amc：望远镜文献的自动任务分类器
amc: The Automated Mission Classifier for Telescope Bibliographies
AI总结：
amc利用大型语言模型自动分类望远镜文献，有效提升天文文献处理效率，适用于历史数据检查与标签错误识别。
摘要：
望远镜文献记录通过捕捉望远镜设施的出版统计数据和引用指标来记录天文学研究的脉动。稳健且可扩展的文献记录确保我们能够衡量设施和档案的科学影响。然而，出版物增长的速度威胁着我们手动标记天文文献的能力。因此，我们提出了自动化任务分类器（amc），一种利用大型语言模型（LLMs）来识别和分类望远镜参考文献的工具，通过处理大量论文文本。修改版本的amc在TRACS Kaggle挑战中表现良好，在保留测试集上实现了宏$F_1$得分为0.84。amc对TRACS以外的望远镜也具有价值；我们为识别由NASA任务特征的论文开发了初始软件。此外，我们探讨了amc如何用于检查历史数据集并发现潜在的标签错误。我们的工作证明了基于LLM的应用为图书馆科学提供了强大且可扩展的帮助。
英文摘要：
Telescope bibliographies record the pulse of astronomy research by capturing publication statistics and citation metrics for telescope facilities. Robust and scalable bibliographies ensure that we can measure the scientific impact of our facilities and archives. However, the growing rate of publications threatens to outpace our ability to manually label astronomical literature. We therefore present the Automated Mission Classifier (amc), a tool that uses large language models (LLMs) to identify and categorize telescope references by processing large quantities of paper text. A modified version of amc performs well on the TRACS Kaggle challenge, achieving a macro $F_1$ score of 0.84 on the held-out test set. amc is valuable for other telescopes beyond TRACS; we developed the initial software for identifying papers that featured scientific results by NASA missions. Additionally, we investigate how amc can also be used to interrogate historical datasets and surface potential label errors. Our work demonstrates that LLM-based applications offer powerful and scalable assistance for library sciences.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
