dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We acknowledge funding from the  Swiss National Science Foundation (SNSF)  through the  CarboCount-CH Sinergia Project  (grant  CRSII2 136273 ) and from the  European Union 's  Horizon 2020 research and innovation programme  through the  CRESCENDO  project (grant agreement No  641816 ). The computing time was provided by the  Swiss National Supercomputing Centre (CSCS). We also thank  Urs Beyerle  for support with downloading and storage of EURO-CORDEX data and  Richard Wartenburger  and  Martin Hirschi  for their help with downloading and processing some of the observation-based data. We are grateful to  Wim Thiery ,  Paul Dirmeyer  and an anonymous reviewer for providing useful comments on the manuscript.
(1998 ), LaRoche et al. (2005) , and  Sarthou et al. (2005)  multiplied by the minimum/maximum N : C ratio Table  1. Growth rates of PFTs at 0 and 20 • C (µ 0 and µ 20 ) and rate increase for a 10 • C increase in temperature (Q 10 ). The uncertainty in µ 0 and Q 10 represents ±1 standard deviation from an optimal parameter value in the parameter space. Full references for the phytoplankton growth rate data are provided in the Supplement. The zooplankton growth rate data are from the published data synthesis cited here. (0. 33) to account for the acclimation of nutrient saturated vs. nutrient limited growth  (Morel, 1987). For picophytoplankton, reported values for the half saturation extend over 3 orders of magnitude. We assigned low half-saturation values as these organisms grow even under very low nutrient conditions  (Timmermans et al.
5-2 based on our model simulations). All data are monthly except for mesozooplankton, which are seasonal. Chl concentration is from SeaW-iFS satellite averaged over 1998-2009 and interpolated to the same grid. The model output was averaged over the same time period, and sampled for the same month and on the same grid box as the observations. The data intervals were chosen to include approximately the same number of grid boxes, except for macrozooplankton where the lowest interval was set to 0-0. 05 µmol C L -1 because of the large number of grid boxes with very low macrozooplankton concentration. Ten concentration intervals were used for the nutrients (Fig. 3 ). Chlorophyll concentrations covary with NO 3 concentrations at < 3 µmol L -1 , and with PO 4 in the range 0. 3-0. 5 µmol L -1 (Fig.
Acknowledgements. The structure of PlankTOM10 was developed through a series of seven international workshops funded in part by the  Max Planck Institute for Biogeochemistry in Jena, Germany , and hosted by the  Villefranche Oceanography Laboratory, France. We thank  C. Klaas  and  D. Wolf-Gladrow  for their input on model development and interpretation,  S. Pesant  for support with data compilations,  G. Madec  and the  NEMO team  for assistance with the physical model,  A. Tagliabue  for providing the iron database,  N. Mahowald  for providing dust deposition fields,  V. Smetacek  and one anonymous referee for their comments. C. Le Quéré  and  E. T. Buitenhuis  were funded by  UK-NERC  projects  NE/C516079/1  and  NE/K001302/1 , and  European Commission  project  EMBRACE 282672. R. Moriarty was funded by  EU   FAASIS  project  MEST/CT/2004/514159.
Experimental methods Field work took place around mid-May in 2014 and 2015, just before the onset of snow melt. It consisted of the measurement of snow depth at several hundred sites, the observation of snow stratigraphy in snow pits, and of the measurement of vertical profiles of snow density, thermal conductivity and specific surface area in these pits. Snow depth was measured with an avalanche probe. Density was measured with a 100 cm 3 metal box cutter 3 cm high, therefore providing a vertical resolution of 3 cm. Snow thermal conductivity was measured by inserting a heated needle probe (TP02 model from Hukseflux) horizontally into the snow  (Domine et al. , 2012). The needle was connected to a data logger. The principle is to monitor the temperature of the needle as it is heated for 100 s at constant power.
Other instruments measure snow height, snow and ground temperature, and thermal conductivity; however, these are not used as forcing data, but rather for model evaluation. Because of the summer active-layer thaw, the CNR4 radiometer shifted from its horizontal position in late August 2014, causing errors in the radiation measurements. Precipitation was not measured either, so for this and possible data gaps, we used ERA-Interim reanalysis data  (Dee et al. , 2011). When field data were available for comparison, ERA-Interim data were corrected to better match local meteorological conditions using the method of  Vuichard and Papale (2015). ERA-Interim amounts of precipitation were not modified, but the phase was recalculated using measured local temperature. With forcing data, Crocus calculates the energy budget of the surface and of each snow layer.
