dataesr/scientific-paragraphs-categorization huggingface dataset:
The flux field was constructed by generating Gaussian random fields g with an appropriately chosen power spectrum  (Font-Ribera et al. , 2012a)  to which the log-normal transformation F = exp(-ae υg ) is applied  (Coles & Jones, 1991; Bi et al. , 1992; Gnedin & Hui, 1996). Here a and υ are free parameters chosen to reproduce the flux variance and mean transmitted flux fraction  (McDonald et al. , 2006). DLA's were added to the spectra according to the procedure described in  Font-Ribera et al. (2012b). Finally, the spectra were modified to include the effects of the BOSS spectrograph point spread function (PSF), readout noise, photon noise, and flux miscalibration. Fifteen independent realizations of the BOSS data were produced and analyzed with the same procedures as those for the real data.
Acknowledgements. We thank  Carlos Allende Prieto ,  Ashley Ross  and  Uros Seljak  for stimulating discussions and  Adam Riess  for providing the data points of Riess et al. (2007) in figure 21. Funding for SDSS-III has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , and the  U. S. Department of Energy Office of Science. The  SDSS-III  web site is http://www. sdss3. org/. The French Participation Group of  SDSS-III  was supported by the  Agence Nationale de la Recherche  under contract  ANR-08-BLAN-0222. The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007(FP7/ -2013 ) ) under grant agreement no. [PIIF-GA-2011-301665]. SDSS-III is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the  SDSS-III Collaboration  including the  University of Arizona , the  Brazilian Participation Group ,  Brookhaven National Laboratory, University of Cambridge, Carnegie Mellon University, University of Florida , the  French Participation Group , the  German Participation Group, Harvard University , the  Instituto de Astrofisica de Canarias , the  Michigan State/Notre Dame/JINA Participation Group ,  Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, Max Planck Institute for Extraterrestrial Physics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia University of Washington , and  Yale University.
Second, larger cells may be required to diagnose the presence of ecosystems characterized by processes that operate over large spatial scales, or diagnostic features that are sparse, cryptic, clustered or mobile (e. g. pelagic or artesian systems). Last, larger cells allow AOO estimation even when high resolution data are limited. These considerations therefore suggest that a larger cell size is appropriate for ecosystems than recommended for species  [79]. A potential limitation of AOO estimates based on large grain sizes is that they may be inflated for ecosystems with many small, dispersed patches (e. g. forest fragments, small wetland patches), yet such occurrences may not substantially offset risks. To reduce this effect, we recommend that cells are counted as occupied only if the ecosystem covers more than 1 km 2 (1%) of cell area.
Sample Ecosystems Twenty ecosystems were selected for assessment based on the authors' areas of expertise, spanning five continents and three ocean basins (full details of assessments in Appendix S2). Although non-random, the selection encompassed terrestrial, subterranean, continental aquatic and marine aquatic environments in Europe, Africa, Asia, Australasia and the Americas and represented a wide range of thematic scales, threatening processes, data availability and levels of risk. Each ecosystem was assessed using the protocol in Fig. 3. The ecosystems assessed are summarised in Table  6.
Estimates of current change in distribution were derived from time series of maps or imagery, almost all of which required reasoned assumptions to justify interpolation or extrapolation to the required 50-year time frame. Historical changes in distribution were most commonly inferred by comparing a contemporary map with a model of environmentally suitable areas which were assumed to be occupied by the ecosystem prior to human transformation of the landscape. This approach was less suitable for marine ecosystems, which were generally Data Deficient in criterion A3. In three ecosystems (Coastal upland swamps, River Red Gum forests, Cape fynbos), models of environmental suitability were used to project future changes in distribution, with outputs of alternative plausible models used to estimate uncertainty in the projections.
Eleven of the case studies used explicit process models to guide selection of functional variables for assessment of criteria C and D. Only one of these models was quantitative, permitting simulations to estimate risks of collapse under criterion E, although data appear sufficient to support construction of such models in at least two other case studies  (1 and 8). A variety of abiotic proxy variables were used to assess environmental degradation, primarily in freshwater and marine ecosystems, including water flows and extraction rates, groundwater flows (subterranean/freshwater) nitrogen levels (both freshwater and marine ecosystems), climatic moisture, water volume, salinity, sea surface temperatures and ocean acidity. Proxy variables used to assess criterion D included the abundance of structurally important groups of species (resprouting shrubs, corals, kelp, seagrass), mobile links (birds), meso-predators (sea otters, fish), sensitive species (plankton), invasive species and threatened species.
