COVID -19 Morbidity and mortality in tropical countries: The effects of economic, institutional, and climatic variables
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
Despite the significant and rising human and economic costs of the novel coronavirus disease (COVID-19), our knowledge on its epidemiology remains limited necessitating expedited research to aid public policy. This study contributes to the knowledge gap by focusing on exploring the effects of potential covariates (economic, institutional, and climatic conditions) on COVID-19 in tropical countries. Using an Ordinary Least Square (OLS) regression, our results showed a non-linear relationship between temperature and infection-to-test ratio. Specifically, temperatures warmer than 18 °C can favor the spread of the disease. In addition, strikingly, countries with better democratic principles registered more positive cases than their counterparts at high levels of corruption.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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