Integrating cortisol and isotopic analyses of archeological hair: Reconstructing individual experiences of health and stress
Bibliographic record
Abstract
Archeological hair from 14 adults from the Nasca Region, Peru (c. AD1-1000) was analyzed for carbon and nitrogen isotopic compositions and cortisol levels. We investigated the relationship between isotopic compositions, which reflect diet, and cortisol, which reflects biogenic cortisol production and chronic stress. Using a case study approach, we determined that there are consistent changes in cortisol production associated with the rapid dietary change characteristic of local mobility. Moreover, changes in nitrogen- and carbon-isotope compositions, when integrated with cortisol levels, enabled inferences to be made about nitrogen metabolism and carbon routing, and elucidated the nature of potential stressors in the months before death. The isotopic and cortisol data suggested a relatively high rate of exposure to stress that is consistent with what is known about the Nasca Region social and physical environments. Of the 14 adults included in this study, six likely suffered from illness/trauma before death, and a further three experienced stress without an observable associated change in isotopic composition. Five individuals also experienced increased stress related to local mobility, inferred from co-occurring changes in cortisol production and dietary shifting. The integration of cortisol and isotopic data revealed individual characteristics of hidden frailty and risk that would not be apparent using more traditional methods of evaluating health status. This approach will provide a powerful enhancement to the understanding of stress, morbidity, and well-being developed through skeletal analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".