Acknowledgments Participants of six international workshops hosted by the  Zoological Society of London (UK) ,  NatureServe (Washington DC, USA) , the  Smithsonian Institution (Washington DC, USA) ,  Tour du Valat (Arles, France) , the  Centre of Excellence for Environmental Decisions (Melbourne, Australia)  and  Centre de Suive Ecologique (Dakar, Senegal). The process to develop Red List criteria for ecosystems was launched with a resolution at the fourth  IUCN (International Union for Conservation of Nature) World Conservation Congress  in 2008, and consolidated with another resolution adopted by the fifth World Conservation Congress in 2012. French and Spanish translations of this article, as well as other key documents and publications, may be found at the website of the IUCN Red List of Ecosystems (www.
The spot size was about 180 μm in diameter and a pressure of 10 -7 Pa was maintained in the chamber during analysis. The full spectra (0-1350 eV) were obtained with constant pass energy of 150 eV and high resolution spectra with constant pass energy of 40 eV. High resolution XPS spectra were fitted and quantified using the AVANTAGE software provided by ThermoFisher Scientific. Microstructural data were investigated using TEM (JEOL 4010 at 400 kV) and high angle annular dark field scanning transmission electron microscopy (HAADF-STEM) imaging was acquired on a Cs-corrected FEI-TITAN 80-300 microscope at 300 kV. Both cross-sectional and plan-view TEM samples were prepared by standard mechanical and ion beam polishing procedures  [23]. The stoichiometric ratios of cations (1Bi:1Cr) were confirmed for both BCOSTO and BCONGO films, by STEMenergy dispersive X-ray (EDX) line scans; excluding the possibilities of Bi volatility under the present growth conditions (not shown here).
In fact, the size of these rectangular blocks decreased with decreasing film thickness for BCOSTO. Figure  4 (b) depicts the AFM image for a BCONGO film (d ~ 160 nm), whose surface morphological features differed much from that of BCOSTO films, since it revealed a dense and grainy characteristics. This is probably due to the morphology of the bottom electrode (SRO), which did not grow in step-flow regime on NGO substrates. The RMS roughness for the BCONGO film was found to be below 0. 8 nm. An XPS analysis was carried out for one of the BCOSTO (d ~ 100 nm thick) films, to analyse the cationic valency and the oxygen homogeneity both from the BCO film's surface to the depth across the BCO/SRO and SRO/STO interfaces. The in-depth XPS profiling was performed by combining a sequence of Ar + ion etching followed by the XPS data acquisition from the etched surface.
We point out that although the HAADF parameters were set to reach Z-contrast, contributions from the B-site (Cr) ions were not evident, possibly due to the large difference in atomic numbers of Cr and Bi and also the large thickness of the BCOSTO film. However, its corresponding FFT pattern (inset) revealed 45° oriented satellite reflections, similar to those observed from the SAED patterns of the cross-sectional TEM. Furthermore, the HAADF image was processed using Digital Micrograph software to filter and create two lattice images by selecting (i) the main reflections (as marked by the red circle in the inset) and (ii) 45° oriented satellite reflections (as marked by the blue circles). The resulting lattice images are displayed in Figs. 10(b ) and 10(c), respectively, both with their corresponding zoomed-in views as an inset.
Moreover, this will explain why no structural changes are observed in our HAADF images. Changes in microstructure with respect to the layer thickness were observed for BCOSTO film thicknesses varying from 10 to 130 nm (Fig. 11 ). Here we show the TEM data only for the 85 and 25 nm thick BCOSTO films [Figs. 11(a ) and 11(c)]. The BF image of the 85 nm thick BCOSTO film exhibited in the vicinity of the top surface several areas with dark contrast, probably strained regions (marked by a circle) in addition to the presence of edge-type dislocations that originate at the BCO/SRO interface, similar to those of Fig. 9(a). All the BCOSTO films grown with thickness below d ≈ 50 nm do not exhibit any evidences of edge-type dislocations. As displayed in Fig. 11(c ), a coherent growth and sharp BCO/SRO interface without any contrast variation were observed for all these films.
