A strontium isoscape of north‐east Australia for human provenance and repatriation
Bibliographic record
Abstract
Abstract It has been estimated that up to 25% of Indigenous human remains held in Australian institutions are unprovenanced. Geochemical tracers like strontium isotope ratios ( 87 Sr/ 86 Sr) have been used globally for over 40 years to discern human provenance and provide independent data to aid in repatriation efforts. To reliably apply this technology, landscape 87 Sr/ 86 Sr isotope ratio variability must be quantified. In Australia, only a few studies have used this technique and they are lacking in detail. Here, we present Australia's first regional strontium isotope ratio variability study. We measured strontium isotope ratios in soil, plant, water, and faunal material throughout Cape York, Queensland, the most northerly point of mainland Australia. Results show a close correlation between surface soil leachates, vegetation, surface water, and faunal 87 Sr/ 86 Sr results with extremely high values (0.78664) associated with ancient Precambrian geology. Our study suggests that measuring 87 Sr/ 86 Sr in soil and plant samples offer a reliable approach for assessing regional Sr isotope distribution, although the inclusion of mammal and freshwater samples is also important to assess exogenous inputs. This study provides an important tool for modern and prehistoric provenance studies and may aid in answering some of Australia's most enduring archaeological questions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".