What is it about?

The use of space in ephemeral archaeological sites can be difficult to interpret due to poor preservation. Analysing microscopic soil traces can help with this, but the datasets produced by such methods are usually incompatible. In order to reach a combined interpretation of space, a novel statistical approach was applied.

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Why is it important?

Finding new ways to maximise the information gained from ephemeral archaeological sites is crucial for our understanding of periods in time and ancient lifestyles that are under-represented within archaeological narratives. These periods can be vital for understanding important transitions in human history, leading to our current subsistence and mobility patterns, and defining the interaction between humans and their environments.


I hope that this article will inspire others to seek out creative solutions by combining existing statistical methods, or incompatible datasets, to reach a better understanding of their case study.

Daniella Vos

Read the Original

This page is a summary of: A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data, PLoS ONE, March 2021, PLOS,
DOI: 10.1371/journal.pone.0248261.
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