Acknowledgement This work was supported by the  CNRS-MPG  joint fellowship programme on nanoscience. V. K. would like to thank  Dr. R. Ranjith  and  Dr. Wei Peng  for the scientific discussions. The authors also acknowledge  Mr. Schammelt  for  PLD lab maintenance ,  Mareike Herrmann  for TEM sample preparations and Dr. Eckhard Pippel for STEM-EDX analysis.
The period July 2005-June 2006 Based on satellite information (the monthly SST composite of MODIS Aqua 9 km, disc. sci. gsfc. nasa. gov/giovanni), the SURATLANT data set (NO - 3 and DIC) and Eq. (  6 ), we built the climatological seasonal cycle of SST, NO - 3 , DIC and δ 13 C DIC for 2005-2006 (Fig. 5 ). Because DIC concentrations have slightly changed during this period (by less than the climatological cycle standard deviation;  Corbière et al. , 2007; Ullman et al. , 2009) , we chose to consider the available DIC (and NO - 3 ) data set sampled between 2001 and 2006, which is much more complete for composing the seasonality. The observations collected on board M/V Skogafoss during this period fit well within this climatology (Fig. 5b  and c ). Figure  5  thus reveals the mean seasonal cycle for δ 13 C DIC in  [2005] [2006] , with an amplitude of 0.
Acknowledgements. This study has been supported by  EU   FP7  project  CARBOCHANGE  ( 264879 ) and by  CNRS/INSU in France (O. R. E. SSS  and  OCEANS-C13  projects). We thank the  EIMSKIP Company , the captains and crews of the M/V Skogafoss and the  M/V Reykjafoss , all the very helpful embarked observers for the 13 C and DIC sampling,  A. Naamar  and the  SNAPO-CO 2  for their help in laboratory analysis and  C. Deschamps-Berger  for the MLD time series. We thank also  L. Bopp  for fruitful discussions and two anonymous reviewers for their constructive comments. Edited by:  K. Suzuki The publication of this article is financed by  CNRS-INSU.
However, virus samples are usually collected from a particular tissue from within an individual-for example from fluid or epithelium from vesicles on a single foot, from vesicles in/ around the mouth or from oesophageal-pharyngeal scrapings (known as probang samples), and it is evident that these different populations can themselves become differentiated through drift or selection for tissue-specific tropisms. Each tissue within an animal is itself comprised cells, and it is already possible to examine micro-evolutionary processes occurring at the scale of individual cells  [32, 33]. There is an obvious sequence of scales moving up from that of the individual. FMDV is commonly managed at a herd scale. However, the majority of data at the herd scale comes from the scale below-the individual animal-as typically samples are only obtained from a single animal (often the animal with the oldest lesion) and used to represent the herd in molecular phylogenetic methods that generate herd-to-herd transmission trees during an epidemic  [10] [11] [12].
(d) Animal For the animal scale, we use the consensus sequences generated from samples taken at various points during the infection of a single animal, specifically animal number two in  [26] ; data available from the EBI SRA repository (http://www. ebi. ac. uk/ena/) accession number ERP001880 from 1 May 2013. Briefly, a calf was naturally challenged by direct contact with another infected calf. A total of nine samples were then collected from this second calf at a range of days post first contact (DPFC) from different tissues. The samples were processed and sequenced on an Illumina platform in the same way as the Tissue scale described earlier. Consensus sequences for each sample were then generated from the reads aligned to the reference genome. A genealogy of the samples within the animal can then be created based on statistical parsimony analysis of the consensus sequences using the software package TCS  [37].
