Analyzing Radiocarbon Reservoir Offsets Through Stable Nitrogen Isotopes and Bayesian Modeling: A Case Study Using Paired Human and Faunal Remains from the Cis-Baikal Region, Siberia
Why this work is in the frame
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Bibliographic record
Abstract
Dietary offsets in radiocarbon dates are becoming increasingly interesting to researchers, not only because of their impact on the reliability of chronologies but also because of the possibilities for extracting further dietary information from the 14 C data itself. This is the case with the cemeteries of the Cis-Baikal region being studied as part of the international Baikal-Hokkaido Archaeology Project set up to examine hunter-gatherer cultural dynamics in eastern Asia. Fortunately, to control for a freshwater reservoir offset, we were able to obtain a number of paired terrestrial herbivore and human material for 14 C dating. This article tests the correspondence between stable isotope evidence and the offsets seen in 14 C values and the implications for the analysis of the 14 C measurements as “chronometric dates.” This is an unusually well-documented example of freshwater reservoir offsets, providing an ideal case study to test different approaches to analyzing such offset information. Here, a purely Bayesian approach is compared with the more frequently applied linear regression analysis.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it