dataesr/scientific-paragraphs-categorization huggingface dataset:
6. GlobSnow again agrees well with the data. Fewer grid cells are marked in the LEGOS data set because the correlation is poorer, and SWE is underestimated in most parts of Eurasia. LPJ-WM, SDGVM and CLM4CN all tend to overestimate SWE in the European sector, but perform well elsewhere in terms of both ρ and. In contrast, JULES significantly and systematically underestimates SWE throughout Eurasia, in many regions by more than 50 %, despite the high values of the correlation coefficients. Since all winter months were considered in estimating ρ, this is more a measure of seasonal than inter-annual variability: it essentially evaluates how well the data sets capture snow appearance and disappearance dates and the variations of SWE throughout the season. To investigate whether the models reproduce the observed inter-annual variability, correlation coefficients using only the January values were calculated for the available periods of each model.
JULES performed notably better than the other models: 63 % of its grid cells had ρ ≥ 0. 6, compared with only 32 %, 25 % and 13 % respectively for LPJ-WM, SDGVM and CLM4CN. In the calculations above, all models used their default or "native" climate drivers; these are constructed from data from the same meteorological stations and are thus expected to be similar. Hancock et al. (2013b)  argue that differences between JULES estimates and observations could arise from the precipitation data in WATCH, since the use of gauge data to bias-correct reanalysis data is inappropriate for snow. Therefore, to check whether different climate data sets could cause significant differences in SWE, we ran SDGVM with both its default CRU driver and WATCH data, and found very similar results. Hence the fact that the other models produce values of SWE consistent with field data indicates that these models are in some sense tuned to the data or that the underestimation of SWE by JULES is caused by snow process representations within the model itself (note that JULES could not be run with CRU data, since these are monthly values, while JULES requires 6-hourly data, such as those provided by its default WATCH climate data set).
0 kg m -3 and R = 0. 36 day -1 ; in other words, the densification rate increased but the fresh snow density was reduced. Figure  3  shows the temporal evo-lution of both CLASS, with the original parameters, and CLASS * , whose parameters were derived from the regression. By adopting the snow density evolution of CLASS and utilizing the synoptic data set, regression analysis suggests that, for the spatial coverage offered by the transect data in Eurasia, a value of fresh snow density within the range 68-111 kg m -3 (80 % confidence interval for t = 0 days) is appropriate early in the snow season. This is within the lower half of the range proposed by CLM4CN and includes the 100 kg m -3 value used by JULES, but the initial value of 150 kg m -3 assumed by LPJ-WM appears too high, probably as a result of compensation for the lack of snow density evolution by the model in the initial 3/4 of the snow season.
Snow density evolution Monthly values of snow density were extracted from the transect data set and compared with the monthly density obtained by JULES and CLM4CN for each month and each possible grid cell over the period 1966-1996 across the spatial extent of the data; these are the only two models containing processes controlling density. CLM4CN performs very well in describing the increase of snow density throughout the year (ρ = 0. 60), as shown in the plot of  Fig. 4 (top). When individual months are examined, low correlation is observed in October and the model overestimates snow density in March; both issues can be attributed to inaccuracies in snow timings, since these two months mark the start and end of the snow season for the region. It was indeed found that most of the areas with poor snow density correlation also exhibited poor SWE correlation or overestimation of SWE (Fig.
2 ). Snow density is affected by overburden pressure, which is taken as proportional to SWE in CLM4CN; hence improved representations of SWE magnitude and timings are likely to improve simulation of snow density evolution. In contrast, snow density evolution in JULES is characterized by a very slow increase after deposition, especially during October and November. This does not agree with the transect data set and causes JULES to underestimate density throughout the winter (Fig. 4 , bottom), perhaps as a result of its underestimation of SWE (Fig. 2 ). As discussed in Sect. 5. 1, this hinders the ability of JULES to provide accurate simulations of soil temperature.
