Elemental variability of prehistoric ceramics from postglacial lowlands and its implications for emerging of pottery traditions – An example from the pre-Roman Iron Age

Jasiewicz, Jarosław; Niedzielski, Przemysław; Krueger, Michał; Hildebrandt‐Radke, Iwona; Michałowski, Andrzej

doi:10.1016/j.jasrep.2021.103177

Cited by 7 publications

(4 citation statements)

References 49 publications

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“…By comparison with patterns obtained through multivariate statistical methods, the authors verified the potential of Self-Organising Maps for the analysis of archaeometric data. In (Jasiewicz et al, 2021) soft clustering with Gaussian Mixture Models are combined in order to select the most important elements and classify prehistoric ceramics. Element concentrations were obtained through ED-XRF analysis and 15 elements were considered.…”

Section: Computer Science Applied To Cultural Heritage Materialsmentioning

confidence: 99%

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Supervised learning algorithms as a tool for archaeology: Classification of ceramic samples described by chemical element concentrations

Ruschioni

Malchiodi

Zanaboni

et al. 2023

Journal of Archaeological Science: Reports

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Section: Computer Science Applied To Cultural Heritage Materialsmentioning

confidence: 99%

“…The resulting classification model was also applied in practical archaeological problems. As in (Charalambous et al, 2016) and (Jasiewicz et al, 2021), the elemental dataset used for the studies was obtained through ED-XRF analysis.…”

Section: Computer Science Applied To Cultural Heritage Materialsmentioning

confidence: 99%

Supervised learning algorithms as a tool for archaeology: Classification of ceramic samples described by chemical element concentrations

Ruschioni

Malchiodi

Zanaboni

et al. 2023

Journal of Archaeological Science: Reports

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“…searching for new, possibly unknown patterns in the data. The first solution is optimisation: an iterative search of a combination of variables until the most optimal subset is found (Jasiewicz et al 2021). An alternative solution is to create a non-linear regression model and then analyse it with interpretative machine learning (EML) (Molnar et al 2020, Chen et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Interpretative Machine Learning as a Key in Recognizing the Variability of Lakes Trophy Patterns

Jasiewicz

Zawiska

Rzodkiewicz

et al. 2022

Quaestiones Geographicae

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The paper presents an application of interpretative machine learning to identify groups of lakes not with similar features but with similar potential factors influencing the content of total phosphorus – P tot. The method was developed on a sample of 60 lakes from North-Eastern Poland and used 25 external explanatory variables. Selected variables are stable over a long time, first group includes morphometric parameters of lakes and the second group encompass watershed geometry geology and land use. Our method involves building a regression model, creating an explainer, finding a set of mapping functions describing how each variable influences the outcome, and finally clustering objects by ’the influence’. The influence is a non-linear and non-parametric transformation of the explanatory variables into a form describing a given variable impact on the modeled feature. Such a transformation makes group data on the functional relations between the explanatory variables and the explained variable possible. The study reveals that there are five clusters where the concentration of P tot is shaped similarly. We compared our method with other numerical analyses and showed that it provides new information on the catchment area and lake trophy relationship.

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“…FACULTY OF ARCHAEOLOGY AMU FACULTY OF ARCHAEOLOGY AMU inference using descriptive statistics, a correlation analysis (Jasiewicz et al, 2021). The research carried out so far both within and outside the project is resulting in the creation of one of the largest archaeometric collections of prehistoric pottery.…”

mentioning

confidence: 99%

About the ‘interim’ or discovering the depths of the pre-Roman Iron Age

Michałowski¹,

Teska²,

Krzyżanowska³

et al. 2021

Treasures of Time: Research of the Faculty of Archaeology of Adam Mickiewicz University in Poznań

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The pre-Roman Iron Age, i.e. the last five last centuries BC, is one of the most mysterious periods in Polish prehistory and constitutes an extremely interesting research issue, which help concentrate the studies on it, into a compact group of research problems. The studies on the pre-Roman Iron Age carried out by the group of researchers at the Faculty of Archaeology of the Adam Mickiewicz University are based on the research traditions of Poznań archaeology. The focal point of work in recent years has been settlement pottery, which is a mass source acquired during excavations whose potential has not yet been fully exploited. One way to better understand the possibilities that this type of mass material can bring has been exploitation of the potential of archaeometry. The ongoing studies on the problems of the pre-Roman Iron Age strive to detail and explain the processes and changes occurring at the time. They fit into the Poznań tradition of studies on this period, being a continuation of previous work undertaken on its intricate issues – empowering it and leading it out of the titular ‘interim’, placing it in the fully deserved centre of research interests.

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Elemental variability of prehistoric ceramics from postglacial lowlands and its implications for emerging of pottery traditions – An example from the pre-Roman Iron Age

Cited by 7 publications

References 49 publications

Supervised learning algorithms as a tool for archaeology: Classification of ceramic samples described by chemical element concentrations

Supervised learning algorithms as a tool for archaeology: Classification of ceramic samples described by chemical element concentrations

Interpretative Machine Learning as a Key in Recognizing the Variability of Lakes Trophy Patterns

About the ‘interim’ or discovering the depths of the pre-Roman Iron Age

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