通过海洋数据正义镜头预测有害藻类
Nicholas R Record1, Benjamin Tupper1, Kenneth Douyon1
1Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine 04544, United States.
Environmental science & technology
|June 10, 2025
概括
有害藻类繁殖 (HAB) 预测系统可能会加剧全球不平等. 数据正义原则可以指导全球HAB监测和预测网络的公平发展.
科学领域:
- 环境科学和数据科学环境科学和数据科学
- 海洋学和生态学
- 数据正义和算法问责制
背景情况:
- 通过网络数据,有害藻类繁殖 (HAB) 的监测和预测正在全球范围内扩大.
- 现有的海洋预测系统已经证明了意想不到的后果和超出范围的应用.
- 数据正义领域为分析数据技术带来的危害提供了一个框架,数据技术越来越多地应用于环境问题.
研究的目的:
- 将数据正义镜头应用于HAB预测系统的开发.
- 在全球HAB数据库和预测工作中识别潜在的偏见和不公平.
- 为HABs提供公平和可访问的全球数据和预测系统提供指导.
主要方法:
- 调查了三个主要的全球数据库:海洋生物多样性信息系统,有害藻类事件数据库和AlgaeBase.
- 分析了一套文献和更广泛的海洋预测文献.
- 检查了数据收集,知识集中和分类学可见性的模式.
主要成果:
- HAB的监测和知识集中在北大西洋北部的高GDP国家.
- 数据模式反映并可能加强海洋健康现有的全球经济和政治不平等.
- 在藻类类的可见性方面存在偏见,有利于与特定地区相关的藻类.
结论:
- 当前的HAB数据系统可能会延续全球海洋压力和不平等.
- 数据正义,设计正义和算法问责制的原则对于公平的HAB预测至关重要.
- 集中权益和准入对于构建包容性全球数据和预测系统至关重要.
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