Recalls of Foods due to Microbial Contamination Classified by the Canadian Food Inspection Agency, 2000 to 2017
Bibliographic record
Abstract
Recall of microbial-contaminated food products is an important intervention in preventing the transmission of foodborne illness. Here, we summarize the number and nature of foods recalled as a result of microbial contamination, classified by the Canadian Food Inspection Agency, for the period 1 January 2000 through 31 December 2017. A total of 10,432 food products were recalled from 2,094 recall events in Canada because of microbial contamination during this period. The meat, meat products and poultry category, followed by fishery and seafood products and nuts and edible seeds, contained the food products most commonly associated with microbial contamination. Most microbial-contaminated food products reported were recalled because of the presence bacterial pathogens. Salmonella contamination was responsible for the largest number of recall events, whereas Listeria monocytogenes contamination accounted for the greatest number of food products recalled because of microbial contamination. L. monocytogenes contamination was also most commonly associated with major food recall events, although records may be inflated because of an invested effort to prevent future L. monocytogenes outbreaks following a 2008 deli meat recall. The findings and data we present in this study will support future surveillance and analysis of microbial-contaminated food recalls in Canada. Top microbial-contaminated food products were meat, seafood, and nuts and edible seeds. Salmonella contamination was responsible for the largest number of recall events. L. monocytogenes accounted for the largest number of recalled food products.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".