Strontium isotope ratios ( <sup>87</sup> Sr/ <sup>86</sup> Sr) of tooth enamel: a comparison of solution and laser ablation multicollector inductively coupled plasma mass spectrometry methods
Bibliographic record
Abstract
Strontium isotope ratios (87Sr/86Sr) in tooth enamel provide a means to investigate migration and landscape use in humans and other animals. Established methods for measuring (87)Sr/(86)Sr in teeth use bulk sampling (5-20 mg) and labor-intensive elemental purification procedures before analysis by either thermal ionization mass spectrometry (TIMS) or multicollector inductively coupled plasma mass spectrometry (MC-ICP-MS). Another method for measuring 87Sr/86Sr is laser ablation MC-ICP-MS, but concerns have been expressed about its accuracy for measuring tooth enamel. In this study we test the precision and accuracy of the technique by analyzing 30 modern rodent teeth from the Sterkfontein Valley, South Africa by laser ablation MC-ICP-MS and solution MC-ICP-MS. The results show a mean difference in 87Sr/86Sr measured by laser ablation and by solution of 0.0003 +/- 0.0002. This degree of precision is well within the margin necessary for investigating the potential geographic origins of humans or animals in many areas of the world. Because laser ablation is faster, less expensive, and less destructive than bulk sampling solution methods, it opens the possibility for conducting 87Sr/86Sr analyses of intra-tooth samples and small and/or rare specimens such as micromammal and fossil teeth.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".