气候变化领域集成服务门户(CCPortal): The next generation of machine learning for tracking adaptation texts

CCPortal

DOI	10.1038/s41558-023-01890-3
	The next generation of machine learning for tracking adaptation texts
	Sietsma, Anne J.; Ford, James D.; Minx, Jan C.
发表日期	2023
ISSN	1758-678X
EISSN	1758-6798
英文摘要	Machine learning presents opportunities for tracking evidence on climate change adaptation, including text-based methods from natural language processing. In theory, such tools can analyse more data in less time, using fewer resources and with less risk of bias. However, the first generation of adaptation studies have delivered only proof of concepts. Reviewing these first studies, we argue that future efforts should focus on creating more diverse datasets, investigating concrete hypotheses, fostering collaboration and promoting 'machine learning literacy', including understanding bias. More fundamentally, machine learning enables a paradigmatic shift towards automating repetitive tasks and makes interactive 'living evidence' platforms possible. Broadly, the adaptation community is failing to prepare for this shift. Flagship projects of organizations such as the IPCC could help to lead the way. This Perspective evaluates efforts using machine learning to track global progress on adaptation, focusing on recent efforts in text analysis. It discusses practical and theoretical challenges, lessons learned and ways forward. It urges the adaptation community to prepare for a paradigm shift.
WOS研究方向	Environmental Sciences ; Environmental Studies ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:001131865000002
来源期刊	Nature Climate Change
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/282474
作者单位	Wageningen University & Research; University of Leeds
推荐引用方式 GB/T 7714	Sietsma, Anne J.,Ford, James D.,Minx, Jan C.. The next generation of machine learning for tracking adaptation texts[J],2023.
APA	Sietsma, Anne J.,Ford, James D.,&Minx, Jan C..(2023).The next generation of machine learning for tracking adaptation texts.Nature Climate Change.
MLA	Sietsma, Anne J.,et al."The next generation of machine learning for tracking adaptation texts".Nature Climate Change (2023).