Bibliographic record
Abstract
When the World Health Organization certified in 1980 that the world was finally free of smallpox as an extant human disease, few suspected that the viral agent that caused it (variola virus) would be a subject of intense debate a quarter of a century later (1). Today, with the threat of bioterrorism, variola virus is once again subject to investigative scrutiny, now with particular focus on antiviral drug discovery and the development of new generations of safer vaccines (2, 3). However, serious researchers have had to come to grips with the fact that almost all our knowledge of variola virus and of the pathogenesis of the disease it once so routinely caused in humans is derived from an era before many of the recent advances of molecular virology and immunology (4). For example, we still do not understand precisely the pathological dysfunction that smallpox patients actually died of, nor do we have exacting surrogate animal model systems with which to address such fundamental issues as the mechanisms of virus pathogenesis and the basis for the immune responses to either the disease or its vaccine. In this issue of PNAS appear two recent entries into this new arena (5, 6). Both papers provide provocative insights into these issues, as well as serious food for thought about the implications and complications of trying to exploit variola virus as a tool for medical research. In the first of the two companion papers, Jahrling et al. (5) investigated the pathogenesis of variola virus in cynomolgus macaques to ascertain whether a nonhuman primate model could recapitulate at least some of the pathologic features of human smallpox. Many closely related orthopoxviruses have been investigated over the years as surrogate models with which to dissect the pathogenesis of smallpox, for example monkeypox in monkeys, ectromelia …
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".