930 resultados para Walter Benjamin


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Signatur des Originals: S 36/G02471

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Signatur des Originals: S 36/G02653

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Signatur des Originals: S 36/G13066

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Signatur des Originals: S 36/G03347

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Signatur des Originals: S 36/G03716

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Signatur des Originals: S 36/G04197

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Signatur des Originals: S 36/F12440

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Signatur des Originals: S 36/F12452

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neu übers. von Friedrich Funck

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dargestellt von Fr. Stolle

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Permafrost-related processes drive regional landscape dynamics in the Arctic terrestrial system. A better understanding of past periods indicative of permafrost degradation and aggradation is important for predicting the future response of Arctic landscapes to climate change. Here, we used a multi-proxy approach to analyze a ~4 m long sediment core from a drained thermokarst lake basin on the northern Seward Peninsula in western Arctic Alaska (USA). Sedimentological, biogeochemistical, geochronological, micropaleontological (ostracoda, testate amoeba) and tephra analyses were used to determine the long-term environmental Early-Wisconsin to Holocene history preserved in our core for Central Beringia. Yedoma accumulation dominated throughout the Early to Late-Wisconsin but was interrupted by wetland formation from 44.5 to 41.5 ka BP. The latter was terminated by deposition of 1 m of volcanic tephra, most likely originating from the South Killeak Maar eruption at about 42 ka BP. Yedoma deposition continued until 22.5 ka BP and was followed by a depositional hiatus in the sediment core between 22.5 and 0.23 ka BP. We interpret this hiatus as due to intense thermokarst activity in the areas surrounding the site, which served as a sediment source during the Late-Wisconsin to Holocene climate transition. The lake forming the modern basin on the upland initiated around 0.23 ka BP, which drained catastrophically in spring 2005. The present study emphasizes that Arctic lake systems and periglacial landscapes are highly dynamic and permafrost formation as well as degradation in Central Beringia was controlled by regional to global climate patterns and as well as by local disturbances.

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In France, farmers commission about 250,000 soil-testing analyses per year to assist them managing soil fertility. The number and diversity of origin of the samples make these analyses an interesting and original information source regarding cultivated topsoil variability. Moreover, these analyses relate to several parameters strongly influenced by human activity (macronutrient contents, pH...), for which existing cartographic information is not very relevant. Compiling the results of these analyses into a database makes it possible to re-use these data within both a national and temporal framework. A database compilation relating to data collected over the period 1990-2009 has been recently achieved. So far, commercial soil-testing laboratories approved by the Ministry of Agriculture have provided analytical results from more than 2,000,000 samples. After the initial quality control stage, analytical results from more than 1,900,000 samples were available in the database. The anonymity of the landholders seeking soil analyses is perfectly preserved, as the only identifying information stored is the location of the nearest administrative city to the sample site. We present in this dataset a set of statistical parameters of the spatial distributions for several agronomic soil properties. These statistical parameters are calculated for 4 different nested spatial entities (administrative areas: e.g. regions, departments, counties and agricultural areas) and for 4 time periods (1990-1994, 1995-1999, 2000-2004, 2005-2009). Two kinds of agronomic soil properties are available: the firs one correspond to the quantitative variables like the organic carbon content and the second one corresponds to the qualitative variables like the texture class. For each spatial unit and temporal period, we calculated the following statistics stets: the first set is calculated for the quantitative variables and corresponds to the number of samples, the mean, the standard deviation and, the 2-,4-,10-quantiles; the second set is calculated for the qualitative variables and corresponds to the number of samples, the value of the dominant class, the number of samples of the dominant class, the second dominant class, the number of samples of the second dominant class.

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Fil: Torchia Estrada, Juan Carlos.

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Fil: González de Díaz Araujo, Graciela. Universidad Nacional de Cuyo. Facultad de Artes y Diseño