Surface microbiology of the electronic menu in all-you-can-eat sushi restaurants in Toronto, Ontario
Bibliographic record
Abstract
The use of electronic menus within the food industry is rapidly expanding. Currently, the role of electronic menus as a vehicle for pathogens has not been explored within the restaurant setting. This preliminary study was conducted to assess the hygienic cleanliness of electronic menus and identify if their use in all-you-can-eat (AYCE) sushi restaurants may pose a public health hazard. Five AYCE sushi restaurants in Toronto, Ontario, with electronic menus were randomly selected and were visited twice by the researcher and a public health inspector. A total of 30 electronic menus were sampled using 3M hydrated sponges with buffered peptone water broth and tested for E. coli and total coliforms. All electronic menus tested negative for E. coli although four electronic menus showed presence of total coliforms. The findings from this study suggest the current use of tablets as electronic menus in AYCE sushi restaurants may be less threatening to the safety of the public than previously thought. However, it is important for restaurants to be aware of the potential for electronic menus to serve as a fomite, and proper sanitation procedures should be monitored and enforced to maintain cleanliness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".