Challenge of Liquid Stressed Protective Materials and Environmental Persistence of Ebola Virus
Bibliographic record
Abstract
After the largest Ebola virus outbreak in history, experts have attempted to answer how the Zaire ebolavirus species emerged in West Africa and caused chains of human-to-human transmission. The widespread and untimely infection of Health Care Workers (HCW) in the affected countries accelerated spread of the virus within the community. Among the reasons attributed to this trend, it must be considered that HCW were exposed to the virus in their occupational environment. The contribution of environmental conditions to the spread of Ebola in West Africa was examined by investigating the effect of temperature/humidity on the virus's environmental persistence and by modeling if saturation (liquid stress) allows for penetration of Ebola virus through personal protective equipment (PPE). Ebola-Makona virus persisted on PPE and materials found in outbreak settings for less than 72 hours at 27 °C and 80% relative humidity (RH). A difference in virus penetration was observed between dry (5%, 1/21 tests) and saturated (33%, 7/21 tests) samples of PPE. Infectious virus particles penetrated through saturated coupons of Tyvek Micro Clean, Tychem QC, whole surgical masks and N95 respirators. These findings suggest inclusion of saturation or similar liquid stress simulation in protective equipment testing standards.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".