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McCarthy, C., Sternberg, T. & Brooks, C. (2026). The conservation metadata gap: Why AI classification is a symptom, not a solution. Environmental Research Letters. 21 (3)

C. McCarthy et al.,  "The conservation metadata gap: Why AI classification is a symptom, not a solution", in Environmental Research Letters, vol. 21, no. 3, 2026

@article{mccarthy2026_1784172422176,
	author = "McCarthy, C. and Sternberg, T. and Brooks, C.",
	title = "The conservation metadata gap: Why AI classification is a symptom, not a solution",
	journal = "Environmental Research Letters",
	year = "2026",
	volume = "21",
	number = "3",
	doi = "10.1088/1748-9326/ae3335",
	url = "https://iopscience.iop.org/journal/1748-9326"
}

TY  - JOUR
TI  - The conservation metadata gap: Why AI classification is a symptom, not a solution
T2  - Environmental Research Letters
VL  - 21
IS  - 3
AU  - McCarthy, C.
AU  - Sternberg, T.
AU  - Brooks, C.
PY  - 2026
SN  - 1748-9326
DO  - 10.1088/1748-9326/ae3335
UR  - https://iopscience.iop.org/journal/1748-9326
AB  - 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.
ER  -