A bio‐available strontium isoscape for eastern Beringia: a tool for tracking landscape use of Pleistocene megafauna
Bibliographic record
Abstract
ABSTRACT Numerous paleoecological questions concern the mobility of ancient fauna in eastern Beringia. Strontium (Sr) isotope ratio (87Sr/86Sr) analysis has emerged as a powerful tracer for determining the provenance of ancient biological materials. However, it is important to characterize 87Sr/86Sr variation across a landscape. We measured the 87Sr/86Sr composition of teeth from present‐day, herbivorous rodents (n = 162) sampled from across eastern Beringia to estimate bio‐available 87Sr/86Sr values. We compiled these data with the very limited number of previously published 87Sr/86Sr values from the region. We then used this dataset and a machine learning, random‐forest regression to predict bio‐available 87Sr/86Sr variations across eastern Beringia. As a case study using our new 87Sr/86Sr map (isoscape), we measured the 87Sr/86Sr and oxygen stable isotope values (δ18O) of five radiocarbon‐dated steppe bison from eastern Beringia and compared these to our 87Sr/86Sr isoscape and a δ18O isoscape to estimate the probable landscape use of these ancient fauna. Our model and isoscape provide important foundations for a wide range of additional applications, including studies of the paleo‐mobility of other fauna, ancient people and present‐day fauna in eastern Beringia.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".