Best Practices for Germicidal Ultraviolet-C Dose Measurement for N95 Respirator Decontamination
Bibliographic record
Abstract
Ultraviolet-C (UV-C) decontamination holds promise in combating the coronavirus disease 2019 pandemic, particularly with its potential to mitigate the N95 respirator shortage. Safe, effective, and reproducible decontamination depends critically on UV-C dose, yet dose is frequently measured and reported incorrectly, which results in misleading and potentially harmful protocols. Understanding best practices in UV-C dose measurement for N95 respirator decontamination is essential to the safety of medical professionals, researchers, and the public. Here, we outline the fundamental optical principles governing UV-C irradiation and detection, as well as the key metrics of UV-C wavelength and dose. In particular, we discuss the technical and regulatory distinctions between UV-C N95 respirator decontamination and other applications of germicidal UV-C, and we highlight the unique considerations required for UV-C N95 respirator decontamination. Together, this discussion will inform best practices for UV-C dose measurement for N95 respirator decontamination during crisis-capacity conditions.
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 | 0.048 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".