New opportunities and challenges for conservation evidence synthesis from advances in natural language processing

IF 5.2 1区 环境科学与生态学 Q1 BIODIVERSITY CONSERVATION
Charlotte H. Chang, Susan C. Cook-Patton, James T. Erbaugh, Luci Lu, Yuta J. Masuda, István Molnár, Dávid Papp, Brian E. Robinson
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Abstract

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.

Abstract Image

自然语言处理的进步为保护证据合成带来了新的机遇和挑战
解决全球环境保护问题需要迅速将自然和保护社会科学证据转化为与政策相关的信息。然而,科学产出的指数增长加上报告研究的学科差异使得跨学科证据综合特别具有挑战性。自然语言处理(NLP)的持续发展,如大型语言模型、机器学习(ML)和数据挖掘,有望加速跨学科证据综合和初级研究。ML、NLP和人工智能(AI)系统在计算科学研究中的发展为加速保护社会科学各个阶段的证据合成提供了新的途径。为了展示机器学习、语言处理和人工智能如何帮助自动化和扩展保护社会科学中的证据合成,我们描述了可以自动查询文献、处理大量非结构化文本证据以及从科学研究中提取感兴趣参数的方法。通过对数据进行大规模分类和标记,自动化可以转化为保护社会科学的其他研究议程,然而,关于如何在保护中合乎道德和有效地使用混合人工智能专家系统,还有一些悬而未决的问题。
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来源期刊
Conservation Biology
Conservation Biology 环境科学-环境科学
CiteScore
12.70
自引率
3.20%
发文量
175
审稿时长
2 months
期刊介绍: Conservation Biology welcomes submissions that address the science and practice of conserving Earth's biological diversity. We encourage submissions that emphasize issues germane to any of Earth''s ecosystems or geographic regions and that apply diverse approaches to analyses and problem solving. Nevertheless, manuscripts with relevance to conservation that transcend the particular ecosystem, species, or situation described will be prioritized for publication.
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