Using δ<sup>2</sup>H in Human Bone Collagen to Correct for Freshwater <sup>14</sup>C Reservoir Offsets: A Pilot Study from Shamanka II, Lake Baikal, Southern Siberia
Bibliographic record
Abstract
ABSTRACT There is increasing awareness of the need to correct for freshwater as well as marine reservoir effects when undertaking radiocarbon (14C) dating of human remains. Here, we explore the use of stable hydrogen isotopes (δ2H), alongside the more commonly used stable carbon (δ13C) and nitrogen isotopes (δ15N), for correcting 14C freshwater reservoir offsets in 10 paired human-faunal dates from graves at the prehistoric cemetery of Shamanka II, Lake Baikal, southern Siberia. Excluding one individual showing no offset, the average human-faunal offset was 515±175 14C yr. Linear regression models demonstrate a strong positive correlation between δ15N and δ2H ratios, supporting the use of δ2H as a proxy for trophic level. Both isotopes show moderate but significant correlations (r2 ~ 0.45, p < 0.05) with 14C offsets (while δ13C on its own does not), though δ2H performs marginally better. A regression model using all three stable isotopes to predict 14C offsets accounts for approximately 65% of the variation in the latter (r2=0.651, p=0.025), with both δ13C and δ2H, but not δ15N, contributing significantly. The results suggest that δ2H may be a useful proxy for freshwater reservoir corrections, though further work is needed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".