Simulated sunlight decreases the viability of SARS-CoV-2
Bibliographic record
Abstract
Abstract The novel coronavirus, SARS-CoV-2, has spread into a pandemic since its emergence in Wuhan, China in December of 2019. This has been facilitated by its high transmissibility within the human population and its ability to remain viable on inanimate surfaces for an extended period. To address the latter, we examined the ability of sunlight to degrade SARS-CoV-2 on stainless steel. All assays were performed using a solar simulator at the equivalent of one air mass (i.e. equatorial sun at its Zenith). Heat-controlled experiments were conducted at approximately 34% relative humidity (RH); otherwise, RH decreased with sunlight exposure until a constant temperature was maintained. When initially suspended in tissue culture medium, the virus was rendered non-viable after two hours of sunlight exposure. However, when suspended in an organic matrix designed to mimic bodily secretions, three hours of continuous sunlight was required for complete degradation. From this work, we demonstrate that sunlight represents an effective decontamination method but the speed of decontamination is variable based on the underlying matrix. This information has an important impact on the development of infection prevention and control protocols to reduce the spread of this deadly pathogen.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".