Bibliographic record
Abstract
Ebola virus (EBOV) infects humans as well as several animal species. It can lead to a highly lethal disease, with mortality rates approaching 90% in primates. Recent advances have deepened our understanding of how this virus is able to prevent the development of protective immune responses. The EBOV genome encodes eight proteins, four of which were shown to interact with the host in ways that counteract the immune response. The viral protein 35 (VP35) is capable of capping dsRNA and interacts with IRF7 to prevent detection of the virus by immune cells. The main role of the soluble glycoprotein (sGP) is still unclear, but it is capable of subverting the anti-GP1,2 antibody response. The GP1,2 protein has shown anti-tetherin activity and the ability to hide cell-surface proteins. Finally, VP24 interferes with the production of interferons (IFNs) and with IFN signaling in infected cells. Taken together, these data point to extensive adaptation of EBOV to evade the immune system of dead end hosts. While our understanding of the interactions between the human and viral proteins increases, details of those interactions in other hosts remain largely unclear and represent a gap in our knowledge.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".