Highly Variable Freshwater Reservoir Offsets Found along the Upper Lena Watershed, Cis-Baikal, Southeast Siberia
Bibliographic record
Abstract
A program of paired dating of human and faunal remains on a sample of 11 prehistoric (Mesolithic/Neolithic to Early Bronze Age) graves in the Upper Lena basin, southeast Siberia, was initiated to investigate the freshwater reservoir effect (FRE). The results show the presence of a substantial but highly variable offset, ranging from 255 to 101014C yr. In contrast to previous studies centered on Lake Baikal and the Angara River, human stable nitrogen isotope values show little or no correlation with the radiocarbon offset, despite the clear trophic differences seen in δ15N between terrestrial and aquatic sources of protein in the region's isotope ecology. However, stable carbon isotope measurements show a moderate negative correlation of some predictive value (r= −0.70,p= 0.016,df= 10). Two different regression equations have been calculated, the first using human δ13C values for the entire data set (r2= 0.49) and the second, using both δ13C and δ15N values, limited to the Early Bronze Age of the southern Upper Lena (r2= 0.84,p= 0.030,df= 5). The source of the old carbon in the Upper Lena River system is not clear. While the river flows over carbonate bedrock and is moderately alkaline, we suggest that old terrestrial carbon entering the riverine foodweb through bank erosion and other processes is a more likely candidate for the majority of the14C offset.
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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 | 0.001 | 0.001 |
| 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".