Robust Evaluation of Ultraviolet-C Sensitivity for SARS-CoV-2 and Surrogate Coronaviruses
Bibliographic record
Abstract
Disinfection of SARS-CoV-2 is of particular importance due to the global COVID-19 pandemic. UV-C irradiation is a compelling disinfection technique because it can be applied to surfaces, air, and water and is commonly used in drinking water and wastewater treatment facilities. UV inactivation depends on the dose received by an organism, regardless of the intensity of the light source or the optical properties of the medium in which it is suspended. The 254 nm irradiation sensitivity was accurately determined using benchmark methodology and a collimated beam apparatus for four coronaviruses (SARS-CoV-2, HCoV-229E, HCoV-OC43, and MHV), a surrogate indicator organism (T1UV), and a resistant recombinant virus (baculovirus vector). Considering the light distribution across the sample surface, the attenuation of light intensity with fluid depth, the optical absorbance of the fluid, and the sample uniformity due to mixing enable accurate measurement of the fundamental inactivation kinetics and UV sensitivity.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".