(e) Herd For the herd scale, we use and compare sequence data from two types of dataset. First, serial cow-to-cow infection chains from controlled experiments from  [38]  where the consensus sequences from viral samples were generated using Sanger sequencing for each animal in two independent cow-to-cow infection chains, one consisting of four animal hosts and the other six; some animals had multiple consensus sequences generated from different samples taken at varying DPFC. Second, we use data from herdto-herd transmission chains inferred from consensus sequence analysis from 2001 to 2007 FMD epidemics in Great Britain (GB). Briefly, information from consensus sequences was combined with dates of disease detection and lesion age to generate the most probably transmission trees between herds during the 2007 epidemic  [12]  and a cluster within the 2001 epidemic  [11].
In this case, the consensus sequence of the animal itself contains only a single mutation from the original inoculum at genome position 2754. All the individual samples from this animal contain the 2754 mutation and are between 0 and 3 mutations away from the animal-level consensus. Therefore, as this mutation is shared across all tissues and time points within the animal, it will very likely be passed on to the next host, enabling reconstruction of transmission trees based on shared mutations. As the underlying sequence dataset is generated by NGS approaches, it is also possible to monitor sub-consensus mutations at the within-host level by tracking the polymorphic frequency of a particular nucleotide through each of the samples. For example, figure  3b  shows the mutation frequencies of bases 1087 and 7355 across all nine samples.
(c) Herd At the herd scale we compare two types of data: (i) serial cow-to-cow infection chains from controlled experiments and (ii) serial herd-to-herd infection chains from real FMD epidemics in the UK. Experimentally manipulated serial cow-to-cow infection chains are not herds because, although they are a set of animals, their transmission is restricted to a serial one-to-one sequence of transmission unlike the one-tomany relationship that can occur when an infected individual   [26]. A consensus sequence was generated for each sample from the NGS data and a statistical parsimony tree using the software package TCS  [37]. Samples are labelled according to the animal number (A2), followed by the number of DPFC, and then the tissue type ( probang, PB; serum, SR; or foot: BRF, back right foot; FLF, front left foot; FRF, front right foot).
We see little difference between rates of substitution generated over cow-to-cow and herd-to-herd transmission events, in terms of number of nucleotide changes between units or substitution rates; however, we note the small number of datasets limits the statistical power of this inference. This could be a product of herd-to-herd transmission essentially being functionally equivalent to cow-to-cow transmission, that is to say, perhaps virus is not passaged extensively from cow-to-cow prior to transmission to a subsequent herd. Although cows do typically move in batches, it is relatively a small number that moves from farm-tofarm; Green et al. [41]  calculated a mean batch size of three animals during the 2002-2005 period suggesting only a small sample of the viral diversity within a herd will be transmitted to the next via movements.
Lythgoe & Fraser  [45]  concluded that there is preferential transmission of ancestral virus through the cycling of virus through very long-lived memory CD4 þ T cells, a process they termed 'store and retrieve'. Although we observe higher evolutionary rates between hosts compared with within hosts for FMDV, there are number of factors that could account for this difference, such as our relatively small dataset, the longer time scales of HIV, the mode of transmission and the role of long-lived memory cells in HIV transmission  [45]. We have focussed on FMDV due to the availability of sequence data at a variety of scales, but similar ideas can be applied to other livestock viruses such as bluetongue and Schmallenberg viruses. Similar ideas are obviously extendable to human viruses such as hepatitis C (e.
The experimental precision on the measured M W is therefore currently the limiting factor on the constraints. The CDF and D0 experiments at the Fermilab Tevatron proton-antiproton collider reported several direct measurements of the natural width  [7]  and mass  [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]  of the W boson, using the eν e and µν µ decay modes of the W boson. Measurements of M W have been reported by CDF with data sets collected during 1988-1989  [8] , 1992-1993  [9] , 1994-1995  [10] , and 2001-2004  [11]  and by D0 using data taken during 1992-1995  [12] [13] [14] [15]  and  2002-2006 [16]. This article describes a combination of M W measurements including recent measurements from CDF using the 2002-2007 dataset  [17]  and D0 using the 2006-2009 dataset  [18]  denoted below as CDF (2012) and D0 (2012), respectively.
