Kinetics And Monte Carlo Simulation of UV Disinfection B. Subtilis Spores And SARS-CoV-2 In Dried Saliva Droplets
Bibliographic record
Abstract
Abstract Surfaces can be contaminated by droplets produced coughing or sneezing. In this short, exploratory work, UV disinfection data from B. subtilis spores in dried saliva droplets were fitted to a first-order model. The model has a disinfection rate constant for single organisms, and a smaller one for aggregates (R2 ≥ 0.97). Changes in the fraction of organisms in aggregates (β) alone could account for the effects of different sized droplets in the experimental work. Since a wide spectrum of droplet sizes can be produced and some of the rate constants were uncertain, Monte Carlo simulation was used estimate the UV inactivation performance in dried saliva droplets in a range of conditions. Using conservative lognormal distribution for β, the model was applied to the UV disinfection of SARS-CoV-2 in dried droplets. It was shown that one-log reduction of SARS-CoV-2 was very likely (p>99.9%) and two-log reduction was probable (p=75%) at a dose of 60 mJ/cm2. Aggregates tend to be variable and limit the log reductions that can be achieved at high UV doses.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".