Journal of Archaeological Method and Theory ( IF 3.2 ) Pub Date : 2025-01-27 , DOI: 10.1007/s10816-025-09695-8
Pavol Hnila, Ellery Frahm, Alessandra Gilibert, Arsen Bobokhyan
Traditionally, reliable obsidian sourcing requires expensive calibration standards and extensive geological reference collections as well as experience with statistical processing. In the South Caucasus — one of the most obsidian-rich regions on the planet — this combination of requirements has often restricted sourcing studies because few projects have geological reference collections that cover all known obsidian sources. To test an alternative approach, we conducted “open sourcing” using portable X-ray fluorescence (pXRF) analyses of geological specimens with three key changes to the conventional method: (1) commercially available calibration standards were replaced with a loanable Peabody-Yale Reference Obsidians (PYRO) set, (2) a comprehensive geological reference collection was replaced with a published dataset of consensus values (Frahm, 2023a, 2023b), and (3) processing in statistical packages was replaced with two semiautomated machine-learning workflows available online. For comparison, we used classification by-eye with JMP 17.2 statistical software. Furthermore, we propose a new method to evaluate calibrations, which streamlines comparisons and which we refer to as a symmetric difference ratio (SDR). The results of this feasibility study demonstrate that this “open sourcing” workflow is reliable, yet currently only in combination with classification by-eye. When the consensus values were combined with the machine-learning solutions, the classification results were unsatisfactory. The most encouraging aspect of our alternative “open sourcing” workflow is that it enables correct source identification without physically measuring reference collections, therefore surmounting an obstacle that, until now, has severely limited archaeological research. We anticipate that rapid developments in machine-learning will also soon improve the workflow.
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将黑曜石文物归因于其火山起源的“开源”工作流程和机器学习方法:来自南高加索的可行性研究
传统上,可靠的黑曜石采购需要昂贵的校准标准和广泛的地质参考收藏,以及统计处理经验。在南高加索地区——地球上黑曜石最丰富的地区之一——这种要求的组合经常限制采购研究,因为很少有项目拥有涵盖所有已知黑曜石来源的地质参考收藏。为了测试另一种方法,我们使用便携式 X 射线荧光 (pXRF) 对地质标本进行了“开源”分析,对传统方法进行了三个关键更改:(1) 市售校准标准被可借用的皮博迪-耶鲁参考黑曜石 (PYRO) 套装取代,(2) 综合地质参考收藏被已发布的共识值数据集(Frahm, 2023a、2023b) 和 (3) 统计包中的处理被两个在线提供的半自动机器学习工作流程所取代。为了进行比较,我们使用了 JMP 17.2 统计软件的肉眼分类。此外,我们提出了一种评估校准的新方法,该方法简化了比较,我们称之为对称差值比 (SDR)。这项可行性研究的结果表明,这种 “开源” 工作流程是可靠的,但目前仅与 by-eye-classification 结合使用。当一致性值与机器学习解决方案相结合时,分类结果并不令人满意。我们的替代“开源”工作流程最令人鼓舞的方面是,它无需物理测量参考馆藏即可实现正确的来源识别,从而克服了迄今为止严重限制考古研究的障碍。 我们预计机器学习的快速发展也将很快改善工作流程。