Bibliographic record
Abstract
Background: The 2013-2015 Ebola outbreak caused severe human suffering and a global health crisis. Ebola Virus (EBOV) is a naturally zoonotic RNA virus that has several immune-evasion mechanisms and can cause serious disease and death in humans. The massive impact of the recent epidemic is unique in the 40-year history of this pathogen. Scientists and public health officials around the world are researching the factors that may have contributed to the scale and devastating nature of the 2013-2015 outbreak. Methods: Terms searched online through the McGill library and Medline Ovid included “Ebola”, “immune evasion”, “sequencing”, “Ebola glycoprotein” and “zoonotic transmission”. Only articles published since 2014 were selected. Summary: In this review article, we will provide discussion on the principal factors contributing to the un- usually destructive nature of the 2013-2015 Ebola outbreak. Interestingly, although several nonsynony- mous mutations have been observed in the recently circulating strains, they were not the principal cause of the unusually devastating nature of the outbreak. Instead, the high rate of transmission was likely caused by sociological factors, such as population dynamics and late detection of the outbreak. However, there is evidence to suggest that once the high rate of transmission in humans was established there was selective pressure on the virus to evade the human immune system. This selective pressure may have exacerbated an already deadly outbreak. Ongoing research efforts indicate that there is still much to be discovered about the virus and the control of outbreak management.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".