Quantification of temperature fluctuations in restaurant coolers and modelled Listeria monocytogenes growth
Bibliographic record
Abstract

 Background: Coolers in food service establishments should ideally operate at 4°C or less. However in restaurant environments cooler doors are continually being opened and closed as food workers gather and store items. These actions may lead to temperature fluctuations in coolers which may pose a health risk towards the storage of potentially hazardous foods. This study measured and analyzed temperature fluctuations in coolers and quantified the risk they presented by modelling Listeria monocytogenes growth in response to these temperatures. Method: ACR Systems Inc. Smart Buttons were placed near the opening of restaurant coolers and recorded temperatures over a 1-week span. Food Spoilage and Safety Predictor (FSSP) was used to model L. monocytogenes growth in response to the collected cooler temperatures. Results: Coolers spend significantly less than 50% of the time above 4°C. The magnitude of temperature fluctuations during open business hours was found to be insignificant in comparison to fluctuations during closed business hours. However, fluctuations were significantly greater in reach-in coolers than in walk-in coolers. With respect to modeled L. monocytogenes growth, it was inconclusive on whether growth would be more or less than Health Canada’s 100cfu/g policy in smoked salmon. However growth was significantly less than this limit in ready-to-eat ham. Conclusions: More restaurant coolers need to be analyzed to confirm whether the defrost cycles of coolers have a greater impact on temperature fluctuations above 4°C than the daily activities of staff members. In addition, more coolers need to be analyzed to determine whether L. monocytogenes growth in smoked salmon stored in coolers for a week grow significantly more than 100cfu/g. However, it can be concluded L. monocytogenes growth will be significantly less than 100cfu/g in ready-to-eat ham and will pose a lower risk for listeriosis than smoked salmon.
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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".