Dating Marine Shell: A Guide for the Wary North American Archaeologist
Bibliographic record
Abstract
Abstract Radiocarbon dates on marine shell and other materials of marine origin appear significantly older than contemporaneous samples of terrestrial/atmospheric origin. Misunderstandings regarding the mechanisms that give rise to this “marine reservoir effect” (MRE), the terminology used to define it, and the mathematics used to describe it cause many coastal archaeologists to distrust or misinterpret marine shell dates. The recent release of a reformulated 14C calibration curve for marine samples (Marine20), which necessitates recalculation of all local reservoir age corrections, may add to the confusion. Here, we review the benefits of dating shell; provide a plain-language explanation of the mechanical, chemical, biological, and cultural processes that give rise to age disparities associated with the MRE; and offer advice to archaeologists intending to date marine shell. Our hope is that these comments will not only aid archaeologists in the planning and interpretive stages of research but also assist in assessing the reliability of legacy chronologies based on marine materials. More broadly, we encourage careful evaluation of all sources of uncertainty in all 14C chronologies, whether based on terrestrial or marine materials.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.030 |
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".