THREE GENERATIONS UNDER ONE ROOF? BAYESIAN MODELING OF RADIOCARBON DATA FROM NUNALLEQ, YUKON-KUSKOKWIM DELTA, ALASKA
Bibliographic record
Abstract
This article presents the results of a program of radiocarbon dating and Bayesian modeling from the precontact Yup'ik site of Nunalleq (GDN-248) in subarctic southwestern Alaska. Nunalleq is deeply stratified, presenting a robust relative chronological framework of well-defined individual house floors abundant in ecofacts suitable for radiocarbon dating. Capitalizing on this potential, we present the results of one of the first applications of Bayesian statistical modeling of radiocarbon data from an archaeological site in the North American Arctic. Using these methods, we demonstrate that it is possible to generate robust, high-resolution chronological models from Arctic archaeology. Radiocarbon dates, procured prior to the program of dating and modeling presented here, suggested an approximately three-century duration of occupation at the site. The results of Bayesian modeling nuance this interpretation. While it is possible that there may have been activity for almost three centuries (beginning in the late fourteenth century), occupation of the dwelling complex, which dominates the site, was more likely to have endured for no more than a century. The results presented here suggest that the occupation of Nunalleq likely encompassed three generations beginning cal AD 1570–1630 before being curtailed by conflict around cal AD 1645–1675.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".