Editorial: Outbreak oracles: how AI's journey through COVID-19 shapes future epidemic strategy
Bibliographic record
Abstract
The authors declare that the research was conducted in the absence of any commercial or financial 98 relationships that could be construed as a potential conflict of interest. 99The author(s) declared that they were an editorial board member of Frontiers, at the time of 100 submission. This had no impact on the peer review process and the final decision. 101 We are grateful to all the authors and reviewers contributing to this Research Topic. 109Publisher's note 110 All claims expressed in this article are solely those of the authors and do not necessarily represent 111 those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any 112 product that may be evaluated in this article, or claim that may be made by its manufacturer, is not 113 guaranteed or endorsed by the publisher. 114 6
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".