Historical overview and new directions in bioarchaeological trace element analysis: a review
Bibliographic record
Abstract
Given their strong affinity for the skeleton, trace elements are often stored in bones and teeth long term. Diet, geography, health, disease, social status, activity, and occupation are some factors which may cause differential exposure to, and uptake of, trace elements, theoretically introducing variability in their concentrations and/or ratios in the skeleton. Trace element analysis of bioarchaeological remains has the potential, therefore, to provide rich insights into past human lifeways. This review provides a historical overview of bioarchaeological trace element analysis and comments on the current state of the discipline by highlighting approaches with growing momentum. Popularity for the discipline surged following preliminary studies in the 1960s to 1970s that demonstrated the utility of strontium (Sr) as a dietary indicator. During the 1980s, Sr/Ca ratio and multi-element studies were commonplace in bioarchaeology, linking trace elements with dietary phenomena. Interest in using trace elements for bioarchaeological inferences waned following a period of critiques in the late 1980s to 1990s that argued the discipline failed to account for diagenesis, simplified complex element uptake and regulation processes, and used several unsuitable elements for palaeodietary reconstruction (e.g. those under homeostatic regulation, those without a strong affinity for the skeleton). In the twenty-first century, trace element analyses have been primarily restricted to Sr and lead (Pb) isotope analysis and the study of toxic trace elements, though small pockets of bioarchaeology have continued to analyse multiple elements. Techniques such as micro-sampling, element mapping, and non-traditional stable isotope analysis have provided novel insights which hold the promise of helping to overcome limitations faced by the discipline. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12520-020-01262-4.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".