Acknowledgements. This work was supported by the  French Polar Institute (IPEV)  through grant  1042  to FD and by  NSERC  through the discovery grant program to FD. The  Polar Continental Shelf Program (PCSP) efficiently provided logistical support for the research at Bylot Island. We are grateful to  Gilles Gauthier  and  Marie-Christine Cadieux  for their decades-long efforts to build and maintain the research base of the  Centre d'Etudes Nordiques.
Section 3 presents an evaluation of the stability and accuracy of the system as well as the spatial resolution. Based on this knowledge of the performance, it then analyzes the snow depth time-series in terms of physical processes at both sites. Section 4 evaluates the benefit of this instrument compared to single ranging probes in the context of the estimation of the mean snow depth. The present paper focuses on this estimation and does not cover the wide scope of spatial information content of the dataset which will be addressed in future work.
At last, permanent artifacts in the scanned area (stacks, spheres,. ) are removed with specific ad hoc criteria. 5. The surface formed by the points (x, y, z) is resampled on a regular grid using the bilinear interpolation implemented in matplotlib. mlab. griddata Python library (version 1. 5. 1). The same grid is used throughout the season allowing easy calculation of the snow depth and other statistics with weighting. This approach is simple but may degrade the resolution compared to mesh based approaches  (Deems et al. , 2013). The chosen spacing was 2 and 3 cm at Col de Porte and Dome C respectively, reflecting the difference of installation height. The procedure requires a few site-specific inputs, i. e. the rotation matrix and the reference plane. To determine the rotation, we installed four spheres in the scanned area and used a professional laser level to ensure they were in the same horizontal plane with an accuracy estimated to be ±5 mm.
The position of each sphere was determined by first selecting only the points of the scan that reasonably lay in the sphere vicinity based on its expected position (first guess), and then minimizing the norm-1 of the function measuring the distance between the points (x, y, z) and the sphere surface: (x -x c ) 2 + (y -y c ) 2 + (z -z c ) 2 -R where R = 7. 3 cm is the known sphere radius and (x c , y c , z c ) is the unknown sphere center in the unrotated coordinate system (obtained after step 2). The minimization uses the Random Sample Consensus algorithm (RANSAC,  Fischler and Bolles, 1981)  to automatically detect and remove outliers. A plane is then fitted onto the four spheres centers by least square fitting and the rotation R which puts this plane horizontal is deduced using the Cloud Compare software.
WMO-SPICE data at Col de Porte For the evaluation of the RLS, we use data collected in the framework of the WMO-SPICE experiment at Col de Porte. Two the vertical so that the measured footprint was not perturbed by snow accumulating on the arm and sensors and occasionally falling down or melting. However, with such an angle, the footprint position in the horizontal plane moves during the season as a function of the snow depth. It will be shown to be a limitation for the comparison with RLS data. The two snow depth timeseries were calculated using range measurements, the tilt angles precisely measured for each sensor, and the offsets determined using the snow-free period : d Dimetix = 4. 2603 + 0. 92666 × r Dimetix (2) d Jenoptik = 4. 2835 + 0. 93643 × r Jenoptik (3). Measurements were carried out every minute during the entire period of operation of the RLS at Col de Porte.
Col de Porte In the absence of spheres, the stability at Col de Porte is evaluated using the 2-axis inclinometer fitted on the laserscan. Figure  6  shows the daily variations along the two axis (calculated by the daily median of the 5-minute acquisitions when the scanner is not running). The time-series reveals a shift occurring the 3 November 2014 before the first snowfalls. It is most probably due to a maintenance operation on the tower and implies to discard the data before this date which is not a problem because there are several available snow-free scans after this date. From 3 November and until the end of the season, the day-to-day variations are of the order of 0. 6 • on the two axis (specifications of the sensors indicate an accuracy of 0. 2 • at -10 • C). The long term trend is of the order of a decrease of 0.