Conclusions The first part of this study makes extensive use of field transect measurements to evaluate how well a range of Earth observation data sets and land surface models perform in describing snow dynamics across the extent of the FSU. The following was found: -GlobSnow offers an accurate SWE product which, due to its global coverage, resolution and 30-year time span, is invaluable for benchmarking SWE retrievals in land surface models when field data are insufficient, and allows for identification of regions where improvement is needed. -All the models reproduce the seasonal dynamics of SWE in Eurasia, although for LPJ-WM, SDGVM and CLM4CN there are model-specific regions where there are systematic and significant anomalies, particularly in the western sector, where none of these models captures the magnitude of SWE.
Improvements by  Burke et al. (2013)  reduced the cold bias observed by  Dankers et al. (2011)  but did not remove it; this was again attributed to underestimation of SWE. Dankers et al. (2011)  used only a small set of field data around the Arctic, but the abundance of transect records across Eurasia offers the opportunity to investigate model underestimation of SWE and correct it using the optimization methods presented in this study. A similar approach could lead to improvements in simulated snow density, although our calculations with LPJ-WM suggest this will have little effect on soil temperatures. Overall, this study demonstrates yet again the importance of systematic field measurements in evaluating and improving land surface models. From the FSU data set it was possible to assess how well SWE was reproduced by several wellknown land surface models, infer fresh snow density and snow density evolution, quantify the effects of uncertainties in SWE on simulated soil temperatures, and test modelled values of snowpack sublimation.
Acknowledgements. This study was supported by the  EU   FP7  project  Monarch-A  (grant no. 242446 ), Monitoring and assessing regional climate change in high latitudes and the  Arctic , and the  EU   FP7 project EuRuCAS  (grant no. 295068 ), European-Russian www. the-cryosphere. net/8/487/2014/The Cryosphere, 8,  487-502 , 2014.
Acknowledgments: Work in the UK was supported by  STFC   Ernest Rutherford Fellowship   ST/K004298/1  and  STFC  grants  ST/K001000/1  and  ST/K000977/1. RM is supported by the  Belgian Science Policy Office through the Solar-Terrestrial Center of Excellence. French participation in the  Cluster  project is funded by  CNES. IMAGE satellite work at the  University of California, Berkeley  was supported through a  SWRI subcontract  under  NASA  contract  NAS5-96020. We acknowledge support from the  International Space Science Institute  through funding of their  International Team on Polar Cap Arcs , and we are grateful for discussions with members of the team. Cluster data were obtained from the  Cluster Active Archive  (http://caa. estec. esa. int/caa/), and the IMAGE FUV data were provided by the  NASA Space Science Data Center  (http://nssdc.
In this issue, Benson et al. test the idea of a ''deep-time'' adaptive radiation in dinosaurs, compiling and using one of the most comprehensive phylogenetic and body-size datasets for fossils. Using recent phylogenetic statistical methods, they find that in most clades of dinosaurs there is a strong signal of an ''early burst'' in body-size evolution, a predicted pattern of adaptive radiation in which rapid trait evolution happens early in a group's history and then slows down. They also find that body-size evolution did not slow down in the lineage leading to birds, hinting at why birds survived to the present day and diversified. This paper represents one of the most convincing attempts at understanding deep-time adaptive radiations. ''It is strikingly noticeable from the fossil record and from its results in the world around us that some time after a rather distinctive new adaptive type has developed it often becomes highly diversified.
In this study, Harmon et al. [17]  examined body size in 49 (and shape in 39) diverse groups of animals, including invertebrates, fishes, amphibians, reptiles, birds, and mammals. They found strong support for the early-burst model in only two of 88 total datasets. This result raises an important question: if adaptive radiation explains most of life's diversity  [1] , how is it possible that there is so little phylogenetic evidence for early bursts of phenotypic evolution? One possibility is that early bursts are hard to detect. This can be due to low statistical power in the most commonly employed tests  [18]. It may also be due to a lack of precision in the way ''early burst'' is defined (and thus tested), as the ecological theory of adaptive radiation suggests that the rate of phenotypic   [15, 32].
