Be air safe: HEPA filters as an air ventilation resource to minimize the spread of COVID-19 in Canadian elementary and high schools
Bibliographic record
Abstract
Background: The Coronavirus disease (COVID-19) is an infectious and exponentially spreading virus. COVID-19 is mainly spread through personal contact and respiratory droplets such as aerosols. COVID-19 was determined to be a global pandemic on March 11 2020, which has led to the evaluation of various personal safety precautions that needed to be implemented in order to decrease the spread of COVID-19 within Canadian communities. Purpose: This literature review explored the use of High-efficiency particulate air (HEPA) filters that have been evaluated in recent studies to reduce the spread of aerosols within indoor spaces. Most primary and secondary schools in British Columbia (BC) reopened in September 2020 and HEPA filters were considered to potentially reduce the spread of COVID-19 in schools across the province. Methods: Aliterature review was conducted from December, 2020 to June, 2021 with research on HEPA filter efficiency in public indoor settings. Results: Adequate air quality is essential for a healthy lifestyle, and during the COVID-19 pandemic, there can be contradicting advice between the implementation and precautions to reduce the spread of COVID-19 and ability for schools to maximize classrooms air quality. As schools have reopened, it is essential to consider all possible precautions to prevent the spread of COVID-19 including; wearing masks, sanitizing hands, social and physical distancing, barriers between one another, and adequate air filtration systems. HEPA filters have been shown to remove at least 99.97% of all particles in the 0.15-0.2 μmrange.Conclusion: Schools must have the ability to adapt with current research on the bestpreventative strategies to fight against COVID-19 in the classroom.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".