The rapid variations on the other hand are relatively random. This suggests they come from reproducibility errors of the sensors. The mean weekly standard difference of the 2 hourly data is about 0. 4 and 0. 7 cm for Jenoptik and Dimetix points on average over the season. The mean daily standard difference is about 0. 3 cm for both points. These values are very low and should be compared to the 2-σ accuracy of the lasermeter of about 0. 3 cm according to the constructor specification. The RLS angle reproducibility error was estimated to 0. 03 • which is sufficient to explain 0. 5 cm RMS (evaluated for a zenith angle of 45 • and a laserscan height of 5. 2 m). 10 The Cryosphere Discuss. , doi:10. 5194/tc-2016-67, 2016 Manuscript under review for journal The Cryosphere Published: 30 March 2016 c Author(s) 2016.
cm at the Col de Porte and 2. 4 cm at Dome C. Since the increment and azimuth speed are appropriately scaled as a function of the zenith angle during the scan, the theoretical resolution in each direction is constant over the scanned area. 11 The Cryosphere Discuss. , doi:10. 5194/tc-2016-67, 2016 Manuscript under review for journal The Cryosphere Published: 30 March 2016 c Author(s) 2016. CC-BY 3. 0 License. The effective resolution should differ because of the angle reading errors, the lasermeter range error, and the lasermeter measurement rate. It is interesting to evaluate the resolution directly from the scans rather than from the theory. However, this is not straightforward because data are not acquired on a regular grid as highlighted in Figure 10 which shows the scans of 15 February 2015 at both sites as examples.
4. 1 1 cm at the Col de Porte and 2. 4 cm at Dome C. Since the increment and azimuth speed are appropriately scaled as a function of the zenith angle during the scan, the theoretical resolution in each direction is constant over the scanned area. 11 The Cryosphere Discuss. , doi:10. 5194/tc-2016-67, 2016 Manuscript under review for journal The Cryosphere Published: 30 March 2016 c Author(s) 2016. CC-BY 3. 0 License. The effective resolution should differ because of the angle reading errors, the lasermeter range error, and the lasermeter measurement rate. It is interesting to evaluate the resolution directly from the scans rather than from the theory. However, this is not straightforward because data are not acquired on a regular grid as highlighted in Figure 10 which shows the scans of 15 February 2015 at both sites as examples.
13 ] [16] [17] [18]  A variety of useful information concerning atomic motion, diffusion pathways, diffusion constants (or at least the statistics of diffusion events) and lattice dynamics, can be extracted from MD trajectories. However, while the diffusion events can easily be seen in a global time-summation of the trajectory, as available in many MD analysis codes (for example LAMP and nMoldyn), we did not find any software to visualise and analyse the on-site motion of the diffusive species, meaning with site occupation in space and time of an atom respect to its equilibrium position. For this reason, we have implemented a MATLAB  19  code to perform the time-summation of specific atoms and their corresponding displacements, involving symmetry and adapted projections, to provide 2D maps that are visually similar to density maps.
Variation in the abundance of tree species at the community level is evident in any forest and has been studied in tropical forests since at least 1979  (Hubbell, 1979). However, only relatively recently has sufficient taxonomic and forest inventory work been carried out at sufficiently large spatial scales (>1,000 km) to explore patterns of dominance across the Amazon; the results have shown conclusively that certain tree species dominate at large spatial scales as well  (Pitman et al. , 2001; Ter Steege et al. , 2013; Ter Steege et al. , 2015). Neutral ecological models where species drift randomly in abundance over time can generate variation in abundance, but they fail to explain the consistent dominance of some tree species across the Amazon  (Hubbell, 2001; Condit et al. , 2002). This begs the question of which characteristics or traits allow certain tree species to dominate, but the absence of adequate trait datasets has limited attempts to answer this question.
There are two large-scale databases available that provide reasonable coverage of Amazonian tree species, for wood density and seed mass  (Zanne et al. , 2009; Royal Botanic Gardens Kew, 2016) , but these two traits do not appear to be related to tree abundance in the Amazon  (Ter Steege et al. , 2013; Ter Steege et al. , 2015). At smaller spatial scales, tree height has been shown to be positively correlated with abundance  (Pitman et al. , 2001; Arellano et al. , 2015) , but it is unclear if this pattern holds at the scale of the entire Amazon Basin. There has been less progress in studying variation in the range size of Amazonian trees, in large part because many areas of the Amazon remain poorly known by botanical scientists. In 1999, Pitman and colleagues noted that ''not a single tree species distribution in the Amazon basin has been reliably mapped''  (Pitman et al.
