Micro‐punches versus micro‐slices for serial sampling of human dentine: Striking a balance between improved temporal resolution and measuring additional isotope systems
Bibliographic record
Abstract
RATIONALE: The last decade has seen a dramatic increase in the application of serial sampling of human dentine in archaeology. Rapid development in the field has provided many improvements in the methodology, in terms of both time resolution as well as the ability to integrate more isotope systems in the analysis. This study provides a comparison of two common sampling approaches, allowing researchers to select the most suitable approach for addressing specific research questions. METHODS: Two common approaches for sequential sampling of human dentine (micro-punches and micro-slices) are compared in terms of viability and efficacy. Using archaeological deciduous second molars and permanent first molars, this study demonstrates how the two approaches capture aspects of the weaning process in different ways. In addition, different aspects related to the extraction protocols, such as the thickness of the central slide and the solubilisation step, are also evaluated. RESULTS: S values are needed, or when the samples are poorly preserved. In addition, the solubilisation step has a large effect on collagen yield, and, to a lesser extent, on isotopic compositions. Therefore, it is important to ensure that only samples that have undergone the same pre-treatment protocol are directly compared. CONCLUSIONS: We present the pros and cons of the two micro-sampling approaches and offer possible mitigation strategies to address some of the most important issues related to each approach.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".