标题： 美国报告的云播种活动的结构化数据集（2000-2025），使用大语言模型 显示英文标题

标题： Structured dataset of reported cloud seeding activities in the United States (2000-2025) using an LLM

摘要： 云种，一种用于增加降水的天气修改技术，自20世纪40年代以来一直在美国西部使用。 然而，目前尚无全面的数据集可用于分析这些努力。 为解决这一差距，我们提出了一个结构化的数据集，记录了2000年至2025年期间美国报告的云种活动，包括项目名称、年份、季节、州、操作员、播种剂、用于部署的设备、声明的目的、目标区域、对照区域、开始日期和结束日期。 结合我们的多阶段PDF到文本提取流程与OpenAI的o3大语言模型（LLM），我们处理了来自美国国家海洋和大气管理局（NOAA）的832份历史报告。 该数据集基于对200个随机抽样记录的手动审查，估计准确率为98.38%，并且可在Zenodo上公开获取。 这个数据集弥补了云种数据的空白，并展示了LLM从历史环境文件中提取结构化信息的潜力。 更广泛地说，这项工作提供了一个可扩展的框架，用于从扫描文档中解锁不同科学领域的历史数据。 显示英文摘要

摘要： Cloud seeding, a weather modification technique used to increase precipitation, has been practiced in the western United States since the 1940s. However, comprehensive datasets are not currently available to analyze these efforts. To address this gap, we present a structured dataset of reported cloud seeding activities in the U.S. from 2000-2025, including the project name, year, season, state, operator, seeding agent, apparatus used for deployment, stated purpose, target area, control area, start date, and end date. Combining our multi-stage PDF-to-text extraction pipeline with OpenAI's o3 large language model (LLM), we processed 832 historical reports from the National Oceanic and Atmospheric Administration (NOAA). The resulting dataset demonstrates 98.38% estimated accuracy, based on manual review of 200 randomly sampled records, and is publicly available on Zenodo. This dataset addresses the gap in cloud seeding data and demonstrates the potential for LLMs to extract structured information from historical environmental documents. More broadly, this work provides a scalable framework for unlocking historical data from scanned documents across scientific domains.