Un breve análisis de la mortalidad del Covid-19 en países de América Latina
Bibliographic record
Abstract
In early the 2020, the world faced a pandemic caused by Covid-19, which created crises in the health systems and economy of all nations. In Latin America, 1,246,190 cases and 65,228 deaths were recorded during the first four months of that year. Therefore, this study analyzed the data from report 101 of the World Health Organization, with the intention of identifying the countries in Latin America most affected by this crisis. With the use of the Pareto diagram tool, it could be shown that, the sum of positive cases in the countries of Brazil, Peru, Ecuador and Mexico represent approximately 80% of the total report of contagions in Latin America, the number of most alarming cases was recorded in Brazil with more than 70000 and the number of contagions does not exceed 0.1% of the total population of each government, with the exception of Ecuador, which surpassed it. However, this virus was not fatal in the first quarter of 2020 in Latin American countries
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".