Pestilence and famine: Continuing down the vicious cycle with COVID-19
Bibliographic record
Abstract
Despite the fact that we produce enough food to feed everyone on Earth, world hunger is on the rise. On the other side of the table, the obesity crisis also weighs heavily. Malnutrition is less about food than about socioeconomic factors such as conflict, poverty, and global disasters such as climate change and the novel Coronavirus Disease 2019 (COVID-19) pandemic. Nutrition and infectious disease exist in an intricate dance. Adequate and balanced nutrition is critical for appropriate response to infection and any changes in the balance can serve as a tipping point for the next pandemic. On the other hand, pandemics, such as COVID-19, lead to greater malnutrition. Both over- and undernutrition increase severity of disease, alter vaccine effectiveness, and potentially create conditions for viral mutation and adaptation-further driving the disease and famine vicious cycle. These long-term health and socioeconomic repercussions have direct effects at individual and global levels and lead to long-term consequences. Therefore, investing in and strengthening public health, pandemic prevention, and nutrition programs become vital at a much more complex systems level.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".