Chang Charlotte H, Cook-Patton Susan C, Erbaugh James T, Lu Luci, Masuda Yuta J, Molnár István, Papp Dávid, Robinson Brian E
Department of Biology and Environmental Analysis Program, Pomona College, Claremont, California, USA.
The Nature Conservancy, Arlington, Virginia, USA.
Conserv Biol. 2025 Apr;39(2):e14464. doi: 10.1111/cobi.14464.
Addressing global environmental conservation problems requires rapidly translating natural and conservation social science evidence to policy-relevant information. Yet, exponential increases in scientific production combined with disciplinary differences in reporting research make interdisciplinary evidence syntheses especially challenging. Ongoing developments in natural language processing (NLP), such as large language models, machine learning (ML), and data mining, hold the promise of accelerating cross-disciplinary evidence syntheses and primary research. The evolution of ML, NLP, and artificial intelligence (AI) systems in computational science research provides new approaches to accelerate all stages of evidence synthesis in conservation social science. To show how ML, language processing, and AI can help automate and scale evidence syntheses in conservation social science, we describe methods that can automate querying the literature, process large and unstructured bodies of textual evidence, and extract parameters of interest from scientific studies. Automation can translate to other research agendas in conservation social science by categorizing and labeling data at scale, yet there are major unanswered questions about how to use hybrid AI-expert systems ethically and effectively in conservation.
解决全球环境保护问题需要迅速将自然科学和保护社会科学的证据转化为与政策相关的信息。然而，科学产出的指数级增长，再加上研究报告中的学科差异，使得跨学科证据综合尤其具有挑战性。自然语言处理（NLP）的不断发展，如大语言模型、机器学习（ML）和数据挖掘，有望加速跨学科证据综合和基础研究。计算科学研究中ML、NLP和人工智能（AI）系统的发展为加速保护社会科学证据综合的各个阶段提供了新方法。为了展示ML、语言处理和AI如何帮助自动化和扩大保护社会科学中的证据综合，我们描述了一些方法，这些方法可以自动查询文献、处理大量非结构化文本证据，并从科学研究中提取感兴趣的参数。自动化可以通过大规模对数据进行分类和标记，转化为保护社会科学中的其他研究议程，但关于如何在保护中道德且有效地使用混合AI专家系统，仍存在一些主要未解决的问题。
