Elemental Analysis of Bone Tissue Using Electrothermal Vaporization Coupled to Inductively Coupled Plasma Optical Emission Spectrometry
Bibliographic record
Abstract
Sex determination of human remains is vital in the field of archaeology, as it provides researchers with a more complete understanding of social and biological structures within ancient societies.Typically, sex determination is performed through the analysis of skeletal features, such as the os coxae (pubic bone) or skull.In the absence of sufficiently preserved features, accurate sex determination can be exceedingly challenging.The multi-elemental analysis of hair, in combination with multi-variate statistics, has been shown to allow for accurate sex determination in both living humans and mummified individuals.However, hair is much rarer in an archeological context than bone tissue.Here, the method developed for hair is applied for the first time to bone tissue collected from 500-year-old mummies originating from Peru.Bone samples were ground prior to analysis via electrothermal vaporization coupled to inductively coupled plasma optical emission spectrometry; only 2 mg of bone tissue is required for analysis.Point-by-point internal standardization was performed with Ar I 430.010 nm to compensate for sample loading effects on the plasma.Peak areas were integrated and mass corrected before being used in combination with multivariate analysis.Principal component analysis was insufficient to determine sex but was used to identify elements that are effective predictors of sex.Using linear discriminant analysis allowed accurate predictions for all samples.Possible correlations between elemental composition of hair and bone were also investigated.This study further expands the potential for accurate sex determination of human remains via non-morphological methods.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
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".