New opportunities and challenges for conservation evidence synthesis from advances in natural language processing
© 2025 The Author(s). Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.
| Veröffentlicht in: | Conservation biology : the journal of the Society for Conservation Biology. - 1989. - 39(2025), 2 vom: 01. Apr., Seite e14464 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2025
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| Zugriff auf das übergeordnete Werk: | Conservation biology : the journal of the Society for Conservation Biology |
| Schlagworte: | Journal Article aprendizaje automático ciencias sociales de la conservación conservation social science evidence synthesis language models machine learning modelos lingüísticos natural language processing procesamiento lingüístico natural |
| Zusammenfassung: | © 2025 The Author(s). Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology. 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 |
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| Beschreibung: | Date Completed 01.04.2025 Date Revised 01.04.2025 published: Print Citation Status MEDLINE |
| ISSN: | 1523-1739 |
| DOI: | 10.1111/cobi.14464 |