Strontium isoscapes for provenance, mobility and migration: the way forward
Bibliographic record
Abstract
Strontium isotopes ( 87 Sr/ 86 Sr) are increasingly used as a provenance tool in multiple disciplines. Application to biological materials requires knowledge of the variation in bioavailable 87 Sr/ 86 Sr across the landscape, potentially in the form of an isoscape (a quantitative model of spatial isotopic variability). This paper summarizes and provides advice on our current understanding of the main concerns in creating and interpreting isoscapes of bioavailable 87 Sr/ 86 Sr. Isoscape creation approaches include domain mapping, geostatistical contour mapping and machine learning, the last becoming more readily achievable with the availability of software packages. It is critically important to develop isoscapes at a resolution appropriate for addressing the research questions. Choice of sample materials depends on the research questions and availability: plants or fauna with small ranges are favoured, with some analytes (snails, soil leachates) posing challenges. Interpreting 87 Sr/ 86 Sr in biological tissues requires considering Sr metabolism and the timing of tissue formation, thus far underappreciated. The numerous sources of error involved in developing and applying isoscapes must be recognized to avoid over-interpreting data and spurious provenance precision. We hope this paper will help researchers investigating provenance, mobility, landscape use and migration to develop the most appropriate isoscapes for their purposes, and possible future use by others.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".