THE HEALTH AND ECONOMIC THREATS OF GLOBAL FOOD RECALLS AND THE GROWING NATIONAL AND INTERNATIONAL EFFORTS TO ADVANCE FOOD SAFETY
Bibliographic record
Abstract
Background and Aims: The global burden of food borne disease and its impact on trade and development is currently unknown. On the other hand, the global recalls of food is increasing, contributing to the risk of pathogens across national borders creating new challenges to food safety regulators. The study examines the public health and economic threats of global food recalls and efforts being made by national and international agencies to advance food safety. Methods: Data collection from multiple sources on food recalls and review of selected national and international initiatives to advance food safety. Results: Based on the number of recalls reported globally, there has been a substantial increase in the past years. Salmonella accounts for the majority cases of food borne illnesses followed by undeclared allergens, and intolerances, E.coli, Listeria, Monocytogens and Clostridium botulism. The main foods that were subject to recall were dairy products, seafood, poultry including eggs, fruits and vegetables, nuts and raw agricultural commodities. Greater number of recalls were reported by United States, Canada, Australia and a few European and Asian countries. Most of the recalled foods originated from China, Mexico, North America and a few Asian and African countries. Two major initiatives laid the foundation to strengthen the food safety system globally. These were the “Food Safety Modernization Act” passed by the U.S senate in January this year and the WHO resolution on “Advancing Food Safety” endorsed by member states in May last year. Conclusions: There is a continued need for closer collaboration among stakeholders to support measures and interventions in the global implementation of the initiatives to advance and modernize food safety. The important roles WHO and FAO play in support of the Codex Alimentarious Commission and the participation of developing countries should be given active consideration.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".