Evolutionary history and palaeoecology of brown bear in North-East Siberia re-examined using ancient DNA and stable isotopes from skeletal remains
Bibliographic record
Abstract
Abstract Over 60% of the modern distribution range of brown bears falls within Russia, yet palaeoecological data from the region remain scarce. Complete modern Russian brown bear mitogenomes are abundant in the published literature, yet examples of their ancient counterparts are absent. Similarly, there is only limited stable isotopic data of prehistoric brown bears from the region. We used ancient DNA and stable carbon (δ13C) and nitrogen (δ15N) isotopes retrieved from five Pleistocene Yakutian brown bears (one Middle Pleistocene and four Late Pleistocene), to elucidate the evolutionary history and palaeoecology of the species in the region. We were able to reconstruct the complete mitogenome of one of the Late Pleistocene specimens, but we were unable to assign it to any of the previously published brown bear mitogenome clades. A subsequent analysis of published mtDNA control region sequences, which included sequences of extinct clades from other geographic regions, assigned the ancient Yakutian bear to the extinct clade 3c; a clade previously identified from Late Quaternary specimens from Eastern Beringia and Northern Spain. Our analyses of stable isotopes showed relatively high δ15N values in the Pleistocene Yakutian brown bears, suggesting a more carnivorous diet than contemporary brown bears from 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".