An ensemble machine learning bioavailable strontium isoscape for Eastern Canada
Bibliographic record
Abstract
Bioavailable strontium isotope ratios (87Sr/86Sr) distribution across the landscape mainly follow the underlying lithology, making 87Sr/86Sr baseline maps (isoscapes) powerful tools for provenance studies. 87Sr/86Sr has already been used in Eastern Canada (EC) to track food and human remains origins, or to reconstruct animal mobility. While bioavailable 87Sr/86Sr isoscapes for EC can be extrapolated from global datasets using random forest modelling (RF), no regionally calibrated isoscape exists. Here, we produce a regionally calibrated bioavailable 87Sr/86Sr isoscape by analysing plants collected at 136 sites across EC, incorporating updated geological variables and applying a novel ensemble machine learning (EML) framework. We generated and compared isoscapes generated by the traditional RF and the EML approaches. Adding local bioavailable 87Sr/86Sr to a global dataset significantly improved the model prediction with a drastic increase of predicted 87Sr/86Sr and increased spatial uncertainty in the northern Canadian craton. EML produced similar 87Sr/86Sr predictions but with tighter spatial uncertainty distribution. Regionally calibrated RF and EML isoscapes significantly outperformed the global bioavailable RF isoscape, confirming the requirement for collecting local data in data-poor regions. This isoscape provides a baseline in EC to monitor and manage the movements and provenance of agricultural products, natural resources, endangered/harmful migratory species, and archaeological human remains and artifacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".