Expert Elicitation to Estimate the Relative Risk of Food Safety Criteria Included in the Establishment-Based Risk Assessment Model for Canadian Hatcheries
Bibliographic record
Abstract
The Canadian Food Inspection Agency is developing an Establishment-based Risk Assessment model for Hatcheries to allocate inspection resources according to the food safety risk associated with each hatchery falling under its jurisdiction. In a previous study, 29 factors contributing to the food safety risk of hatcheries were identified and grouped into three clusters (inherent risk, risk mitigation, and compliance) and assessment criteria were defined. The objective of the current study was to estimate the relative risk (RR) of these criteria. Two rounds of expert elicitations were conducted to allow 13 Canadian experts to estimate the RR of each criterion ( n = 96) based on its potential impact on human health, with a specific focus on Salmonella spp. This process also aimed to estimate the maximum increase or decrease in the overall food safety risk of a hatchery when considering multiple criteria belonging to a specific cluster and to assess the risk attribution of Salmonella spp. at the hatchery and bird-type levels. Results showed that the respondent profile had no influence on the importance given to a majority of criteria. Uniformity of answers among experts improved from the first to the second round. Overall, 62.5%, 32.3%, and 5.2% of the criteria were attributed to an RR that was less than 2, between 2 and 3, and greater than 3, respectively. Mixing eggs from different supply flocks when placed into the same hatching unit, hatching multiple species, and importing eggs with unknown quality status were identified as having the highest contribution to a hatchery's inherent risk. Requiring information on the foodborne pathogen status of supplying flocks and the occurrence of regulatory enforcement actions were the most impactful risk mitigation and compliance factors, respectively. The median RR value assigned to each criterion and cluster will be used to build this new model.
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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 | 0.022 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".