A Bioavailable Strontium Isoscape of Australia
Bibliographic record
Abstract
Abstract. Strontium isotope ratios (⁸⁷Sr/⁸⁶Sr) at the Earth’s surface offer powerful tools for geological, environmental, and archaeological applications. In minerals and biological materials, ⁸⁷Sr/⁸⁶Sr reflects the isotopic composition of the local bedrock and derived soils. In Australia, however, large regional-scale surveys of bioavailable ⁸⁷Sr/⁸⁶Sr remain scarce. Here, we present a new dataset of bioavailable ⁸⁷Sr/⁸⁶Sr ratios from 278 catchment outlet (floodplain) sediment samples, spanning inland southeastern Australia (South Australia, New South Wales, Victoria), northern Western Australia, the Northern Territory, Queensland (north of 21.5° S), and the Yilgarn Craton in southern Western Australia. Combined with more than 20,000 global Sr isotope measurements, this dataset was used to generate a high-resolution isoscape of Australia using random forest regression (Bataille et al., 2020). Australian bioavailable ⁸⁷Sr/⁸⁶Sr values span a narrower range (0.70501–0.78121) compared to co-located bulk sediment values (0.70480–1.09089) (Caritat et al., 2022, 2023, 2025b), reflecting the influence of soluble and exchangeable mineral phases and atmospheric inputs such as rain and dust. The predicted isoscape reproduces major geological patterns, with higher values over ancient crustal provinces like the Yilgarn Craton and eastern Palaeozoic orogens, and lower values across younger sedimentary basins and coastal margins. Model uncertainty, assessed via prediction standard deviations, is lowest across well-sampled, geologically stable regions and highest in coastal and lithologically complex zones. Compared to existing global and regional isoscapes, our model offers significantly improved coverage and resolution for Australia. This isoscape provides a robust baseline for applications in provenance research, palaeoecology, and environmental geochemistry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".