efforts and resources of the food stakeholder community to reach consistent and accessible guidance on food fraud prevention, validated analytical methods along with an increased emphasis on prevention in food regulatory measures targeting food fraud. Further development is needed to reach consistent and accessible guidance on food fraud prevention, validated analytical methods, along with an emphasis on food fraud prevention. HIGHLIGHTS: Food fraud is receiving increased attention from consumers, regulators, and industry. International food fraud experts were invited to three workshops. Contributions and conclusions from the workshops are reported and discussed.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".