Differences in causal reasoning about resource dynamics and consequences for the participatory debate on North Sea fisheries     

M.C. Verweij  W.L.T. van Densen 《Marine Policy》2010

Fishermen, scientists, policy makers, and staff of environmental NGOs (ENGOs) have significantly different understandings of the processes that determine developments in fish stocks. These perception differences hinder the participatory debate on why fish stocks change and which management measures are effective. In this study, differences in causal reasoning about processes between fishermen, policy makers, ENGO-staff, and scientists were examined, regarding four case studies within the management of the fishery on North Sea plaice. First, it appeared that all parties, besides scientists, had difficulty reasoning about long-term effects because of comprehension problems with stock dynamics, and because of short-term economical interests. Second, there were differences in how parties deal with natural variation and interconnectedness of natural and anthropogenic influences. Stock assessment scientists work with single-species models, reducing complexity by using assumptions that rule out variation, in order to inform policy makers about the effect of one isolated management measure. Fishermen on the other hand, relying on information from their daily lives at sea, emphasize complexity and interconnectedness, and the impact of the ever-changing and unpredictable nature. ENGO-staff appeared reluctant to reason about single species and broaden the debate to the ecosystem-level, while emphasizing the effect of man. As a consequence of the diverging perceptions, much time in multi-stakeholder settings is lost on repetitive discussions, mainly on the relative importance of 'nature' versus 'man'. No wonder that policy makers feel lost, and experience processes as very complex. Concluding, to handle these perception differences, there is need for a directive process coordinator, and a more creative informative role for fisheries scientists. Together with all participants, they should map all expectancies and lines of reasoning at the beginning of the debate. This scheme can be relied on during subsequent meetings, in which perceptions can adequately be positioned.  相似文献   

DZN3型自动土壤水分观测仪故障处理及日常维护          下载免费PDF全文

韩波  王黎明  李四维  贺中华  张敏  许翔 《中低纬山地气象》2023,47(1):107-111

作为全国重要水稻生产基地的湖南,因夏秋多旱的气候特征,水稻种植经常受干旱影响而减产。为做好气象为农服务工作,湖南省气象局从2010年开始,在全省建设了60个自动土壤水分观测站,显著改进了土壤水分观测手段和方法,提升了现代农业气象业务和干旱监测服务水平。该文简要介绍了DZN3型自动土壤水分观测仪的工作原理和系统结构,在总结省、市、县三级维护保障实践基础上,重点阐述了DZN3型自动土壤水分观测仪常见故障的分析及排查,对台站日常巡查维护及有关注意事项提出了建议。为气象部门基层台站业务人员提高维护维修时效,降低故障发生概率提供借鉴与参考。  相似文献   

自动地震速报软件测试平台的设计与实现     

何少林  林健富 《中国地震》2021,37(1):22-32

针对自动地震速报软件功能和性能需进行专门测试的需求,以及目前行业内相关测试方法或过程的不足,研制了 一种自动地震速报软件测试平台.该平台能构建期望的震相数据,重构期望的地震波形数据,可分别对被测软件的自动震相识别和定位功能进行测试,并以期望的输出结果为依据,根据给出的评分标准对测试结果进行评估,为用户正确使用软件给出指...  相似文献   

内外业一体化技术在江都市龙川广场施工放线测量中的应用     

张跃  黄继红 《江苏地质》2003,27(2):100-102

应用内外业一体化技术，对龙川广场整个建筑物特征点进行了施工放线测量，以实例说明该方法是实用可行的。  相似文献   

Multi-hazard susceptibility mapping based on Convolutional Neural Networks     

《地学前缘(英文版)》2022,13(5):101425

Multi-hazard susceptibility prediction is an important component of disasters risk management plan. An effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their interactions. However, with the rapid development of artificial intelligence technology, multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge bottleneck. In order to effectively solve this problem, this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks (CNN). First, we use historical flash flood, debris flow and landslide locations based on Google Earth images, extensive field surveys, topography, hydrology, and environmental data sets to train and validate the proposed CNN method. Next, the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria, i.e., coefficient of determination, overall accuracy, mean absolute error and the root mean square error. Experimental results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods, debris flows and landslides. Finally, the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility map. It can be observed from the map that 62.43% of the study area are prone to hazards, while 37.57% of the study area are harmless. In hazard-prone areas, 16.14%, 4.94% and 30.66% of the study area are susceptible to flash floods, debris flows and landslides, respectively. In terms of concurrent hazards, 0.28%, 7.11% and 3.13% of the study area are susceptible to the joint occurrence of flash floods and debris flow, debris flow and landslides, and flash floods and landslides, respectively, whereas, 0.18% of the study area is subject to all the three hazards. The results of this study can benefit engineers, disaster managers and local government officials involved in sustainable land management and disaster risk mitigation.  相似文献