El conjunt arqueològic del segle XVI localitzat a la cisterna est del Castell de Montsoriu

Masnou, Jordi Tura

doi:10.33115/a/26046679/4_2

Cited by 1 publication

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“…The only clear and systematic univocal prediction from the results displayed in Table 4 is the ascription of the CM2 sample set (lead-glazed cooking ware from the Montsoriu castle) to the Barcelona cluster. This origin was already hypothesized by archaeologists [61] as a possible provenience for the three analyzed sample sets retrieved from the Montsoriu castle. However, for the other two sets from Montsoriu (CM1 and CM3), the probability percentages corresponding to the Barcelona origin are very low, and there is no other reference cluster that systematically captures a portion of probability above 60%.…”

Section: Cluster Predictionsupporting

confidence: 60%

“…Pastes can be both reddish/ochre (oxidizing conditions) or grey (fired in reducing conditions). The ensemble of local pottery is dated between 1475 and 1560 [60] and despite some resemblance with later productions from Breda, it is often assumed that this pottery, in particular the greencolored glazed pottery, was produced in Barcelona's workshops [61]. The three types of pottery from Montsoriu selected for the present investigation correspond (Table 2) to non-glazed grey pottery (7 shards), ochre-reddish pottery with a lead-glaze (10 shards), and a green-glaze (6 shards).…”

Section: Archaeological Samples Of Unknown Proveniencementioning

confidence: 99%

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Supervised Machine Learning Algorithms to Predict Provenance of Archaeological Pottery Fragments

et al. 2022

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Code and data sharing are crucial practices to advance toward sustainable archaeology. This article explores the performance of supervised machine learning classification methods for provenancing archaeological pottery through the use of freeware R code in the form of R Markdown files. An illustrative example was used to show all the steps of the new methodology, starting from the requirements to its implementation, the verification of its classification capability and finally, the production of cluster predictions. The example confirms that supervised methods are able to distinguish classes with similar features, and provenancing is achievable. The provided code contains self-explanatory notes to guide the users through the classification algorithms. Archaeometrists without previous knowledge of R should be able to apply the novel methodology to similar well-constrained classification problems. Experienced users could fully exploit the code to set up different combinations of parameters, and they could further develop it by adding other classification algorithms to suit the requirements of diverse classification strategies.

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Section: Cluster Predictionsupporting

confidence: 60%

Section: Archaeological Samples Of Unknown Proveniencementioning

confidence: 99%