B. D0 Measurement The D0 (2012) measurement uses data corresponding to 4. 3 fb -1 of integrated luminosity recorded between 2006 and 2009. D0 calibrates the calorimeter energy scale using Z → ee decays. Corrections for energy lost in un-instrumented regions are based on a comparison between the shower-development profiles from data and from a detailed geant-based simulation  [30]  of the D0 detector. The world average value for M Z  [29]  is used to determine the absolute energy-scale of the calorimeter, which is thereafter used to correct the measurement of the electron energy from the W -boson decay. This M W measurement is therefore equivalent to a measurement of the ratio of W -and Z-boson masses. This calibration method eliminates many systematic uncertainties common to the W -and Z-boson mass measurements, but its precision is limited by the size of the available Z-boson data set.
When nonmagnetic barrels were used with the APC, core orientation with the FlexIt tool would be measured for each cored section. If depth objectives could not be achieved with APC/XCB coring, the rotary core barrel (RCB) would be deployed. The downhole logging program of Expedition 340 was designed to complement the coring program, measuring continuous in situ profiles of physical properties such as bulk density, porosity, resistivity, and natural gamma radiation. Wireline logging was planned for eight of the nine primary sites. Two standard tool strings would be deployed in each logged hole. The first run would be the triple combination (triple combo) tool string, which records resistivity, bulk density, and natural and spectral gamma radiation. The second run would be the Formation MicroScanner (FMS)-sonic tool string, which records gamma radiation, sonic velocity (compressional and shear waves), and oriented high-resolution electrical resistivity images.
g. , U content in hemipelagic sediment is higher than that found in volcanic products). Single bioclastic and volcaniclastic turbiditic units throughout both holes can be traced by their uphole monotonically decreasing values in magnetic susceptibility, P-wave velocity, and density, mimicking their grading in grain size. A sharp drop in each of these values occurs at the boundaries of the turbiditic units. Where sufficiently thick, ash layers give positive peaks in magnetic susceptibility. Between 10 and 15 mbsf, magnetic susceptibility, density, and NGR data systematically differ in Holes U1394A and U1394B. This pattern is consistent with differences in lithologies retrieved from both holes. Hole U1394A is dominated by dark, dense turbidites with andesitic composition at this interval, whereas lighter colored pumice-rich deposits were cored in Hole U1394B.
Geochemistry In addition to studies done on retrieved sediment, pore water samples obtained from hemipelagic sediment were also analyzed. Pore water samples from turbiditic units were generally not obtained because it is not possible to collect meaningful pore water data from such highly permeable material. Pore water samples were largely taken from intervals dominated by pelagic carbonate. Alkalinity values increase from 3. 3 mM in the uppermost section to a consistent value of 11. 5 mM at roughly the middle of the hole before decreasing to 7. 5 mM at the base of the hole. pH values remain relatively constant at 7. 4-7. 5 throughout the hole. Of the major cations, calcium decreases from values close to bottom water in the uppermost sample to a minimum at roughly the middle of the hole before increasing toward the bottom, whereas magnesium monotonically decreases with depth, with the exception of the deepest sample (Fig.
Geochemistry In addition to studies done on the retrieved sediment, headspace gas samples and pore water samples obtained from hemipelagic sediment were also analyzed. Pore water samples from turbiditic units were generally not obtained because it is not possible to collect meaningful pore water data from such permeable material. Alkalinity values increased from 3. 5 mM in the uppermost two sections to a maximum of 7 mM at 52. 9 mbsf before decreasing to relatively constant values of 5. 4 mM from 72. 7 mbsf to the deepest sample at 195 mbsf. pH values are more variable (8. 0-7. 1) than those observed in Hole U1394B, but no consistent pattern is observable in the data, suggesting there may be some analytical artifacts in the data. Ammonium concentrations increased from 0. 1 mM in the uppermost sample to 1.
Paleomagnetism The magnetostratigraphic record obtained from the material cored at Site U1396 is in excellent accordance with biostratigraphic observations. The FlexIt orientation tool was deployed with all cores from Holes U1396B and U1396C and 12 of the 15 cores from Hole U1396A. Declination can therefore be corrected to true north for most of the site. The recorded declination data show sixteen 180° shifts in declination in the longer U1396C record and fifteen in U1396A (Fig. F10 ). Using the geomagnetic polarity timescale (GPTS) of  Cande and Kent (1995) , nine periods of normal polarity and nine periods of reversed polarity can be identified for this site. (Manga et al. , 2012).