Improving stable isotopic interpretations made from human hair through reduction of growth cycle error
Bibliographic record
Abstract
A recent trend in stable isotopic analysis involves the reconstruction of short-term variations in diet using hair segments. However, bulk hair samples typically contain a growth cycle error, which may conceal or confound the most recently incorporated isotopic information. It is assumed that, at any given time, ∼85-90% of scalp hairs are actively growing, while the remaining 10-15% have transitioned into a resting or inactive phase, which lasts up to 4 months before hairs are shed. This study uses growth phase to determine the effects of age, sex, and health status on carbon and nitrogen isotopic ratios of hair analyzed in sequential segments. For this study, we selected archaeological hair samples from 10 individuals from Dakhleh Oasis, Egypt. Isotopic analyses of actively growing hair segments were compared to those for mixed growth phase segments from each individual. These data demonstrate the presence of growth cycle error and show that an understanding of structural-functional relationships is essential for interpreting normal versus pathological changes in hair follicle and fiber production. In situations where diet change and mobility produce variations in an individual's isotopic composition, elimination of positional-temporal error in sequential segment hair analyses can facilitate greater understanding of intraindividual metabolic reactions and changes in hair growth cycles. Phase identification may aid in determining the presence of pathological conditions in individuals, especially in those lacking skeletal indications, and provide a more precise estimation of seasonal dietary patterns, access to changing food resources, and metabolic equilibration to a new locality.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".