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 isoztopic 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 a well-established random forest spatial regression framework (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) (de Caritat et al., 2022, 2023, 2025b), reflecting the influence of soluble and exchangeable mineral phases and atmospheric inputs such as rain and dust/seaspray. 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 quantile random forest regression, is lowest across well-sampled, geologically stable regions where the model is well-trained and highest in poorly-sampled regions and lithologically complex zones. Despite remaining spatial gaps and areas of high prediction uncertainty, our model offers significantly improved coverage and resolution for Australia compared to other global or regional isoscapes. It also provides a scalable framework for updating the Australian isoscape as sampling density increases. This isoscape establishes a robust baseline for applications in provenance research, palaeoecology, and environmental geochemistry. The bioavailable Sr isotope dataset is available from the Geoscience Australia e-Catalogue entry by de Caritat et al. (2025a) on https://doi.org/10.26186/150024
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".