{"refrec":{"BRefID":438035,"RR":"<b>McCarthy, C.; Sternberg, T.; Brooks, C.</b> (2026). The conservation metadata gap: why AI classification is a symptom, not a solution. <i>Environ. Res. Lett. 21(3)</i>: 031001. <a href=\"https://dx.doi.org/10.1088/1748-9326/ae3335\" target=\"_blank\">https://dx.doi.org/10.1088/1748-9326/ae3335</a>","BEntID":435866,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Environ. Res. Lett. 21(3)</i>: 031001. <a href=\"https://dx.doi.org/10.1088/1748-9326/ae3335\" target=\"_blank\">https://dx.doi.org/10.1088/1748-9326/ae3335</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"McCarthy, C.; Sternberg, T.; Brooks, C.","OrigTitleTranslFlag":0,"Authorstringtrunc":"McCarthy, C.; Sternberg, T.; Brooks, C.","Englishabstract":"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. 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