An ensemble machine learning bioavailable strontium isoscape for Eastern Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bioavailable strontium isotope ratios ( 87 Sr/ 86 Sr) distribution across the landscape mainly follow the underlying lithology, making 87 Sr/ 86 Sr baseline maps (isoscapes) powerful tools for provenance studies. 87 Sr/ 86 Sr has already been used in Eastern Canada (EC) to track food and human remains origins, or to reconstruct animal mobility. While bioavailable 87 Sr/ 86 Sr 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 87 Sr/ 86 Sr 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 87 Sr/ 86 Sr to a global dataset significantly improved the model prediction with a drastic increase of predicted 87 Sr/ 86 Sr and increased spatial uncertainty in the northern Canadian craton. EML produced similar 87 Sr/ 86 Sr 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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it