The conservation metadata gap: Why AI classification is a symptom, not a solution

Abstract

Conservation science needs structured metadata captured at submission, not reconstructed afterward by artificial intelligence (AI). Each year, thousands of studies are published that could inform decisions under the United Nations Sustainable Development Goals (SDGs), the Kunming–Montreal Global Biodiversity Framework, the Commission for the Conservation of Antarctic Marine Living Resources (CCAMLR), and National Biodiversity Strategies and Action Plans (NBSAPs). Authors know their study species, locations, methods, and often their work’s policy relevance, yet this information remains buried in article text rather than searchable metadata. While AI classification tools accelerate evidence synthesis compared to manual efforts, they attempt to extract this information post-publication, turning a simple data entry task into a complex natural language processing challenge.