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Record W4407189556 · doi:10.1038/s43587-025-00812-6

Recommendations for implementing the Hevolution Alliance for Aging Biomarkers initiative

2025· article· en· W4407189556 on OpenAlexaff
Toshiko Tanaka, Felipe Sierra, Vadim N. Gladyshev, Tony Wyss‐Coray, Ana María Cuervo, Viviana Perez, Luigi Ferrucci, John Beard, Daniel W. Belsky, Heike A. Bischoff‐Ferrari, Eileen M. Crimmins, Linda P. Fried, W. Dale Greene, Steve Horvath, Rose Anne Kenny, Stephen B. Kritchevsky, Louise Lafortune, Andrea B. Maier, Terrie E. Moffitt, Sophia Pathai, Parminder Raina, Joanne Ryan, Jonathan C. Silverstein, Yousin Suh

Bibliographic record

VenueNature Aging · 2025
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAllianceAging in placePolitical sciencePsychologyGerontologyMedicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.132
metaresearch head score (Gemma)0.261
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.132
Threshold uncertainty score0.697

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1320.261
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0050.003
Science and technology studies0.0050.005
Scholarly communication0.0140.016
Open science0.0120.015
Research integrity0.0400.033
Insufficient payload (model declined to judge)0.0380.015

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.

Opus teacher head0.039
GPT teacher head0.379
Teacher spread0.339 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractno

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