METHODS We intersected a list of all Neotropical tree genera (from http://ctfs. si. edu/webatlas/ neotropicaltree/) with a list of Amazonian plant species  (Feeley & Silman, 2009)  in order to generate a list of Amazonian tree species. The  Feeley & Silman (2009)  dataset additionally includes estimates of range size for all species. We obtained estimates for the total abundance of Amazonian tree species from Ter  Steege et al. (2013). We obtained sequences of the rbcL plastid gene for 631 Amazonian angiosperm tree genera (Table  S1 ), with 567 sequences coming from Genbank (www. ncbi. nlm. nih. gov/ genbank/) and an additional 64 genera being newly sequenced following protocols outlined in  Baraloto et al. (2012). We obtained sequences of the matK plastid gene from Genbank for 452 of the 631 genera with rbcL data (Table  S1 ).
Sequences were aligned using the MAFFT software  (Katoh & Standley, 2013). 'Ragged ends' of the sequences that were missing data for most genera were manually deleted from the alignment. Preliminary phylogenetic analyses allowed us to exclude sequences from GenBank for genera that were phylogenetically placed in a different family to that which they are thought to belong taxonomically. The final alignment can be found in Supplemental Information. We estimated a maximum likelihood phylogeny for the genera in RAxML v8. 0. 0  (Stamatakis, Hoover & Rougemont, 2008) , on the CIPRES web server (www. phylo. org). We used the default settings, including a General Time Reversible (GTR) + Gamma (G) model of sequence evolution, with separate models for the rbcL and matK genes (i. e. , a partitioned analysis).
We included sequences of Amborella trichopoda (Amborellaceae) and Nymphaea alba (Nymphaeaceae) as outgroups. This phylogeny (see Supplemental Information) was used as a starting tree for simultaneous topology and divergence time estimation in the software BEAST v1. 82  (Drummond & Rambaut, 2007). We implemented fossil-based age constraints for 25 nodes distributed across the phylogeny, using log-normal prior distributions with an offset to impose a hard minimum age (see Table  S2 ). We used a GTR + G model of sequence evolution, with separate models for the rbcL and matK genes, an uncorrelated relaxed lognormal clock, and a birth-death model for the speciation process. We conducted several preliminary runs to optimise the tuning and weight of parameters as per recommendations generated by the software.
Once parameter optimisation stabilised, we ran two separate chains for 100 million generations. The first 50 million generations of each chain were discarded as ''burn-in,'' as the posterior probability of the phylogeny did not stabilise until this point. We combined the post burn-in posterior distributions of parameters and confirmed that effective sample size (ESS) values exceeded 100 for all parameters. We then used the phyutility software  (Smith & Dunn, 2008)  to generate an all-compatible consensus tree from the combined post burn-in posterior distribution of trees. Node ages were optimised onto this consensus phylogeny as the median value for a given node across all trees in the posterior distribution that contained the node (using the TreeAnnotator software,  Drummond & Rambaut, 2007).
For each genus in the phylogeny, we calculated the mean range size and abundance for all constituent species in the  Feeley & Silman (2009)  and Ter  Steege et al. (2013)  datasets. Of the 631 genera in the phylogeny, 493 had an abundance estimate for at least one species in Ter  Steege et al. (2013). We considered the number of species for each genus in the  Feeley & Silman (2009)  dataset as an estimate of the species richness of that genus in the Amazon. As an alternative estimate, we used the Neotropical species richness estimates for genera in  Gentry (1993) , which produced highly similar results. We assessed correlations amongst these genus-level characteristics using Pearson's correlation coefficients for both the raw values and for their phylogenetically independent contrasts. We tested for phylogenetic signal for each of these genus-level characteristics using Pagel's λ  (Freckleton, Harvey & Pagel, 2002).
