Freshwater Reservoir Offsets Investigated Through Paired Human-Faunal<sup>14</sup>C Dating and Stable Carbon and Nitrogen Isotope Analysis at Lake Baikal, Siberia
Bibliographic record
Abstract
Thirty-three paired accelerator mass spectrometry (AMS) radiocarbon dates on human and terrestrial faunal remains from the same Neolithic and Early Bronze Age graves are used to develop a correction for the freshwater reservoir effect (FRE) at Lake Baikal, Siberia. Excluding two outliers, stable nitrogen isotope (δ15N) values show a positive correlation(r2= 0.672,p< 0.000) with offsets in14C yr between paired human and fauna determinations. The highest offset observed in our data set is 622 yr, which is close to the value of ∼700 yr suggested for endemic seals in the lake. For each per mil increase in δ15N, the offset increases by 77 ± 10 yr in the overall data set. However, there are indications that different regression models apply in each of two microregions of Cis-Baikal. In the first, sites on the southwest shore of the lake and along the Angara River show a strong positive correlation between δ15N values and offsets in14C yr (r2= 0.814,p< 0.000). In the other, the Little Sea, both δ13C and δ15N values make significant contributions to the model (adjustedr2= 0.878; δ13Cp< 0.001; δ15Np< 0.000). This can be related to the complex13C ecology of the lake, which displays one of the widest ranges of δ13C values known for any natural ecosystem. The results will be important in terms of refining the culture-history of the region, as well as exploring the dynamic interactions of hunter-gatherer communities both synchronically and diachronically.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".