Simulation of <i>Escherichia coli</i> O157:H7 Behavior in Fresh-Cut Lettuce Under Dynamic Temperature Conditions During Distribution from Processing to Retail
Bibliographic record
Abstract
The temperature of packaged lettuce was recorded throughout a retail supply chain in Canada during the various stages of storage and shipping from the processor to retail. Temperatures were monitored in 27 cases of lettuce destined for three stores in three replicate trials conducted during the winter. A dynamic model that predicts the effect of temperature on the growth or die-off of Escherichia coli O157:H7 in packaged fresh-cut lettuce was applied to simulate the behavior of E. coli O157:H7 in the system. Simulations were carried out using distributions to account for variation in the temperature parameter and the die-off coefficient of the dynamic growth/death model. The results indicate that there was a predicted overall mean decline in cell numbers of 0.983 log cfu g⁻¹ and that the extent of cell death was proportional to the total time spent in the cold chain. Slight growth was predicted in a few instances when the dynamic temperature was above the permissive temperature of 5°C. These results suggest that generally there would be little or no growth of E. coli O157:H7 in product maintained at the proper temperature in the chain. Moreover, the predicted decline in cell numbers at refrigeration temperatures suggests that storage at 5°C or below prior to consumption would reduce populations of the pathogen in fresh-cut lettuce.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".