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Titlebook: U.S. Organic Dairy Politics; Animals, Pasture, Pe Bruce A. Scholten Book 2014 Bruce A. Scholten 2014 Agribusiness.agriculture.Policy.politi

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书目名称U.S. Organic Dairy Politics
副标题Animals, Pasture, Pe
编辑Bruce A. Scholten
视频video
图书封面Titlebook: U.S. Organic Dairy Politics; Animals, Pasture, Pe Bruce A. Scholten Book 2014 Bruce A. Scholten 2014 Agribusiness.agriculture.Policy.politi
描述Based on a decade of study, this book provides a scholarly overview of organic dairy politics, showing how politics, policy, and protest both inside and outside of agriculture can determine a future of pastoral landscapes resembling an earlier time in the western world or, alternatively, one made of dystopian ruralities.
出版日期Book 2014
关键词Agribusiness; agriculture; Policy; politics; Protest; revolution
版次1
doihttps://doi.org/10.1057/9781137476289
isbn_softcover978-1-349-46110-3
isbn_ebook978-1-137-47628-9
copyrightBruce A. Scholten 2014
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Bruce A. Scholtense screening, etiology analysis, disease prediction, and other aspects in the medical field, thereby improving research efficiency and accuracy. However, obtaining supervised data is difficult due to the fact that medical texts such as cases often contain sensitive patient information. This difficul
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Bruce A. Scholtenas the input dataset to analyze the effect of non-time-varying factors. However, according to the clinical experiences, the changes of physical status can partly reflect the disease progression and the mortality which have been ignored in the existing studies. Thus, based on the dynamic perspective,
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Bruce A. Scholtenoit possibilistic networks (PN) and a multi-terminology in order to extract and disambiguate terms and then to retrieve documents. The two measures of possibility and necessity were used to select the relevant concept of an ambiguous term. Thus, the user query and unstructured documents are describe
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its value for resource sharing and navigation. Tag clustering could help alleviate these problems by gathering relevant tags. In this paper, we introduce a link-based method to measure the relevance between tags based on random walk on graphs. We also propose a new clustering method which could add
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