Identification and selection of food safety‐related risk factors to be included in the Canadian Food Inspection Agency’s Establishment‐based Risk Assessment model for Hatcheries
Bibliographic record
Abstract
Towards the continuous improvement of its inspection system, the Canadian Food Inspection Agency (CFIA) is developing an Establishment-based Risk Assessment model for Hatcheries to allocate inspection resources based on the food safety risk associated with the Canadian hatcheries falling under its jurisdiction. The objectives of the current study were to identify and select critical food safety-related risk factors that would be included in this model, with a main focus on Salmonella. A literature review was used to develop a comprehensive list of risk factors that could potentially contribute to the food safety risk attributed to Canadian hatcheries operating in all production streams (breeders, layers, broilers, turkeys, waterfowl and game birds). The development of this list used a selection process that was conducted according to the availability of data sources, the clarity of definition and the measurability of the selected risk factors. A panel of experts reviewed and adjusted the identified risk factors. A final list of 29 risk factors was generated; 20 originated from the scientific literature and nine from the expert panel. Risk factors were grouped in three clusters according to whether they pertained to the inherent risk (nine factors identified), risk mitigation (nine factors identified) or compliance of a hatchery with its preventive control plan and regulatory requirements (11 factors identified). Criteria for assessing each risk factor were defined based on common practices used in the Canadian hatchery industry. This comprehensive list of risk factors and criteria represents useful information to support the design and implementation of a Canadian risk assessment model for hatcheries, but could also be used by like-minded food safety authorities.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".