Biotechnology to improve health in developing countries: a review
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 growing health disparities between the developing and the developed world call for urgent action from the scientific community. Science and technology have in the past played a vital role in improving public health. Today, with the tremendous potential of genomics and other advances in the life sciences, the contribution of science to improve public health and reduce global health disparities is more pertinent than ever before. Yet the benefits of modern medicine still have not reached millions of people in developing countries. It is crucial to recognize that science and technology can be used very effectively in partnership with public health practices in developing countries and can enhance their efficacy. The fight to improve global health needs, in addition to effective public health measures, requires rapid and efficient diagnostic tools; new vaccines and drugs, efficient delivery methods and novel approaches to therapeutics; and low-cost restoration of water, soil and other natural resources. In 2002, the University of Toronto published a report on the "Top 10 Biotechnologies for Improving Health in Developing Countries". Here we review these new and emerging biotechnologies and explore how they can be used to support the goals of developing countries in improving health.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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