High-Precision Bayesian Modeling of Samples Susceptible to Inbuilt Age

Abstract

Radiocarbon dates on samples susceptible to inbuilt age are common in the chronological record of many archaeological and environmental sites. Indeed, fragments of charcoal and wood are sometimes the only materials sufficiently well preserved for dating. However, where high-precision estimates are required the extra uncertainty associated with such measurements often renders them unusable. This article tests three Bayesian modeling approaches that are designed to tackle this problem. The findings of our study suggest that successful corrections can be made for the inherent age offsets. The most effective and versatile approach was based on a version of outlier analysis. It is hoped that this method will become more widely employed and enable samples susceptible to inbuilt age to be included in high-precision chronologies.

A new time: Bayesian models of an Early Neolithic enclosure in North-Western Denmark

“…The second method is running a charcoal outlier model (Bronk Ramsey 2009b, Dee andBronk Ramsey 2014). Such a model can allow for outliers due to old wood effect, but still allow the dates to be included in the analysis.…”

“…This method is useful in many situations, but it is questionable if it adds any value to this question, since it is doubtful whether the After function puts enough weight on the possibility that the dates could in fact be contemporary with the event they should date. Testing has shown that the result is often imprecise when dealing with datasets consisting only or mainly of charcoal dates (Dee and Bronk Ramsey 2014).…”

Redating of the To’aga site (Ofu Island, Manu’a) and a revised chronology for the Lapita to Polynesian Plainware transition in Tonga and Samoa

“…It has been demonstrated in Bayesian models that a small dataset has a greater detrimental impact on chronological resolution than the potential of unidentified charcoals to skew to older ages [29]. Outlier analysis specifically designed to account for this inbuilt age in charcoal is promising [5] [29], but requires additional constraints (e.g., dates on short-lived materials or stratigraphy) against which to anchor the model (see also [30] where the model allows a small number of charcoal samples with inbuilt age to be younger than the context they represent, as would be the case with intrusive material).…”

Dust Cave Revisited: A Bayesian Reanalysis of the Radiocarbon Record

“…Outliers can be handled in one of two ways: elimination or down-weighting (Dee and Bronk Ramsey 2014; Bronk Ramsey 2009b). It is a common practice to simply discard outliers.…”