Effect of ventilation and wearing a facemask in reducing indoor aerosol transmission
Bibliographic record
Abstract
The COVID-19 pandemic has significantly disrupted research in respiratory protection and transmission routes. Indeed, the pandemic has highlighted a number of issues, including those related to the performance and use of respiratory protective equipment such as masks and ventilation. A plethora of commercial and homemade masks have widely appeared during the pandemic, although they are not yet fully regulated in performance and fit test. However, with regard to the source reduction process, testing facemasks not at the inhalation, but at the source (exhalation) offers a new perspective on how to prevent particle emissions. Different means of transmission reduction are measured and analysed here, and different conditions were compared: the ventilation environment, the type, filtration properties and fit (leaks) of three different facemasks. It was found that ventilation greatly helped reduce the wearer’s emissions at source. Additionally, while some materials are certainly more effective than others at inhibiting particle penetration, an even more important factor is the amount of leakage emitted from a mask.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".