Time-of-Flight Secondary Ion Mass Spectrometry Analysis of Hair from Archaeological Remains
Bibliographic record
Abstract
Hair from four individuals excavated from burial sites in Pacatnamu, Peru from the Moche (450-800 AD) and Lambayeque (900-1100 AD) periods was sectioned longitudinally and analysed with time-of-flight secondary ion mass spectrometry (ToF-SIMS). An attempt was made to distinguish biogenic and diagenetic contributions to the elemental concentrations in the hair samples. Significant contamination was observed to have penetrated the hair samples from the burial environment. Results from the analyses indicate that the burial environment plays an important role in the postmortem variation in elemental content of hair samples. Various elements demonstrated an ability to permeate through the hair matrix over time. In addition, NaCl and what are believed to be aluminosilicates and mineral sulphates, were observed to have accumulated on the surface of the samples. Degradation of the samples was also suspected due to the presence of molecular fragments, possibly resulting from oxidation of the keratin proteins. The results should assist in the identification of reliable elemental signals in the analysis of ancient hair samples and promote caution when considering elements that are abundant in the burial environment.
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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 | 0.000 |
| 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 | 0.000 |
| 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 | 0.000 |
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".