Asymptomatic infection is the pandemic’s dark matter
Bibliographic record
Abstract
Physicists who study the behavior of galaxies posit the existence of invisible dark matter, which has mass but cannot be seen.The behavior of galaxies cannot be explained without the existence of something we cannot see (1).In this sense, asymptomatic infection is the dark matter of the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic.Asymptomatic infection is difficult to observe and characterize, by definition, as asymptomatic individuals are not sickened, do not present for care, and cannot be identified without testing.Nonetheless, the frequency with which such infection occurs is key to understanding the epidemiology of the pandemic.In PNAS, Sah et al.(2) provide a rigorous systematic review and metaanalysis of what we know about asymptomatic infection to date.Their review includes 390 studies-a testimonial to the intensity with which this question has been studied.Their results are important: Notwithstanding the virulence of SARS-CoV-2 infection, true asymptomatic infection is common (35%), and asymptomatic infection a
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.
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 | 0.017 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".