Reply to the letter: To consider the exercise density in the dose–response relationship: the idea is promising, the operationalization tricky!
Bibliographic record
Abstract
We thank Desgorces for taking the time to read our recent publication in the European Journal of Applied Physiology (Herold et al. 2025b ) and providing an insightful commentary (Desgorces 2025 ). In his commentary, Desgorces ( 2025 ) supports our view that density is an important variable for analyzing and prescribing the dose and dosage of physical activity (PA) in the consideration of their effects on brain health. However, he also acknowledges that quantifying PA density is challenging, which is underlined by different perspectives on its definition, operationalization, and interpretation. In this context, we are pleased to have the opportunity for further clarification and for a constructive discussion by providing our perspective on the points raised by Desgorces ( 2025 ) in this reply. While we acknowledge the points raised in Desgorces’ commentary (2025), our response provides additional support for our understanding of PA density as conveyed in our initial review.
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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 | 0.015 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.050 | 0.056 |
| Insufficient payload (model declined to judge) | 0.006 | 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".