Automatic Vending-Machines Contamination: A Pilot Study
Bibliographic record
Abstract
Hot-drinks vending machines are disseminated worldwide and millions of drinks are served every day. Because of a small number of studies on hot-drinks related illnesses, the aim of this pilot study was to identify the presence and load of bacterial species, potentially harmful for consumers, within hot-drinks vending machines external critical surfaces. This preliminary cross sectional study was carried out in April 2015 at the University of Siena, Italy. Samples were taken from the critical surfaces of 4 hot-drinks vending machines (VM); the analyzed VM’s critical surfaces were: Dispense Areas, Nozzles and Glass-Holders. The samples were sown on selective culture media: Plate Count Agar (PCA) at 22°C and 36°C, Slanetz and Bartely Agar (SBA) and Mannitol Salt Agar (MSA). Total Viable Count (TVC) at 36°C and 22°C was assessed for mesophilic and psychrophilic contamination. Results were expressed in terms of average CFU/cm2. Descriptive and statistical analyses were performed in order to assess which surface was the most contaminated. The nozzles resulted to be the most contaminated critical surface, showing average values over the limits in all the culture media (PCA 36°C, PCA 22°C, MSA and SBA). The statistical analysis showed that the nozzles were significantly more contaminated (p <0.05) than Dispense Areas and Glass-holders both in PCA 36°C and in PCA 22°C. Given the high number of CFU/cm2, VM may constitute a potential threat for consumer’s health, reason for which further studies are recommendable.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".