Assessing the safety of new germicidal<scp>far‐UVC</scp>technologies
Bibliographic record
Abstract
The COVID-19 pandemic underscored the crucial importance of enhanced indoor air quality control measures to mitigate the spread of respiratory pathogens. Far-UVC is a type of germicidal ultraviolet technology, with wavelengths between 200 and 235 nm, that has emerged as a highly promising approach for indoor air disinfection. Due to its enhanced safety compared to conventional 254 nm upper-room germicidal systems, far-UVC allows for whole-room direct exposure of occupied spaces, potentially offering greater efficacy, since the total room air is constantly treated. While current evidence supports using far-UVC systems within existing guidelines, understanding the upper safety limit is critical to maximizing its effectiveness, particularly for the acute phase of a pandemic or epidemic when greater protection may be needed. This review article summarizes the substantial present knowledge on far-UVC safety regarding skin and eye exposure and highlights research priorities to discern the maximum exposure levels that avoid adverse effects. We advocate for comprehensive safety studies that explore potential mechanisms of harm, generate action spectra for crucial biological effects and conduct high-dose, long-term exposure trials. Such rigorous scientific investigation will be key to determining safe and effective levels for far-UVC deployment in indoor environments, contributing significantly to future pandemic preparedness and response.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".