RESULTS The phylogeny derived from the DNA sequence dataset that we compiled spans from the Magnoliids to the Asterids, thus encompassing all major clades of angiosperms (Fig. 1 ; see Supplemental Information for newick-formatted tree file). The 64 rbcL sequences that were generated as part of this study represent ∼10% of the genera in the phylogeny, and they therefore represent a valuable contribution to our understanding of the evolutionary relationships of Amazonian tree genera. Most orders and families were monophyletic in the phylogeny with the notable (previously known) exceptions of Olacaceae and Icacinaceae, while the large-scale phylogenetic relationships are largely in agreement with those from recent, angiosperm-wide phylogenetic analyses (e. g. ,  Magallón et al. , 2015). The species richness of genera is negatively correlated with mean range size (r = -0.
1 , Table  S3 ). Diverse lineages in the Magnoliids and the Asterids show high species richness and low mean range size and abundance, including the Lamiales and multiple lineages in the Rubiaceae and Solanales. One marked exception to the general pattern in the Asterids is Lecythidaceae, which shows low species richness and high abundance. Meanwhile, many lineages in the Rosids show low species richness and high mean range size and abundance, including Euphorbiaceae, Salicaceae and Moraceae. Within the Rosids, the exception to this pattern is found in multiple lineages in the Myrtales, including the Melastomataceae, which show a pattern similar to most lineages in A) log 10 (Species richness) B) log 10 (Mean range size, km 2 ) C) log 10 (Mean abundance, # Inds) the Asterids. The Leguminosae (Fabaceae), the most genus-rich family in our dataset, does not show any significant departures from null expectations, although individual lineages therein show low species richness and high mean range size.
, 2014). Thus, small-stature may be a biological trait that spurs diversification and may also underlie the negative correlation between mean range size and species richness of genera. Small-statured lineages also show lower abundances in the datasets assembled, although this is partly, if not entirely, explained by the abundance estimates being derived from tree plots that survey individuals >10 cm diameter at breast height  (Ter Steege et al. , 2013). Meanwhile, the relationship between mean abundance and species richness of genera may simply be a by-product of correlations between range size and abundance. These are likely necessarily related as the range size of a species will restrict the total abundance that it can achieve. Our focus on tree height as a key variable does not negate a role for other phylogenetically correlated traits in contributing to the observed results.
Acknowledgment We thank the  Archaeological Survey of India  for the authorization delivers to the  Society for Archaeological and Anthropological  to survey Masol site, in collaboration with the  Mission Archéologique Franc aise en Inde. This study has been funded by the  French Ministry of Foreign Affairs and International Development , with the patronage of  Yves Coppens ,  Honorary Professor  at the  Collège de France  and member of the  French Academy of Sciences. We would like to thank the reviewer for the constructive comments.
Here we study the global distribution of the plasma density irregularities in the topside ionosphere by using the concurrent GPS and Langmuir probe measurements onboard the Swarm satellites. We analyze 18 months (from August 2014 till January 2016) of data from Swarm A and B satellites that flew at 460 and 510 km altitude, respectively. To identify the occurrence of the ionospheric irregularities, we have analyzed behavior of two indices ROTI and RODI based on the change rate of total electron content and electron density, respectively. The obtained results demonstrate a high degree of similarities in the occurrence pattern of the seasonal and longitudinal distribution of the topside ionospheric irregularities derived from both types of the satellite observations. Among the seasons with good data coverage, the maximal occurrence rates for the post-sunset equatorial irregularities reached 35-50 % for the September 2014 and March 2015 equinoxes and only 10-15 % for the June 2015 solstice.
Introduction After recent reentry of the C/NOFS satellite on November 28, 2015, the Swarm becomes the only one satellite mission providing the in situ measurements of the ionospheric plasma density for altitudes below 550 km. For many decades, in situ plasma probe measurements represent the very important data source for ionosphere research, development and validation of the empirical ionospheric models. Special attention focuses on the plasma density irregularities occurring in the topside ionosphere (above the ionospheric F2 peak), as only in situ measurements onboard low-earth-orbit (LEO) satellites can be used to study the global distribution of these ionospheric irregularities, their climatology, occurrence probability and other characteristics without taking into account the sparsity of the ground-based facilities and their operational limitation.