For the future and possible ensuing waves of COVID-19: A perspective to consider when disseminating data
Bibliographic record
Abstract
During the COVID-19 pandemic, most citizens in North America receive daily updates, which highlight the number of new cases per day in a specified region. However, as this data metric is often presented alone on media and news platforms, the spread of the novel coronavirus may often be misinterpreted. Among these daily updates which are critical to informing the public, the authors emphasize the importance of controlling for variation attributed to changes in surveillance. The number of test results that have been analyzed each day along with the total number of tests being conducted in a region have a significant impact on capturing virus spread and should always be included in widespread data. Presenting these variables may help to differentiate increases or decreases of new cases attributed to the expansion of surveillance and testing, or rather other environmental and behavioral factors. Overall, to best inform politicians, healthcare workers, and all citizens of the progress against COVID-19, there is a need to constantly improve analyses and reporting of data.
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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 | 0.127 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".