解决大学存储库中论文难以获取未发表数据的问题
Héctor L Venegas-Quiñones1, Pablo A Garcia-Chevesich1, Madeleine Guillen2
1Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO.
Ground water
|July 17, 2025
概括
本研究介绍了一种使用光学字符识别和大型语言模型 (LLM) 来提取大学论文中的关键地下水数据的自动化方法. 该创新系统克服了传统搜索的局限性,改善了对重要科学信息的访问.
科学领域:
- 环境科学 环境科学
- 水文学的水文学
- 数据科学数据科学数据科学
- 信息检索 信息检索
背景情况:
- 大学论文包含有价值的,往往未发表的环境和水文数据.
- 由于PDF格式和库搜索工具不足,阻碍了数据访问和分析.
- 手动从论文中提取数据是耗时且低效的.
研究的目的:
- 开发一种自动化解决方案,从大学论文中提取地下水数据.
- 为了克服基于关键字的搜索在访问灰色文学的局限性.
- 为秘鲁阿雷基帕地区创建一个可搜索的地下水数据库.
主要方法:
- 光学字符识别 (OCR) 用于从PDF文件中提取文本.
- 用于初始关键字评分的Python脚本.
- 大型语言模型 (LLM),包括谷歌Gemini和Ollama,用于语义内容分析.
- 数据提取和组织成像Excel电子表格这样的格式.
主要成果:
- 成功识别和提取关键的地下水数据 (例如,水质,井位置).
- 通过随后的手动检查证实了高精度.
- 开发一个系统,使许多文件能够快速地进行上下文查询.
结论:
- 开发的方法有效地解决了从灰色文献中获取和利用数据的挑战.
- 该系统提高了区域地下水数据的可访问性和管理.
- 该方法具有可扩展性和适应性,可用于跨学科科学发现的更广泛应用.
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