Dealing with outliers and offsets in radiocarbon dating Online sexy chat with girls for free website


20-Mar-2020 13:02

We appreciate support from Robert Spengler (Max Planck Institute for the Science of Human History) and Fengshi Luan (Shandong University).

Thanks are also extended to colleagues at Poznan Radiocarbon Laboratory, Shandong Institute of Cultural Relics and Archaeology, and Yantai Museum.

In this paper, we present a method for wiggle-matching Southern Hemisphere data sets against Northern Hemisphere curves, using the Bayesian calibration program Ox Cal 4.1 with the Reservoir Offset function accommodating a potential interhemispheric offset.

The accuracy and robustness of this approach is confirmed by wiggle-matching known-calendar age sequences of the Southern Hemisphere calibration curve SHCal04 against the Northern Hemisphere curve Int Cal04.

Wheat is regarded as one of the most important West Asian domesticates that were introduced into Late Neolithic/Early Bronze Age China.

Despite a growing body of archaeological data, the timing and routes of its dispersal remain controversial. These results neither support long-standing hypotheses of a progressive spread of wheat agriculture from Xinjiang or Gansu to eastern China nor suggest a nearly synchronous appearance in this vast zone, but corroborate transmission to lower Yellow River elites as an exotic good through cultural interactions with the Eurasian steppe along north–south routes. 1: Topographical maps showing the archaeological sites from China with directly dated wheat remains contained in the compiled data set and potential dispersal routes of wheat into and within China.

However there are Bayesian statistical methods which can be used to perform this rejection in a more objective way (Christen, 1994b) but these are not often used.

dealing with outliers and offsets in radiocarbon dating-24

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C) dates are the most important means for determining the age of Holocene archaeological deposits.

All of these Bayesian approaches can be used either for outlier detection and rejection or in a model averaging approach where dates most likely to be outliers are downweighted.



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