Automation is not evidence of efficiency.

IF 7 4区环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES

Environmental Evidence Pub Date : 2026-07-02 DOI:10.1186/s13750-026-00388-7

Matthew Grainger, Sini Savilaakso, Neal Haddaway, Chris Pritchard

引用次数: 0

Abstract

We welcome Hodgson et al. (2026) empirical evaluation of ontology-grounded large language models (LLMs) for data extraction in environmental evidence synthesis. The study makes a valuable contribution by quantifying performance across attribute types and by openly documenting where current approaches struggle.

查看原文本刊更多论文

自动化并不是效率的证明。

我们欢迎Hodgson等人（2026）对环境证据合成中用于数据提取的基于本体的大型语言模型（LLMs）进行实证评估。该研究通过对跨属性类型的性能进行量化，以及公开记录当前方法的不足之处，做出了有价值的贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Environmental Evidence Environmental Science-Management, Monitoring, Policy and Law

CiteScore

6.10

自引率

18.20%

发文量

审稿时长

17 weeks

期刊介绍： Environmental Evidence is the journal of the Collaboration for Environmental Evidence (CEE). The Journal facilitates rapid publication of evidence syntheses, in the form of Systematic Reviews and Maps conducted to CEE Guidelines and Standards. We focus on the effectiveness of environmental management interventions and the impact of human activities on the environment. Our scope covers all forms of environmental management and human impacts and therefore spans the natural and social sciences. Subjects include water security, agriculture, food security, forestry, fisheries, natural resource management, biodiversity conservation, climate change, ecosystem services, pollution, invasive species, environment and human wellbeing, sustainable energy use, soil management, environmental legislation, environmental education.