Food Safety Challenges within North American Free Trade Agreement (NAFTA) Partners
Bibliographic record
Abstract
Abstract: Organizational elements and limitations influencing the effective operation of the food safety regulatory infrastructures in the United States, Canada, and Mexico are compared. Progress to improve the safety of food in North American countries is hampered by common problems, yet differences exist. Foodborne illness surveillance and reporting are most comprehensive in the United States, but it is uniformly more reactive than proactive in all 3 countries. Food safety policy is based on outbreak data, but that may be short‐sighted because these represent roughly 10% of foodborne illness cases. Food inspection in each country is done at 2 tiers (federal and other) by many agencies at 3 (federal/state‐provincial/municipal) levels. Interagency collaboration at times of crisis is weak and frequent heterogeneity in training, inspection targets, and inspection rigor affect regulatory credibility. Enhanced recognition that industry has the prime responsibility for food safety is warranted (and must not be confused with self‐inspection) along with justifiably aggressive regulatory agency interrogation of food safety system performance. End product testing should be used to verify safety system operation and should not be used to predict product safety. Specific microbial and nonmicrobial challenges to safe food in North America are highlighted and a rationalization of fiscal/human resource allocation to most effectively reduce the burden of foodborne illness is provided.
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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 | 0.030 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".