A New JNHA Section on Interviews with Experts
Bibliographic record
Abstract
e are pleased to announce the launch of a new section in the Journal of Nutrition, Health and Aging that features interviews with experts in aging, geroscience, frailty, and nutrition.This new section will provide readers with unique insights into the latest research, trends, and best practices in these rapidly evolving fields.Each interview will be conducted with a leading expert in the field of aging, geroscience, frailty, and nutrition, and will provide an opportunity for our readers to learn about the challenges, opportunities, and innovations shaping these fields.Topics covered may include the role of nutrition in healthy aging or frailty, and the latest developments in gerosciencebased nutrition interventions for chronic diseases, frailty, sarcopenia, and cognitive decline.We are particularly interested in interviews that explore interdisciplinary perspectives and innovative approaches to promoting healthy aging through nutritional interventions.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.129 | 0.041 |
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".