Analysis of SARS-CoV-2 and factors predicting next spillover of its more contagious variant.
Bibliographic record
Abstract
Background: At the beginning of 2020, the world has started experiencing the epidemic of a novel coronavirus; by the mid of March 2020, it has been declared a pandemic. The disease has been named COVID-19, and the virus is labelled as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on the type of infection it is causing. Coronaviruses are not new to us, and there are 15 different coronaviruses known to us. In the last 20 years, this is the fourth coronavirus pandemic, and SARS-CoV-2 seems to be the deadliest among all, with the ability to continue producing more contagious variants.
 Methodology: In this review, we used EMBASE, MEDLINE and PubMed database search engines (retrieval systems) for the words SARS-CoV-2, its origin, transmission, tropism, zoonotic, vaccines, and the factors that contribute to its contagious and virulent nature.
 Results: Accumulating evidence indicated that the pandemic might have a massive impact on both physical and mental health, particularly on those predisposed to COVID-19. Approved vaccines are in use around the globe, they may show different effects in future. We observe signs of constant mutation of SARS-CoV-2, recently SARS-CoV-2 variants such as delta and C.1.2.
 Conclusion: In this review, we endeavour to reveal the factors which can impede the ability of this highly lethal virus to reproduce into more contagious variants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".