In this issue, Benson et al. [22]  combine these two types of data to address whether dinosaurs show signs that they adaptively radiated. Unlike most other studies, the temporal scale of the current study is very large-in this case, over 170 million years throughout the Mesozoic era, starting at 240 million years ago in the Triassic period. This characteristic allowed Benson et al. to shed light on deep-time adaptive radiation. The authors estimated body mass from fossils by using measurements of the circumference of the stylopodium shaft (the largest bone of the arm or leg, such as the femur), which shows a consistent scaling relationship with body mass in extant reptiles and mammals  [23]. They then combined published phylogenies to obtain a composite phylogeny for the species in their body-size dataset.
This gives a glimpse into why modern birds have so many species (an order of magnitude higher than the nonavian dinosaurs) and so much ecological diversity. The use of fossils allowed Benson et al. [22]  to address deep-time radiation in dinosaurs and its consequence on present-day bird diversity. Nevertheless, the promise of using fossils to understand adaptive radiation has its limits. The paleontological dataset presented here is exceptional, yet still insufficient to explore major components of adaptive radiations like actual ecological diversification. As in many paleontological studies, Benson et al. used bodysize data to represent ecology because body size is one of the few variables that is available for most species. But it is unclear how important body size really is for ecological diversification and niche filling, because body size is important for nearly every aspect of organismal function.
Consequently, evolutionary change in body size can result not only from the competition that drives adaptive radiation, but also from predation pressure, reproductive character displacement, and physiological advantages of particular body sizes in a given environment, among other reasons  [27]. Despite the broad coverage of extinct species presented in Benson et al. [22] , the data were insufficient to study another major part of adaptive radiation: early bursts of lineage diversification. While new approaches are becoming available to study diversification with phylogenies containing extinct species  [28, 29]  or with incomplete fossil data  [30] , these approaches are limited when many taxa are known from only single occurrences. This is the case in the Benson et al. dataset, and more generally in most fossil datasets.
Acknowledgements. This study was conducted as part of the  Malina Scientific Program  funded by  ANR (Agence nationale de la recherche) ,  INSU-CNRS  ( Institut National des Sciences de l'Univers -Centre National de la Recherche Scientifique ),  CNES  ( Centre National d'Etudes Spatiales)  and  ESA  ( European Space Agency). We thank all participants to the Malina cruise for their help, especially  M. Babin  who coordinated the project,  K. Lévesque  for the logistics, and all the R/V CCGS Amundsen crew members. D. Boeuf  and  F. Humily  were supported by grants from the  French Ministry of Higher Education and Research  and from  ANR , respectively. The publication of this article is financed by  CNRS-INSU.
47°C, respectively, than predicted by the range in 373 ERSST of 3. 82 ± 0. 42°C observed in data for 1990 to 2003. We also assessed the pre-374 industrial seasonality in both coral 18 O records, here defined as the oldest period of 375 overlap between 1846 and 1876 in both time series (T ab. 2). The seasonality in both 376 coral 18 O records is lower in NEP and BVB by 1. 27 and 0. 97°C, respectively, than 377 predicted by the range of 3. 62±0. 28°C observed in ERSST between 1854 to 1876 378 (T ab. 2). 379 Sr/Ca ratios obtained for the SA coral covered 1990-2003. The mean Sr/Ca is 380 8. 82 mmol/mol and the seasonal cycle in the SA is 0. 23±0. 05 mmol/mol or converted 381 to SST 3. 83±0. 83°C, in agreement with the range in observed ERSST and from coral 382 18 O (F ig. 5; T ab. 2). The regression equation determined from in-situ SST  (2001-383 2002)  with Sr/Ca= -0.
