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Titlebook: Vegetation of the Earth and Ecological Systems of the Geo-biosphere; Heinrich Walter Book 19853rd edition Springer-Verlag Berlin Heidelber

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發(fā)表于 2025-3-23 10:26:07 | 只看該作者
0073-1595 nsideration of secondary, man-made ecosystems. 2. Classification of the Geo-biosphere into Zonobiomes The biosphere is the thin layer of the earth‘s surface to which the phenomena connected with living matter are confined. On land, this comprises the lowest layer of the atmosphere permanently inhabi
12#
發(fā)表于 2025-3-23 16:28:25 | 只看該作者
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發(fā)表于 2025-3-23 20:46:48 | 只看該作者
Zonobiome of the Arctic Tundra Climatermafrost extends far down into the ground (Fig. 155). The depth to which the ground freezes in winter has no influence upon the vegetation, since the growth of plants depends only on the thickness of the upper soil layer thawing out in summer.
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發(fā)表于 2025-3-23 23:34:28 | 只看該作者
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發(fā)表于 2025-3-24 02:39:38 | 只看該作者
Heinrich Walterl information and to communicate it to humans in an appropriate way. Approaches, which work either on a purely analytical or on a purely visual level, do not sufficiently help due to the dynamics and complexity of the underlying processes or due to a situation with intelligent opponents. Only a comb
16#
發(fā)表于 2025-3-24 08:45:26 | 只看該作者
cated with the 17th International Conference on Algorithmic Learning Theory (ALT 2006). The two conferences shared the invited talks. This LNAI volume, containing the proceedings of the 9th International C- ference onDiscoveryScience, is structured in three parts. The ?rstpart contains the papers/ab
17#
發(fā)表于 2025-3-24 11:46:03 | 只看該作者
Heinrich Walters lowcost labor forces. Many studies on innovation attempt to identify endogenous and exogenous variables that impact innovation [.], in order to better understand the environment that promotes innovation. The author’s recent efforts have focused on developing processes for enhancing innovation that
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發(fā)表于 2025-3-24 18:02:37 | 只看該作者
19#
發(fā)表于 2025-3-24 19:24:02 | 只看該作者
Heinrich Walterlier detection is the task of individuating anomalous objects within a given data population they belong to. In this paper we propose a new technique to explain why a given data object has been singled out as anomalous. The explanation our technique returns also includes counterfactuals, each of whi
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發(fā)表于 2025-3-25 00:02:32 | 只看該作者
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