Bibliographic record
Abstract
Growing Up in the Cis-Baikal Region of Siberia, Russia analyses the dietary life histories of prehistoric hunter-gatherers from six cemeteries in the Lake Baikal region of Siberia, Russia. The overarching goal was to better understand how they lived by examining what they ate, how they utilized the landscape, and how this changed over time. Recent archaeological advances offer new ways to gain insight into the lives of people who died many years ago. With the application of biochemistry, archaeologists can study an individual’s dietary choices from the time they were born up until the last few months of life, providing a fuller picture of how people lived, the challenges they may have faced, and the choices they made. This study tests the application of a technique known as dentine micro-sampling, in which the inner part of a tooth is sectioned into thin strips, each representing roughly nine months of development. These strips were subjected to stable carbon and nitrogen isotope analysis, unveiling the chemical markers of different foods. The results show that the dietary contribution of terrestrial and aquatic food sources varied within and between cemeteries and cultural periods, which can be viewed as evidence of dietary independence among groups occupying the same area. The results also show that the movement of these individuals around the Lake Baikal region is observable in the chemical markers from their teeth. In conjunction with other methods, dentine micro-sampling helps us understand the interplay of personal choice and ecological constraint that makes up the dietary behaviour of these prehistoric peoples.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".