Summary and Conclusions 677 Here, we used a collection of fossil and modern corals and reconstructed the seasonal 678 range in SST from the Mid-Holocene to the present for the Seychelles archipelago. 679 The reconstructions of the seasonal cycle were used to assess the interplay between 680 changes in insolation and the bimodality in SST seasonality. Paleoclimate simulations 681 of a fully coupled atmosphere -ocean -sea ice general circulation model allow 682 comparison with proxy data for the Mid-Holocene around 6kyr BP. Our results reveal 683 the lowest seasonal SST range in the Mid-Holocene (6-5kyr BP) corals and around 684 2kyr BP coupled with a weak SST bimodality, while the highest range and strong 685 bimodality is observed around 4. 6 kyr BP and between 1990-2003. The climate model 686 simulations support the reduction in the seasonal range around 6 kyr BP compared to 687 the Late Holocene, with maximum reduction in WIO bimodality in austral summer 688 (reduced difference between first and second warming peak).
2). The seasonal range in both corals is lower in NEP and BVB by 0. 97 and 1. 47°C, respectively, than predicted by the range of 3. 82±0. 42°C observed in extended reconstructed SST (hereafter ERSST;  Smith and Reynolds, 2003)  data for 1960 to 1990. The seasonal range in NEP is significantly different at the 98% level (2-sided ttest) from BVB over 1960 to 1990 (Tab. 2). We also assessed the pre-industrial seasonality in both coral ! 18 O records, here defined as the oldest period of overlap in both time series (between 1846 and 1876; Tab. 2). The seasonality in both corals is statistically similar to that between 1960-1990, which also holds for ERSST data (1854-1876). Yet, the seasonality in both coral ! 18 O records is lower in NEP and BVB by 1. 27 and 0. 97°C, respectively, than predicted by the range of 3.
Ecological studies, with data on both outcome and exposure collected at the aggregate level, were excluded because of the inherent limitations of such studies. Instead, we considered all cohort and case-control studies available that provided individual outcome information and-in many cases-individual measures of exposure. The reference lists of the papers judged to be relevant at this stage were then searched for other potentially relevant papers, which were screened in turn. Members of the working group who were familiar with the research identified three additional studies that were in press at the time of the electronic search. Through this process, 201 potentially relevant papers were identified. Electronic full-text copies of those papers were made available to members of the working group, who reviewed the search results and the papers in detail and selected those studies considered relevant for inclusion in the IARC Monograph.
fr/ (Péquegnat et al. 2008). The resulting high-quality dataset provides a good opportunity to access information on earthquake sources, regional attenuation, and local site effects. The recent study of  Drouet et al. (2011)  provided updated regional attenuation relationships and amplification factors of the accelerometric stations in this area. Through this new information, we have been able to analyze the corrected source spectra of the Les Saintes earthquake sequence and to evaluate how the source affects control of the observed spectra. Based on the main-shock study of  Feuillet et al. (2011) , accelerometric stations are either located in directive sites (Guadeloupe) or anti-directive sites (Martinique), and this suitable station coverage allows us to analyze the azimuthal dependency of the radiated energy.
Acknowledgments The comments of the associate editor and two anonymous reviewers significantly improved the submitted manuscript. We thank  Ross Stein  and  Mathieu Causse  for their useful suggestions and encouraging comments. This study originates from  Yen-Shin 's master's degree dissertation research project in Earthquake Engineering and Engineering Seismology (MEEES Consortium: www. meees. org). This study would not have been possible without the huge amount of work from the  RAP network  staff who provides the seismological community with high-quality accelerometric data. We especially thank  Marie-Paule Bouin  and our colleagues of the  Observatoire Volcanologique et Sismologique de Guadeloupe  for their support. Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
Acknowledgements ERK thanks  JL Devidal  for his precious guidance during the electron probe analysis. ERK is indebted to  Cedrick O'Shaughnessy  for the final proof reading of the manuscript. The authors acknowledge financial support from the  French Agence Nationale de la Recherche  (projects: SlabFlux grant no. ANR 2009 Blanc 0338  and  DegazMag , grant no. ANR 2011 Blanc SIMI 5-6 003 ). This research was financed by the  French Government Laboratory of Excellence initiative  no. ANR-10-LABX-0006 , the  Région Auvergne , and the  European Regional Development Fund. This is Laboratory of Excellence ClerVolc contribution no. 107. The NordSIMS facility is financed and operated under a joint Nordic contract; this is NordSIMS contribution no. 377.
