Prevelance of Listeria in produce
Bibliographic record
Abstract
Background: Fruits, vegetables, and ready to eat processed produce are vulnerable to bacteria contamination during production, harvesting, transportation, packaging, and distribution. Agencies like the Canadian Food Inspection Agency (CFIA), and the Food and Drug Administration (FDA) regulate and create legislative policies to ensure the food is safe for public consumption. When a product does not meet CFIA or FDA regulations or if the product is tested positive to biological, chemical, or physical contamination the product will be recalled. The main objective of this study is to determine if Listeria monocytogenes recalls in produce have increased over the last ten years. Listeria is a food-borne pathogen that is often overlooked and underreported. The diagnosis of Listeriosis can be difficult because symptoms can take up to 70 days to surface. Despite this, it is responsible for 41% of food-borne deaths in Canada. Methods: To determine if Listeria has increased over the last ten years, food recalls were collected from the CFIA, FDA, Food and Safety Inspection Service (FSIS), and Health Canada websites and recorded in Microsoft Excel. All food recalls were counted and analyzed using a one-tailed T-Test conducted in NCSS. Results: The study concluded that produce recalls due to the pathogen Listeria have increased by 60% over the last ten years. During the years of 2016 to 2018 the top pathogen responsible for food recalls was Listeria followed by the pathogens Salmonella and Escherichia coli. The study also concluded that total food recalls during the years of 2016 to 2018 was 45% higher than ten years ago. Conclusion: The results of this study could indicate that there is a need to increase traceability by obtaining produce through approved sources. This could allow for stricter policies, programs, and legislation regarding the use of irrigation water during production and identify breakdowns in sanitation procedures during processing and distribution.
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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 | 0.001 | 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 | 0.000 | 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".