Delivering disease
Bibliographic record
Abstract
Background and Purpose: Online food delivery services are third party entities that deliver foods from restaurants to consumers. These services are exploding in popularity around the world. The lack of regulation in this industry creates a scenario for time temperature abuse to occur. This study specifically investigates the efficacy of thermally insulated delivery bags used ubiquitously by these online food services. Methods: 30 samples of Janes Pub-Style Chicken Nuggets were cooked for 30 minutes to an internal temperature of 74°C. The nuggets were then inserted with a SmartButton temperature data logger. The nuggets were then placed into a High-Density Polyethylene take-out container. The whole set-up was then placed into a Winco Thermally Insulated Delivery Bag for a one hour period. Time and temperature of the chicken nuggets was recorded over a one hour period to reflect realistic delivery times. Results: A correlation/regression analysis was performed which showed that as the time increased so too did the temperature. Correlational coefficient, r = -0.9363, coefficient of determination, r2 = 0.8766, (p = 0.000). The equation of the line was Temperature = (67.9996) + [(-0.6828) × Time]. Temperature of the chicken nuggets fell below 60°C after 12 minutes in the delivery bag. The median temperature of chicken nuggets after a one hour period was 44.5°C. This was found to be statistically different from the standard of 60°C with a p-value of 0.0000 Conclusion: The median temperature of chicken nuggets after the one hour sampling period did not reach the food safety standard of 60°C. Foods kept at a temperature below 60°C and above 4°C are considered in the temperature danger zone. In this range, pathogenic bacteria grow at their optimal rate. Therefore, it was determined that thermally insulated bags were unable to maintain foods at temperatures hot enough to be considered safe.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".