Air pollution and other risk factors might buffer COVID-19 severity in Mozambique
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
Mozambique is located on the East Coast of Africa and was one of the last countries affected by COVID-19. The first case was reported on 22 March 2020 and since then the cases have increased gradually as they have in other countries worldwide. Environmental and population characteristics have been analyzed worldwide to understand their possible association with COVID-19. This article seeks to highlight the evolution and the possible contribution of risk factors for COVID-19 severity according to the available data in Mozambique. The available data highlight that COVID-19 severity can be magnified mainly by hypertension, obesity, cancer, asthma, HIV/SIDA and malnutrition conditions, and buffered by age (youthful population). Due to COVID-19 epidemic evolution, particularly in Cabo Delgado, there is the need to increase laboratory diagnosis capacity and monitor compliance of preventive measures. Particular attention should be given to Cabo Delgado, including its isolation from other provinces, to overcome local transmission and the spread of SARS-CoV-2.
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 | 0.003 | 0.014 |
| 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