{"title":"利用语言模型人工智能(AI)构建的软件加强石料来源研究:以古卡拉布里亚采石场(意大利南部)为例","authors":"Domenico Miriello, Raffaella De Luca","doi":"10.1111/arcm.13091","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":8254,"journal":{"name":"Archaeometry","volume":"67 5","pages":"1283-1308"},"PeriodicalIF":1.5000,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/arcm.13091","citationCount":"0","resultStr":"{\"title\":\"Enhancing stone provenance studies through software built with language model artificial intelligence (AI): An example of ancient Calabrian quarries (southern Italy)\",\"authors\":\"Domenico Miriello, Raffaella De Luca\",\"doi\":\"10.1111/arcm.13091\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":8254,\"journal\":{\"name\":\"Archaeometry\",\"volume\":\"67 5\",\"pages\":\"1283-1308\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2025-04-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/arcm.13091\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Archaeometry\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/arcm.13091\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"ARCHAEOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archaeometry","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/arcm.13091","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"ARCHAEOLOGY","Score":null,"Total":0}
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 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.
期刊介绍:
Archaeometry is an international research journal covering the application of the physical and biological sciences to archaeology, anthropology and art history. Topics covered include dating methods, artifact studies, mathematical methods, remote sensing techniques, conservation science, environmental reconstruction, biological anthropology and archaeological theory. Papers are expected to have a clear archaeological, anthropological or art historical context, be of the highest scientific standards, and to present data of international relevance.
The journal is published on behalf of the Research Laboratory for Archaeology and the History of Art, Oxford University, in association with Gesellschaft für Naturwissenschaftliche Archäologie, ARCHAEOMETRIE, the Society for Archaeological Sciences (SAS), and Associazione Italian di Archeometria.