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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引用次数: 0
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.
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.