Environmental change and seasonal behavior of mastodons in the Great Lakes region inferred from stable isotope analysis
Bibliographic record
Abstract
Abstract We investigate seasonal variations in the diet and drinking water of four Great Lakes mastodon (Mammut americanum) specimens using stable isotope analysis of serially sampled inner-enamel bioapatite structural carbonate (δ13Csc, δ18Osc), and previously published bulk analyses. Isotopic analyses and thin section measurements showed that mastodon tooth enamel extension rates (~ 12–4 mm/yr, decreasing toward the cervix) were lower than those of mammoths or modern elephants. Mastodons had distinct and highly regular seasonal variations in δ13Csc and δ18Osc, which we interpret in the context of local glacial history and vegetation changes. Seasonal variations in δ18O were large but variations in δ13C were small, and may have been obscured if coarser sampling methods than our inner-enamel sampling approach were used. Thus, our approach may be particularly useful for understanding relatively small seasonal changes in δ13C within C3 environments. The seasonal patterns, though not entirely conclusive, suggest that the Ontario mastodons did not migrate over very long distances. Rather, the climate and seasonal dietary patterns of mastodons within the region changed over time, from ~ 12,400 to 10,400 14C yr BP (~ 15,000 – 12,000 cal yr BP). Insights gained using these methods can contribute to a better understanding of megafaunal extinctions and Paleoamerican lifeways.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".