Author reply: Do Tunisians have a European ancestry?
Bibliographic record
Abstract
H. Ben Saad's summary of the Tunisian population and use of the “European” label in our recent Global Lung Function Initiative (GLI) lung volume paper [1] highlights that we live in a diverse world and using a limited number of labels to define populations and ancestral groups is fraught with bias and historical artefact. We recognise that no single reference equation for lung function outcomes can be generalisable globally. Until we have more comprehensive data from all regions in the world, we must be careful not to apply GLI equations indiscriminately. It is imperative that we continue to conduct research to better understand how ancestry, as well as social and environmental determinants, impacts lung health. The GLI is reviewing the factors that explain observed population differences and will work towards developing more broadly applicable reference equations in the future. Until we have more comprehensive data from all regions in the world, we must be careful not to apply GLI equations indiscriminately
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.024 | 0.027 |
| 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".