COVID-19 vaccines - are we there yet?
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 novel coronavirus SARS-CoV-2, the cause of the COVID-19 pandemic, is a highly infectious human respiratory pathogen to which the global population had no prior immunity. The virus will likely continue to cause significant morbidity until there is a broadly effective vaccine As of mid-December 2020, more than 200 COVID-19 vaccine candidates are in development and 11 have entered phase III clinical trials globally. All generate immunity to the viral spike glycoprotein Three vaccine candidates have agreements for procurement and use in Australia if efficacy and safety requirements are met - one protein-based vaccine, one vaccine using a simian-derived adenovirus vector and one messenger RNA vaccine. The latter two vaccines have published interim analyses and efficacy results of their phase III trials. The messenger RNA vaccine is being rolled out in the UK, USA and Canada Significant uncertainties remain. How well will some of those at highest risk of severe disease (such as older people aged >75 years and those with immunocompromising conditions) be protected by a vaccine, and for how long? Also, to what extent will vaccination protect against infection? This will determine the degree of indirect 'herd' protection needed through broad vaccine coverage of younger age groups.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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