Tackling COVID-19: Can the African continent play the long game?
Bibliographic record
Abstract
Events have progressed with dizzying rapidity since the World Health Organization (WHO) was first alerted to cases of severe pneumonia in the Wuhan City of China on December 31st 2019. The novel SARS-CoV-2 coronavirus disease (COVID-19) was declared a pandemic on March 11th 2020. As of April 7th, a total of 1.38 million cases of COVID-19 had been diagnosed globally with over 78 000 deaths attributable to the disease [1]. Comparisons have been drawn between COVID-19 and other deadly pandemics such as the 1918 Spanish flu that infected about one-third of the world’s population, killed 40-50 million people and changed the course of history [2]. While it is premature to judge the final death toll of COVID-19, the global response to the pandemic will determine how bad it becomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".