Enhancing stone provenance studies through software built with language model artificial intelligence (AI): An example of ancient Calabrian quarries (southern Italy).

This study represents the first attempt to develop archaeometric software that enables researchers without programming knowledge to address archaeometric challenges, specifically determining the provenance of rocks extracted from ancient quarries. Through interaction with ChatGPT 4.0, an advanced ar...

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Detalles Bibliográficos
Publicado en:Archaeometry Vol. 67; no. 5; pp. 1283 - 1309
Autores principales: Miriello, Domenico, De Luca, Raffaella
Formato: Artículo
Publicado: Wiley-Blackwell Oct2025
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Oct2025
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        atl: Enhancing stone provenance studies through software built with language model artificial intelligence (AI): An example of ancient Calabrian quarries (southern Italy).
      aug:
        au:
          Miriello, Domenico
          De Luca, Raffaella
        affil:
          Dipartimento di Studi Umanistici, Università della Calabria, Rende (CS), Italy
          Collaborator of the Institut d'Arqueologia de la Universitat de Barcelona (IAUB), Barcelona, Spain
          Direzione Regionale Musei Nazionali Calabria, Palazzo Arnone, Cosenza (CS), Italy
      su:
        Artificial intelligence
        Rock analysis
        Stone industry
        Quarries & quarrying
        Principal components analysis
        Analytical geochemistry
        Fisher discriminant analysis
        Electronic data processing
        Italy
        Calabria (Italy)
      sug:
        subj:
          Italy
          Calabria (Italy)
          Artificial intelligence
          Rock analysis
          Stone industry
          Quarries & quarrying
          Principal components analysis
          Analytical geochemistry
          Fisher discriminant analysis
          Electronic data processing
      keyword:
        artificial intelligence (AI)
        fractal dimension
        image analysis
        provenance
        stones
      ab: This study represents the first attempt to develop archaeometric software that enables researchers without programming knowledge to address archaeometric challenges, specifically determining the provenance of rocks extracted from ancient quarries. Through interaction with ChatGPT 4.0, an advanced artificial intelligence (AI) language model, the authors guided the AI to develop StoneScanalyzer 1.0 software in Python programming language. The step‐by‐step collaborative process resulted in software capable of automatically extracting 43 quantitative variables from sets of images of cut, wet rocks acquired under reflected light, thin sections of rocks acquired under natural and polarized transmitted light using a flatbed scanner. Data elaboration using linear discriminant analysis (LDA) models and principal component analysis (PCA) led to the construction of discriminant diagrams for 250 samples taken from 10 quarries located in Calabria (southern Italy). StoneScanalyzer 1.0 software can be easily used by researchers without basic petrographic or geological knowledge, making it highly appealing as a first step for archaeologists, architects, art historians and anyone interested in studying rock provenance without expertise in mineralogy, geochemistry or petrography.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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