Is innate immunity our best weapon for flattening the curve?
Bibliographic record
Abstract
The COVID-19 pandemic is a stern reminder not to take our immune system for granted. The fact that some individuals contract and clear the SARS-CoV-2 virus without apparent symptoms stands in sharp contrast to the damage that this virus has brought upon more vulnerable populations, including the elderly and patients with chronic conditions or cancer. While the differences in severity of infection between these populations are multifactorial, it is likely that innate immunity provides the underpinning, given its central role in the early response to viral infections. Within two decades, there have been three known coronavirus zoonoses (SARS-CoV-1, MERS, and SARS-CoV-2), all of which have taken a devastating toll on the human and economic health of affected societies. Unfortunately, with the frequency and diffusion of novel zoonoses, this is unlikely to be our last battle. As we begin the long and daunting recovery from this pandemic, we must take the opportunity to think about how to exploit our innate immune system to better prepare us to fight the next virus.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".