The Development of a Risk Assessment Model for Inedible Rendering Plants in Canada: Identifying and Selecting Feed Safety-Related Factors
Bibliographic record
Abstract
The Canadian Food Inspection Agency (CFIA) is developing an establishment-based risk assessment model to categorize rendering plants that produce livestock feed ingredients (ERA-Renderer model) according to animal and human health risks (i.e., feed safety risks) and help in determining the allocation of inspection resources based on risk. The aim of the present study was to identify and select feed-safety-related factors and assessment criteria for inclusion in the ERA-Renderer model. First, a literature review was performed to identify evidence-based factors that impact the feed safety risk of livestock feed during its rendering processes. Secondly, a refinement process was applied to retain only those that met the inclusion conditions, such as data availability, lack of ambiguity, and measurability. Finally, an expert panel helped in selecting factors and assessment criteria based on their knowledge and experience in the rendering industry. A final list of 32 factors was developed, of which 4 pertained to the inherent risk of a rendering plant, 8 were related to risk mitigation strategies, and 20 referred to the regulatory compliance of a rendering plant. A total of 179 criteria were defined to assess factors based on practices in the Canadian rendering industry. The results of this study will be used in the next step of the model development to estimate the relative risks of the assessment criteria considering their impact on feed safety. Once implemented, the CFIA's ERA-Renderer model will provide an evidence-based, standardized, and transparent approach to help manage the feed safety risks in Canada's rendering sector.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| 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".