Novel methodologies for classifying scientific articles related to the nuclear fuel cycle have been developed using machine learning to discover declarable activities under the Additional Protocol of the International Atomic Energy Agency. In this study, the relationships between articles and their lists of references or authors were analyzed using a network to examine the resultant features. By comparing the original network and a randomly rewired network based on the original data, we show that article topics and lists of references or authors form clusters in a projected bipartite network, indicating that lists of references or authors can be employed as independent variables for classification. The topics of scientific articles were classified using the lists of article authors, lists of references, and abstract word counts. Notably, decision-tree classifiers and logistic regression exhibit high F1_score and recall. Furthermore, to improve classifier performance, ensemble classifiers were applied based on the abovementioned single classifiers. The combined classifiers with logistic regression based on author lists as an independent variable showed a particularly high recall value when the subject of an article was distinguished. This classification method could contribute to a better understanding for determining and monitoring nuclear fuel cycle–related R&D to achieve safeguard objectives.

使用机器学习开发了对与核燃料循环相关的科学文章进行分类的新方法，以发现国际原子能机构附加议定书下的可申报活动。在这项研究中，使用网络分析文章与其参考文献或作者列表之间的关系，以检查结果特征。通过比较原始网络和基于原始数据的随机重新布线的网络，我们表明文章主题和参考文献或作者列表在投影的二分网络中形成集群，表明参考文献或作者列表可以用作分类的独立变量. 科学文章的主题使用文章作者列表、参考文献列表和摘要字数进行分类。尤其，决策树分类器和逻辑回归表现出高 F1_score 和召回率。此外，为了提高分类器的性能，在上述单个分类器的基础上应用了集成分类器。当区分文章主题时，基于作者列表作为自变量的逻辑回归组合分类器显示出特别高的召回值。这种分类方法有助于更好地理解确定和监测与核燃料循环相关的研发，以实现保障目标。当区分文章主题时，基于作者列表作为自变量的逻辑回归组合分类器显示出特别高的召回值。这种分类方法有助于更好地理解确定和监测与核燃料循环相关的研发，以实现保障目标。当区分文章主题时，基于作者列表作为自变量的逻辑回归组合分类器显示出特别高的召回值。这种分类方法有助于更好地理解确定和监测与核燃料循环相关的研发，以实现保障目标。