Species-specific reservoir effect estimates: A case study of archaeological marine samples from the Bering Strait
Bibliographic record
Abstract
Due to the marine reservoir effect, radiocarbon dates of marine samples require a correction. Marine reservoir effects, however, may vary among different marine species within a given body of water. Factors such as diet, feeding depth and migratory behaviour all affect the 14 C date of a marine organism. Moreover, there is often significant variation within single marine species. Whilst the careful consideration of the Δ R values of a single marine species in a given location is important, so too is the full range of Δ R values within an ecosystem. This paper illustrates this point, using a sample pairing method to estimate the reservoir effects in 17 marine samples, of eight different species, from the archaeological site of Ekven (Eastern Chukotka, Siberia). An OxCal model is used to assess the strength of these estimates. The marine reservoir effects of samples passing the model range from Δ R (Marine20) = 136 ± 41–Δ R = 460 ± 40. Marine reservoir effect estimates of these samples and other published samples are used to explore variability in the wider Bering Strait region. The archaeological implications of this variability are also discussed. The calibrating of 14 C dates from human bone collagen, for example, could be improved by applying a dietary relevant marine reservoir effect correction. For humans from the site of Ekven, a Δ R (Marine20) correction of 289 ± 124 years or reservoir age correction of 842 ± 123 years is suggested.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".