Recent insights into SARS‐CoV‐2 omicron variant
Bibliographic record
Abstract
The SARS-CoV-2 omicron variant (B.1.1.529) was first identified in Botswana and South Africa, and its emergence has been associated with a steep increase in the number of SARS-CoV-2 infections. The omicron variant has subsequently spread very rapidly across the world, resulting in the World Health Organization classification as a variant of concern on 26 November 2021. Since its emergence, great efforts have been made by research groups around the world that have rapidly responded to fill our gaps in knowledge for this novel variant. A growing body of data has demonstrated that the omicron variant shows high transmissibility, robust binding to human angiotensin-converting enzyme 2 receptor, attenuated viral replication, and causes less severe disease in COVID-19 patients. Further, the variant has high environmental stability, high resistance against most therapeutic antibodies, and partial escape neutralisation by antibodies from convalescent patients or vaccinated individuals. With the pandemic ongoing, there is a need for the distillation of literature from primary research into an accessible format for the community. In this review, we summarise the key discoveries related to the SARS-CoV-2 omicron variant, highlighting the gaps in knowledge that guide the field's ongoing and future work.
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 | 0.001 |
| 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) | 0.004 | 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".