Initiation of a coronavirus vaccine library
Bibliographic record
Abstract
Coronaviruses (CoVs) continue to pose a global health threat as they spill over from animal hosts to humans. The Coalition for Epidemic Preparedness Innovations (CEPI) is a global partnership focused on vaccine research and development. CEPI's CoV Vaccine Library initiative aims to generate knowledge and tools to accelerate vaccine development against emergent CoVs, prioritizing those with the highest assessed risk of spillover, vaccine feasibility, and phylogenetic coverage. Based on expert consultations and published data, we developed a prioritized list of 26 CoVs to serve as a foundation for building a knowledge base to support future vaccine efforts. As a first step, in silico designs will be used to develop stabilized spike antigens for select targets. These antigens will be tested for pre-clinical immunogenicity and breadth using different vaccine modalities. Assays, tools, and data generated will nucleate an open-access library and serve as a starting point for emergent CoV vaccine development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".