A bioavailable strontium (87Sr/86Sr) isoscape for Aotearoa New Zealand: Implications for food forensics and biosecurity
Bibliographic record
Abstract
As people, animals and materials are transported across increasingly large distances in a globalized world, threats to our biosecurity and food security are rising. Aotearoa New Zealand is an island nation with many endemic species, a strong local agricultural industry, and a need to protect these from pest threats, as well as the economy from fraudulent commodities. Mitigation of such threats is much more effective if their origins and pathways for entry are understood. We propose that this may be addressed in Aotearoa using strontium isotope analysis of both pests and products. Bioavailable radiogenic isotopes of strontium are ubiquitous markers of provenance that are increasingly used to trace the origin of animals and plants as well as products, but currently a baseline map across Aotearoa is lacking, preventing use of this technique. Here, we have improved an existing methodology to develop a regional bioavailable strontium isoscape using the best available geospatial datasets for Aotearoa. The isoscape explains 53% of the variation (R2 = 0.53 and RMSE = 0.00098) across the region, for which the primary drivers are the underlying geology, soil pH, and aerosol deposition (dust and sea salt). We tested the potential of this model to determine the origin of cow milk produced across Aotearoa. Predictions for cow milk (n = 33) highlighted all potential origin locations that share similar 87Sr/86Sr values, with the closest predictions averaging 7.05 km away from their true place of origin. These results demonstrate that this bioavailable strontium isoscape is effective for tracing locally produced agricultural products in Aotearoa. Accordingly, it could be used to certify the origin of Aotearoa's products, while also helping to determine if new pest detections were of locally breeding populations or not, or to raise awareness of imported illegal agricultural products.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".