Cross Contamination during Simulated Food Pantry Handling of Apples, Oranges and Potatoes
Bibliographic record
Abstract
Food pantries receive, store, distribute and serve food to hundreds of people throughout a community. Safe food handling during all these activities is of paramount importance or food may support growth of pathogens and result in foodborne illnesses after consumption. Year round, and more frequently during holiday seasons, food pantries are seeking food donations to support people in need. Since most food pantries products are donated, the safety of these products from a health perspective can be tricky. The quality of food donations coupled with the way food is handled in these facilities can pose major safety concerns. To determine the potential for transfer of bacteria during handling of food at food pantries, apples, potatoes and oranges were inoculated with a non-pathogenic, fluorescently-labeled strain of Escherichia coli, then handled with gloved hands after which another non-inoculated apple, potato or orange was handled. E. coli was then enumerated from each piece of food and gloves to determine the transfer rate from the inoculated piece of food to gloves and the non-inoculated food. The average transfer rate from the inoculated food to gloves was 36.4%, from glove to un-inoculated food was 42.4% and from the inoculated food to un-inoculated food was 15.2%. Whether it be from surfaces or hands, cross-contamination of food and surfaces can readily occur and can result in increased likelihood of illness unless proper hygiene is employed. Food pantries can reduce the likelihood of microbial cross-contamination using proper handwashing techniques, and sanitation of surfaces and utensils. Proper hygiene is critical for food pantries to reduce the likelihood of bacterial transfer to previously uncontaminated surfaces.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".