Detection of SARS-CoV-2 on surfaces in food retailers in Ontario
Bibliographic record
Abstract
The COVID-19 pandemic has generated increased interest in potential transmission routes. In food retail settings, transmission from infected customers and workers and customers through surfaces has been deemed plausible. However, limited information exists on the presence and survival of SARS-CoV-2 on surfaces, particularly outside laboratory settings. Therefore, the purpose of this project was to assess the presence of the virus at commonly found surfaces at food retail stores and the potential role that these spaces play in virus transmission. Samples (n=957) were collected twice a week for a month in food-retail stores within Ontario, Canada. High-touch surfaces were identified and surveyed in 4 zones within the store (payment stations, deli counters, refrigerated food section and carts and baskets). The samples were analyzed using a molecular method, i.e., reverse transcriptase quantitative Polymerase Chain Reaction (RT-qPCR). Regardless of the store's location, the sampling day or time, the location of the surface within the store or the surface material, all samples tested negative for SARS-CoV-2. These results suggest that the risk of exposure from contaminated high-touch surfaces within a food retailer store is low if preventive measures and recommended sanitizing routines are maintained.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".