Discussion Remarkably, the results found confirm that smithsonite belongs to the group of calcite, since calcite structure is the most stable structure that ZnCO 3 may adopt at ambient conditions. In order to assess the reliability of our simulation, the mechanical properties of ZnCO 3 structure-phase I (R-3c space group) are evaluated. The obtained data are fitted to the Birch-Murnaghan equation of state  25  in order to obtain the bulk modulus, which is in good agreement with the experimental  15  and previous theoretical results  16. The set calculated data are listed in Table  4. The dependence of the elastic constants of ZnCO 3 -phase I as a function of pressure variation is calculated from 0 to 90 GPa. Fig. 3  shows that elastic constants increase proportion-ally with the applied pressure until 78 GPa where a clear discontinuity is noticed, especially for C 11 , C 22 , C 33 , C 12 , C 13 and C 23 curves.
Magnesite II and magnesite III with 30 atoms in unit cell were added to the resulting structures. The underlying ab initio structure relaxations and enthalpy calculations were carried out using a plane-wave method and the local density approximation (LDA) for the exchange-correlation  22  , as implemented in the Vienna Ab-initio Simulation Package (VASP)  23, 24. In addition, in order to investigate the fundamental properties of Smithsonite calculations, the exchange and correlation function was treated by means of generalized gradient approximation (GGA). Among the available GGA functional  28  , we selected AM05 29 functional, which is particularly appropriate for calculating the properties of ZnCO 3  16. The electron-ion interaction was described by the all-electron projector augmented wave (PAW) scheme  30  and the electron configurations 4s 2 , 2s 2 2p 2 and 2s 2 2p 4 were treated as valence for Zn, C, and O, respectively.
Acknowledgments This work was supported by the  National Natural Science Foundation of China  under Grants  41301379 ,  41231170 , and  41101326  and  China Postdoctoral Science Foundation  under Grant  2013M541029. The authors would like to thank  NSMC (National Satellite Meteorological Center in Beijing)  for providing FY2C satellite data. The authors greatly appreciate the careful and insightful suggestions and comments of reviewers which helped to improve the paper and data analysis.
The refractive-index gradient vector field approach establishes a connection between a tomographic data set of differential phase contrast images and the distribution of the partial spatial derivatives of the refractive index in an object. The reconstruction of the refractive index in a plane requires the integration of its gradient field. This work shows how this integration can be efficiently performed by converting the problem to the Poisson equation, which can be accurately solved even in the case of noisy and large datasets. The performance of the suggested method is discussed and demonstrated experimentally by computing the refractive index distribution in both a simple plastic phantom and a complex biological sample. The quality of the reconstruction is evaluated through the direct comparison with other commonly used methods.
To this end, the refractive index is retrieved from the same data set using also (1) the filtered backprojection algorithm for gradient projections, and (2) the regularized phase-retrieval procedure. Results show that the gradient vector field approach combined with the developed integration technique provides a very accurate depiction of the sample internal structure. Contrary to the two other techniques, the considered method does not require a preliminary phase-retrieval and can be implemented with any advanced computer tomography algorithm. In this work, analyzer-based phase contrast images are used for demonstration. Results, however, are generally valid and can be applied for processing differential phase-contrast tomographic data sets obtained with other phase-contrast imaging techniques.
In general, the reconstruction procedure can be divided into three separate steps: 1. a CT data set of phase-contrast projections is recorded with a selected XPCI technique. The intensity in these images depends on both the attenuation and the refraction (i. e. angular deflection) of the X-ray beam induced by the interaction with the sample. In this work we focus on the so-called "in plane" XPCI CT arrangement  [9] , in which the phasecontrast signal depends on the amount of the X-ray deflections in the CT reconstruction plane. 2. The deflection angles of the X-rays are calculated in the object exit plane according to the XPCI method employed in the first step. In the geometrical optics approximation the angular deflection at a point in the object exit plane is linearly proportional to the local spatial derivative of the wave phase.