Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The global COVID-19 pandemic has brought tremendous momentum to the field of messenger RNA (mRNA) vaccines. The advantages of this vaccine platform, such as rapid development and high efficacy, resulted in mRNA vaccines being the first approved vaccines against COVID-19. Looking forward to the development of future vaccines, how can we make RNA vaccines even better? While improvements in the stability of the formulation and cost of the vaccine are inevitable, one of the main challenges is lowering the dose of RNA in order to avoid side effects associated with high doses of RNA. One way to do this is by using self-amplifying RNA (saRNA), a type of mRNA that encodes a replicase that copies the original strand of RNA once it’s in the cell. Here, we discuss the origins of saRNA, how it works in comparison to mRNA, current challenges in the field and the future of saRNA vaccines.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it