Dietary and physiological controls on the hydrogen and oxygen isotope ratios of hair from mid‐20th century indigenous populations
Bibliographic record
Abstract
A semimechanistic model has recently been proposed to explain observed correlations between the H and O isotopic composition of hair from modern residents of the USA and the isotopic composition of drinking water, but the applicability of this model to hair from non-USA and preglobalization populations is unknown. Here we test the model against data from hair samples collected during the 1930s-1950s from populations of five continents. Although C and N isotopes confirm that the samples represent a much larger range of dietary "space" than the modern USA residents, the model is able to reproduce the observed delta(2)H and delta(18)O values given reasonable adjustments to 2 model parameters: the fraction of dietary intake derived from locally produced foods and the fraction of keratin H fixed during the in vivo synthesis of amino acids. The model is most sensitive to the local dietary intake, which appears to constitute between 60% and 80% of diet among the groups sampled. The isotopic data are consistent with a trophic-level effect on protein H isotopes, which we suggest primarily reflects mixing of (2)H-enriched water and (2)H-depleted food H in the body rather than fractionation during biosynthesis. Samples from Inuit groups suggest that humans with marine-dominated diets can be identified on the basis of coupled delta(2)H and delta(18)O values of hair. These results indicate a dual role for H and O isotopic measurements of keratin, including both biological (diet, physiology) and environmental (geographic movement, paleoclimate) reconstruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".