美国报告的云播种活动 (2000-2025) 的结构化数据集,使用LLM
Jared Joseph Donohue1, Kara D Lamb2
1Data Science Institute, Columbia University, New York, NY, USA. jjd2203@columbia.edu.
Scientific data
|December 11, 2025
概括
一个新的数据集详细介绍了2000-2025年的美国云播种活动,使降水修饰研究成为可能. 这项工作展示了大型语言模型 (LLM),用于从历史环境报告中提取有价值的数据.
科学领域:
- 环境科学 环境科学
- 大气科学 大气科学
- 数据科学数据科学数据科学
背景情况:
- 云播种是一种气候改造技术,自20世纪40年代以来,在美国西部一直用于增加降水量.
- 现有的云播种操作记录是分散的,阻碍了全面的分析和研究.
- 存在对历史云播种努力的结构化,可访问的数据的需求.
研究的目的:
- 创建一个全面的,结构化的数据集美国云播种活动从2000-2025.
- 为了解决缺乏可访问的数据来分析气候变化努力.
- 展示大型语言模型 (LLM) 在提取历史环境数据中的实用性.
主要方法:
- 开发了一个多阶段的PDF-to-text提取管道.
- 利用OpenAI的o3大语言模型 (LLM) 来处理来自NOAA的832份历史报告.
- 经过验证的数据准确性,根据200个记录的手动审查,估计准确率为98.38%.
主要成果:
- 编制了云播种活动的结构化数据集,包括项目细节,日期,位置和方法.
- 通过LLM驱动的提取和手动验证实现了高数据准确性 (98.38%).
- 在Zenodo上公开提供数据集以供研究使用.
结论:
- 创建的数据集填补了云播种研究的关键数据缺口.
- LLM提供了一种强大而可扩展的解决方案,用于从历史环境文件中提取结构化信息.
- 该框架可用于解锁各种科学学科的扫描文档中的数据.
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