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Record W4412396430 · doi:10.1038/s41591-025-03808-2

The exposome of healthy and accelerated aging across 40 countries

2025· article· en· W4412396430 on OpenAlexaff
Hernán Hernandez, Hernando Santamaría‐García, Sebastián Moguilner, Francesca R Farina, Agustina Legaz, Pavel Prado, Jhosmary Cuadros, Raúl González-Gómez, Joaquín Migeot, Carlos Coronel‐Oliveros, Enzo Tagliazucchi, Marcelo Adrián Maito, Maria Eugenia Godoy, Josephine Cruzat, Ahmed Shaheen, Temitope Farombi, Daniel Salazar, Lucas Uglione Da Ros, Wyllians Vendramini Borelli, Eduardo R. Zimmer, Alfred K. Njamnshi, Swati Bajpai, Ankita Dey, Cyprian M. Mostert, Zul Merali, Mohamed Salama, Sara A. Moustafa, Sol Fittipaldi, Florencia Altschuler, Vicente Medel, David Huepe, Kristine Yaffe, Chinedu T. Udeh – Momoh, Harris A. Eyre, Paweł Świeboda, Brian Lawlor, J. Jaime Miranda, Claudia Duran‐Aniotz, Sandra Báez, Agustín Ibáñez

Bibliographic record

VenueNature Medicine · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsCarleton UniversityUniversity of Ottawa
FundersNational Cancer InstituteNational Institute of Mental HealthNational Institute of Diabetes and Digestive and Kidney DiseasesFogarty International CenterSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Heart, Lung, and Blood InstituteBiotechnology and Biological Sciences Research CouncilNational Institute on AgingWellcome Trust
KeywordsExposomeHealthy agingGerontologyMedicineBiologyEnvironmental health

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.329
Teacher spread0.319 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations36
Published2025
Admission routes1
Has abstractno

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