Review of National Food Control Plans in Australia, Canada, New Zealand and United States
Bibliographic record
Abstract
Background We commissioned Campden BRI to complete a desk study reviewing and comparing the sampling systems of four countries of interest: Australia, Canada, New Zealand and the United States. This report aims to provide a qualitative assessment of how competent authorities in each of these jurisdictions perform sampling and analysis of food and feed, their systems for gathering intelligence and other information which informs the need and structure of any sampling and testing programme. Research approach The aims of the project were addressed by systematically reviewing for each country: • The underpinning legislative and regulatory basis • How official controls and surveys are performed together with methodologies adopted • Intelligence gathering together with hypothesis generation and testing • Use of third-party data (for example, generated by food or feed businesses) to provide leverage to quality of outputs from regulatory activities The objectives were addressed in a three-stage process: • Web-based literature review • Interviews with national representatives • Review of information in the scientific and technical literature Results The outcome of the study suggests that there is no one size that fits all as considerable differences in terms of planning and conducting various sampling activities were observed between the four countries reviewed and sometimes even between the authorities within the same country.
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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 | 0.041 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.042 | 0.048 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".