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Maurette M., Gounelle M., Duprat J., Engrand C. & Matrajt G. (2000). The Early micrometeorites Accretion Scenario and the Origin of Earth's Hydrosphere. A New Era in Bioastronomy, 213, 257–278.
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Gerday C., Aittaleb M., Arpigny J.L., Baise E., Chessa J.P., Francois J.M., Garsoux G., Petrescu I. & Feller G. (1999). Cold enzymes: a hot topic. Cold-adapted Organisms: Ecology, Physiology, Enzymology and Molecular Biology, , 257–275.
Abstract: R. Margesin & F. Schinner eds
Programme: 193
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Sciare J., Kanakidou M. & Mihalopoulos N. (2000). Diurnal and seasonal variation of atmospheric dimethylsulfoxide (DMSO) at Amsterdam Island Indian Ocean. J. Geophys. Res., 105(17), 257–265.
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Maggi A., Tape C., Chen M., Chao D. & Tromp J. (2009). An automated data-window selection algorithm for adjoint tomography. Geophysical journal international, 178, 257–281.
Abstract: We present FLEXWIN, an open source algorithm for the automated selection of time windows on pairs of observed and synthetic seismograms. The algorithm was designed specifically to accommodate synthetic seismograms produced from 3-D wavefield simulations, which capture complex phases that do not necessarily exist in 1-D simulations or traditional traveltime curves. Relying on signal processing tools and several user-tuned parameters, the algorithm is able to include these new phases and to maximize the number of measurements made on each seismic record, while avoiding seismic noise. Our motivation is to use the algorithm for iterative tomographic inversions, in which the synthetic seismograms change from one iteration to the next. Hence, automation is needed to handle the volume of measurements and to allow for an increasing number of windows at each model iteration. The algorithm is sufficiently flexible to be adapted to many tomographic applications and seismological scenarios, including those based on synthetics generated from 1-D models. We illustrate the algorithm using data sets from three distinct regions: the entire globe, the Japan subduction zone, and southern California.
Programme: 133;906
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. (2007). The autocyclic nature of glaciations. Bulletin de la societe geologique de france, 178, 4, 257–272.
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Polenta G. and the BRAIN collaboration. (2007). The Brain CMB polarisation experiment. New astronomy reviews, 3(4), 256–259.
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Magand, O.; Picard, G.; Brucker, L.; Fily, M.; Genthon, C. (2008). Snow melting bias in microwave mapping of Antarctic snow accumulation. TCD, 2(2), 255–273.
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JOLY D., NILSEN L., ELVEBAKK A., BROSSARD T.,. (2006).35, 255–270.
Abstract: Climate change is a key issue, especially in the Arctic. However, this general observation is not sufficient to determine the consequences of this phenomenon since the impact of climate change varies with geographical position and local conditions in different environmental contexts. The study area is located at 79?N, in Kongsfjorden (Svalbard). Botanical observations and temperature measurements, remote sensing and topographical indices are input into a Geographical Information System. These GIS layers are then computed to provide variables that can give a statistical explanation of the temperature and vegetation distribution at high resolution.
Keywords: arctic; bioclimatology; interpolation; spatial modelling
Programme: 304
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Haan D. & Raynaud D. (1998). Ice core record of CO variations during the last two millennia: atmospheric implications and chemical interactions within the Greenland ice. Tellus series a-dynamic meteorology and oceanography, 50B(3), 253–262.
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Roy Jean-Claude. (2009). (Vol. Tome 1). Bachelor's thesis, , .
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