Wild capuchin monkeys as a model system for investigating the social and ecological determinants of ageing
Bibliographic record
Abstract
Studying biological ageing in animal models can circumvent some of the confounds exhibited by studies of human ageing. Ageing research in non-human primates has provided invaluable insights into human lifespan and healthspan. Yet data on patterns of ageing from wild primates remain relatively scarce, centred around a few populations of catarrhine species. Here, we introduce the white-faced capuchin, a long-lived platyrrhine primate, as a promising new model system for ageing research. Like humans, capuchins are highly social, omnivorous generalists, whose healthspan and lifespan relative to body size exceed that of other non-human primate model species. We review recent insights from capuchin ageing biology and outline our expanding, integrative research programme that combines metrics of the social and physical environments with physical, physiological and molecular hallmarks of ageing across the natural life courses of multiple longitudinally tracked individuals. By increasing the taxonomic breadth of well-studied primate ageing models, we generate new insights, increase the comparative value of existing datasets to geroscience and work towards the collective goal of developing accurate, non-invasive and reliable biomarkers with high potential for standardization across field sites and species, enhancing the translatability of primate studies.This article is part of the discussion meeting issue 'Understanding age and society using natural populations'.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".