Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Air filtration in various implementations has become a critical intervention in managing the spread of coronavirus disease 2019 (COVID-19). However, the proper deployment of air filtration has been hampered by an insufficient understanding of its principles. These misconceptions have led to uncertainty about the effectiveness of air filtration at arresting potentially infectious aerosol particles. A correct understanding of how air filtration works is critical for further decision-making regarding its use in managing the spread of COVID-19. The issue is significant because recent evidence has shown that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can remain airborne longer and travel farther than anticipated earlier in the COVID-19 pandemic, albeit with diminishing concentrations and viability. While SARS-CoV-2 virions are around 60-140 nm in diameter, larger respiratory droplets and air pollution particles (>1 µm) have been found to harbor the virions. Removing particles that could carry SARS-CoV-2 from the air is possible using air filtration, which relies on the natural or mechanical movement of air. Among various types of air filters, high-efficiency particle arrestance (HEPA) filters have been recommended. Other types of filters are less or more effective and, correspondingly, are easier or harder to move air through. The use of masks, respirators, air filtration modules, and other dedicated equipment is an essential intervention in the management of COVID-19 spread. It is critical to consider the mechanisms of air filtration and to understand how aerosol particles containing SARS-CoV-2 virions interact with filter materials to determine the best practices for the use of air filtration to reduce the spread of COVID-19.
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.
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 it