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Undisturbed forest canopies slow understory warming by one-third across Europe
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Leonardo, 2013
Conceived by Chris Milk and Aaron Koblin and produced by Tate Modern and Google, This Exquisite Forest was inspired by the surrealist game "exquisite corpse" and its idea of collaborative creation. The project lets users create short animations that build of one another as they explore specific themes. The result is a collection of branching narratives
Aaron Koblin, Chris Milk
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Conceived by Chris Milk and Aaron Koblin and produced by Tate Modern and Google, This Exquisite Forest was inspired by the surrealist game "exquisite corpse" and its idea of collaborative creation. The project lets users create short animations that build of one another as they explore specific themes. The result is a collection of branching narratives
Aaron Koblin, Chris Milk
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International Economic Review, 1983
Publisher Summary This chapter describes a productive process involving the point input of labour, the repeated input of land, and the point output of forest products and then describes the process in a model of the economy as a whole. The optimal duration or maturity of the process is to be determined in terms of the parameters of the model.
Kemp, Murray C, Long, Ngo Van
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Publisher Summary This chapter describes a productive process involving the point input of labour, the repeated input of land, and the point output of forest products and then describes the process in a model of the economy as a whole. The optimal duration or maturity of the process is to be determined in terms of the parameters of the model.
Kemp, Murray C, Long, Ngo Van
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Computational Statistics, 2013
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu-Chuan Chen +3 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu-Chuan Chen +3 more
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Journal of Classification, 2019
The prediction of the values of ordinal response variables using covariate data is a relatively infrequent task in many application areas. Accordingly, ordinal response variables have gained comparably little attention in the literature on statistical prediction modeling.
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The prediction of the values of ordinal response variables using covariate data is a relatively infrequent task in many application areas. Accordingly, ordinal response variables have gained comparably little attention in the literature on statistical prediction modeling.
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2021
Forests are a unique study ecosystem that can reveal global insights into the patterns and processes associated with biological invasions via their natural diversity, as well as their diversity of anthropogenic interactions. In this chapter we focus on emblematic species groups in different forest ecosystems, giving temperate and tropical examples of ...
Tommaso Sitzia +5 more
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Forests are a unique study ecosystem that can reveal global insights into the patterns and processes associated with biological invasions via their natural diversity, as well as their diversity of anthropogenic interactions. In this chapter we focus on emblematic species groups in different forest ecosystems, giving temperate and tropical examples of ...
Tommaso Sitzia +5 more
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When Is a Forest Not a Forest?
Journal of Forestry, 2002Abstract Numerous published and legitimate definitions of forest exist. Most can be grouped into three categories–those that describe administrative units, those that describe land cover, and those that mean a type of land use–and then there are some miscellaneous types.
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Forests and Forest Environments
2005North-western Europe has on various counts a very heterogeneous character. Crystalline and metamorphic bedrocks of various ages and Tertiary and Quaternary deposits define its geology and geomorphological features. The area belongs to several climatic zones and parts of it went through quite different processes during their Quaternary development.
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Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2017
Random forests are among the most successful methods used in data mining because of their extraordinary accuracy and effectiveness. However, their use is primarily limited to multidimensional data because they sample features from the original data set.
Saket Sathe 0001, Charu C. Aggarwal
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Random forests are among the most successful methods used in data mining because of their extraordinary accuracy and effectiveness. However, their use is primarily limited to multidimensional data because they sample features from the original data set.
Saket Sathe 0001, Charu C. Aggarwal
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