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在环境研究中进行系统审查的AI辅助证据选方法:将ChatGPT与领域知识集成
Chen Zuo1, Xiaohao Yang1,2, Josh Errickson3
1School for Environment and Sustainability, University of Michigan, Ann Arbor, USA.
Environmental evidence
|April 14, 2025
概括
人工智能 (AI) 可以通过一致应用资格标准来改善环境科学中的系统审查 (SRs). 一个人工智能框架显示了与专家的实质性协议,提高了选效率和跨学科研究的透明度.
科学领域:
- 环境科学 环境科学
- 跨学科研究 跨学科研究
- 信息科学 信息科学 信息科学
背景情况:
- 环境科学中的系统审查 (SRs) 面临着由于方法和术语的多样性而带来的挑战.
- 符合资格标准的不一致应用阻碍了SRs的可复制性和透明度.
研究的目的:
- 探索人工智能 (AI) 在一致应用证据选的资格标准方面的潜力.
- 开发和评估人工智能辅助的跨学科环境SR证据选框架.
主要方法:
- 开发了一个人工智能辅助的证据选框架,使用 SR 的实例研究来研究便大肠杆菌和土地使用/土地覆盖 (LULC).
- 微调了一个ChatGPT-3.5 Turbo模型,使用专家审查的数据来选120篇文章 (标题,摘要,全文).
主要成果:
- 人工智能模型在标题/摘要选阶段与专家审稿人达成实质性协议.
- 在完整文本选阶段观察到适度的协议,保持了内部一致性.
- 该框架显示了在复杂的环境SR中提供结构化选援助的潜力.
结论:
- 人工智能辅助的证据选提供了一种结构化的方法,以提高环境SRs的一致性,效率和透明度.
- 这种框架可以降低劳动力和成本,并为科学研究中大型语言模型 (LLM) 的整合提供信息.
- 人工智能可以帮助管理研究人员的分歧,尽管对多样化和跨学科研究需要进一步验证.
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