Bibliographic record
Abstract
We have had recent reminders of the threats posed by naturally occurring and bioengineered pandemic respiratory infections. It is estimated that if a pandemic infection were to arise anywhere in the world, such an infection would become widespread within 3 months and would have its maximum effect within 6 months. At present, the fastest that a vaccine effective against a new combination of antigens can be developed, purified, and produced is 9-12 months, not counting time for mass production. The current rate at which the production of influenza vaccines can be accelerated is limited by the fact that production is carried out in eggs. Therefore, there is urgent need for cell-based vaccine technologies. These are under way in several centers, yet attainment of a safe product remains several years away. Furthermore, there is need for public and private investment in manufacturing surge capacity and/or dedicated National Institutes of Health facilities to enable accelerated production. We must support efforts to shorten development time by developing and approving subunit antigens and immunogens that anticipate the most virulent viral mutations. Surveillance sites and their electronic interconnections must be expanded. Another component still lacking is funding for laboratories with high throughput screening and strong informatics capabilities to enable the fingerprinting and cataloguing of all known specimens of influenza and other pathogenic organisms for rapid identification of emerging or bioengineered pathogens. In all these efforts, we look to the federal government and to the biomedical research community in both public and private sectors.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.030 | 0.011 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".