Developing incremental isotope sampling methods for the dental enamel of human canines and molars
Bibliographic record
Abstract
The isotope analysis of human dental enamel has become an essential tool in forensic and archaeological science for reconstructing childhood diet, mobility, and geographic provenance. Enamel forms during early life and does not remodel, permanently incorporating isotopic values such as δ 13 C, δ 18 O, and 87 Sr/ 86 Sr ingested at the time. The majority of studies rely on a singular bulk enamel sample from an individual to determine birthplace, but this approach overlooks isotope variation within the enamel. This study aims to enhance enamel isotope analysis by constructing and expanding incremental enamel sampling techniques to track childhood mobility and diet at a higher resolution. The Plomp et al. (2020) methodology for third molars (M3) was replicated and applied to first molars (M1), with the addition of a fourth sampling increment near the enamel-root junction. A new standardised method for hand-drilled incremental sampling of canines was also developed, guided by thin sections of enamel growth patterns. While molars yielded overlapping increments due to complex growth patterns and wear, canines provided up to four distinct, minimally overlapping increments spanning 1.5–7 years of age. These methods effectively capture gradual and abrupt shifts in mobility and diet in time-resolved order, which are not detectable in bulk samples. The results suggest that canines may offer improved chronological resolution compared to molars due to the latter's growth complexity and more substantial wear. This study demonstrates the potential of incremental enamel sampling for reconstructing individual life histories and aiding the identification of unknown individuals in forensic contexts. • A standardised method for incremental enamel isotope sampling of human canines. • Adapted third molar sampling to first molars with an added root-junction increment. • Incremental profiles reveal life-history changes missed by bulk sampling. • Recommend a two-tiered bulk and incremental strategy for forensic and archaeological 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".