Disinfection and decontamination in the context of SARS‐CoV‐2‐specific data
Bibliographic record
Abstract
Given the high transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as witnessed early in the coronavirus disease 2019 (COVID-19) pandemic, concerns arose with the existing methods for virus disinfection and decontamination. The need for SARS-CoV-2-specific data stimulated considerable research in this regard. Overall, SARS-CoV-2 is practically and equally susceptible to approaches for disinfection and decontamination that have been previously found for other human or animal coronaviruses. The latter have included techniques utilizing temperature modulation, pH extremes, irradiation, and chemical treatments. These physicochemical methods are a necessary adjunct to other prevention strategies, given the environmental and patient surface ubiquity of the virus. Classic studies of disinfection have also allowed for extrapolation to the eradication of the virus on human mucosal surfaces by some chemical means. Despite considerable laboratory study, practical field assessments are generally lacking and need to be encouraged to confirm the correlation of interventions with viral eradication and infection prevention. Transparency in the constitution and use of any method or chemical is also essential to furthering practical applications.
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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".