Ceramic production at Selinunte (Sicily) during the 4th and 3rd century BCE: New archaeometric data through the analysis of kiln wastes

Montana, Giuseppe; Randazzo, Luciana; Tsantini, Evanthia; Fourmont, Martine H.

doi:10.1016/j.jasrep.2018.09.022

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Analyses on different sites, including production centers, enable inferring inter-site connections and, ultimately, assigning provenance to most of the identified classes [14,26]. In addition to the characterization of pottery sampled at the production centers [27], reference groups are often also defined using kiln wasters [28,29], ceramic analogous materials [30,31] produced from clays fired under controlled conditions or even simply the presumably used clay deposits [32,33]. However, the most frequently used unsupervised method (PCA) is not strictly a classification method [34].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Supervised Machine Learning Algorithms to Predict Provenance of Archaeological Pottery Fragments

et al. 2022

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Comparison of log-ratio and log10 chemical elemental data analysis of Central Amazonian pottery and archaeological implications

Hazenfratz,

Mongeló,

Munita

et al. 2024

Archaeol Anthropol Sci

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Research on the Application of Porous Ceramic Waste Gas Purification Device in Ceramic Kiln

Feng

Pan

Wang

2020

MSF

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Ceramic kiln in the use of the process of high energy consumption, high resource consumption, serious environmental pollution and other problems are the current ceramic industry development of serious problems.Kiln combustion exhaust gas pollution types mainly include: SO2, NOx, CO, particulate matter, lead, cadmium, nickel and its compounds, fluoride and chloride and so on, and the traditional kiln direct emissions into the atmosphere, causing serious pollution to the environment, the need to have reliable purification treatment devices to meet emissions requirements, but the original purification plant purification effect is poor, process complex aspects of problems.This paper introduces a new porous ceramic purification device to improve the electric kiln exhaust gas purification to improve the catalytic conversion of CO, NOX, SO2 desulfurization and particulate matter purification.

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Ceramic production at Selinunte (Sicily) during the 4th and 3rd century BCE: New archaeometric data through the analysis of kiln wastes

Cited by 3 publications

References 6 publications

Supervised Machine Learning Algorithms to Predict Provenance of Archaeological Pottery Fragments

Supervised Machine Learning Algorithms to Predict Provenance of Archaeological Pottery Fragments

Comparison of log-ratio and log10 chemical elemental data analysis of Central Amazonian pottery and archaeological implications

Research on the Application of Porous Ceramic Waste Gas Purification Device in Ceramic Kiln

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