OCCURRENCE AND ASSESSMENT OF PHYSICAL CONTAMINANTS BASED ON FOOD RECALLS IN CANADA
Bibliographic record
Abstract
This paper investigates the association between types of food products, physical contaminants and year using food recalls dataset obtained from the Canadian Food Inspection Agency (CFIA) between 2014 and 2019. In the period of studies, a total of 269 foods under the category of physical hazards were recalled. Chi-square per cell test was used to deeply analyse the contingency table of the investigated topic categories. The results show that there is no association between the year and the number of food recalls by food products and year by physical contaminants type. However, the results indicated that there is an association between the food products and physical contaminants type. In particular, there were significant numbers of insects being found in grain and fruits/vegetables products with 15 and 53 cases, respectively. Plastic and bone fragments were significantly found in dairy and meat, poultry and seafood products, respectively with 9 and 15 cases. Glass was significantly found in wine and beverage (6 cases) and other food product (9 cases). Plastic material was highly detected in candy and confectionery product with 9 cases. The sources of the physical contaminants have been analysed, together with the precautionary measures that must be taken. Findings from this study provide the food industry with essential information. An understanding and analysis of physical hazards is critical for companies in order to restructure their food safety policies and technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".