dataesr/scientific-paragraphs-categorization huggingface dataset:
Unlike  Monteiller et al. (2013)  who use the DSM method to compute the incident wavefield in a radially symmetric global earth, we use the spectral-element AxiSEM software to compute the 3-D incident wavefield  (Nissen-Meyer et al. 2014). AxiSEM is a spectral element solver that allows one to infer 3-D wavefields in a global axisymmetric earth for fullmoment tensor sources from four 2-D simulations  (Nissen-Meyer et al. 2007a ,b, 2008). Its use greatly decreases the memory requirements of simulations performed with DSM in a radially symmetric earth  (Takeuchi et al. 2000)  or the computational burden of the SPECFEM3D_GLOBE code in a fully 3-D earth  (Komatitsch & Tromp 2002). A cheaper alternative would have been to use a F-K method  (Kennett 1983; Zhu & Rivera 2002; Tong et al. 2014)  under the assumption that the wavefield reaching the lithospheric target is planar.
The CIFALPS experiment The CIFALPS array (for China-Italy-France-ALPS seismic transect)  (Zhao et al. 2015)  was deployed from June 2012 to September 2013 between the Rhône valley in France and the Po plain in Italy (Fig. 3a ). The network was composed of 56 three-components broad-band stations from which 46 formed a 350 km long transect across the Western Alps from SE France to NW Italy  (Zhao et al. 2016b ). The additional 10 stations were located off-profile 40 km to the North and South to fill gaps in French and Italian permanent networks (network code FR: RESIF (1995); network code GU: University of Genova (1967); network code IV: INGV Seismological Data Centre (1997)). On average, the interstation spacing was less than 10 km for the transect with a 5 km densification in the internal Alps.
Another possibility would be to interpret this anomaly as the signature of the fluid-induced serpentinized mantle resulting from the subduction the alpine ocean passive margin. Further investigations with AlpArray and CIFALPS-2 data will help in improving the data coverage and hence the resolution of the seismic images at greater depths. FWI of the crust as well as the upper mantle down to 600 km depth might give new clues about the fate of the European slab.
C O N C L U S I O N S We presented the results of a multiparameter 3-D elastic FWI of the CIFALPS teleseismic data set recorded in the Western Alps. We showed that FWI, by using the full information content of the seismograms, is able to generate multiparameter images of the lithosphere with unprecedented resolution. Particularly, we emphasized the ability of FWI to recover reliable density information on the upper lithosphere. Recovered images of the Western Alps lithosphere agree well with previous receiver function, LET and teleseismic tomography studies as well as regional gravity measurements. Our models nicely display sharp crustal structures of the Western-Alps, notably, the Ivrea body and the Moho boundary and support the idea of a continental subduction event during the Alpine orogeny.
Additionally, the revealed structures of the lower lithosphere support the hypothesis of a slab detachment or slab tear in the Western Alps. We show that the application of FWI at lithospheric scale fills the resolution gap between highly resolved crustal structures provided by receiver function analysis and classical tomographic images of the upper mantle. Further applications may be considered for more complex elastic properties of the medium using the transverse component or even bigger time window to include first S-wave arrival and its multiples. Added constrains on density reconstructions may be provided if one consider a joint inversion with gravity data. The new data sets obtained from current AlpArray or future CIFALPS-2 experiments will provide additional lithospheric information on the Alpine Belt and may confirm current interpretations.
A C K N O W L E D G E M E N T S We warmly thank L. Combe for invaluable help in the code development, especially for designing the domain decomposition library that allowed high-performance parallelism, and J. Virieux who encouraged the use of the CIFALPS data set and motivated fruitful discussions. This study also benefits from discussions with E. Bozdag, Y. Rolland, J. -X. Dessa and A. Sladen. The CIFALPS project is funded by the State Key Laboratory of Lithospheric Evolution, China, the National Natural Science Foundation of China (Grant 41625016) and by a grant from Labex OSUG@2020 (Investissements d'avenir -ANR10 LABX56, France). This study was partially funded by the SEISCOPE consortium (http://seiscope2. osug. fr), sponsored by BP, CGG, CHEVRON, EXXON-MOBIL, JGI, PETROBRAS, SAUDI ARAMCO, SCHLUMBERGER, SHELL, SINOPEC, STATOIL, TOTAL and WOODSIDE.
In our study, we go a step further with the direct implementation of the non-conservative transport of C through the inland water network into the ORCHIDEE land surface model  (Krinner et al. , 2005). This approach has the advantage of accounting for the effects of the lateral exports on the carbon budgets of terrestrial ecosystems and could thus help refine the assessment of the terrestrial C sink and its feedback on the climate system. The newly developed model branch, called ORCHILEAK, represents DOC production from soils and canopy, DOC and CO 2 leaching from soils to river headstreams, DOC decomposition and CO 2 evasion to the atmosphere during its lateral transport in rivers, as well as exchange with the soil carbon and litter stocks in riparian wetlands. The production and leaching of DOC relies on a new soil carbon module ORCHIDEE-SOM  (Camino Serrano, 2015)  with a vertically resolved soil column.
Model developments ORCHILEAK is based on the recent model branch ORCHIDEE-SOM (Camino  Serrano, 2015)  which relies on a novel module representing the vertical distribution of soil organic carbon (SOC) and associated transport and reaction processes. These processes include the production, consumption, adsorption, desorption and transport of DOC within the soil column as well as DOC exports from the soil column by drainage and surface runoff. In this study, the module is upgraded to represent DOC cycling in tropical rainforests, in particular by adding fluxes of DOC from the atmosphere and canopy with throughfall and by distinguishing soil carbon processes on non-flooded and flooded soils, including the direct input of DOC and CO 2 from the decomposition of submerged litter and soil carbon to the water column.
The trunk version of ORCHIDEE, as well as the branch ORCHIDEE-SOM, includes a river routing module  (Guimberteau et al. , 2012; Polcher, 2003)  that simulates the lateral transfer of water from one grid to another, representing the river channel as well as connected wetlands. Here, this routing module has been upgraded with a tracer transport equation to simulate the fluxes of DOC and CO 2 along the fluvial network, distinguishing two pools of DOC: labile and refractory DOC. In addition, the representation of the floodplain dynamics is improved in this study to better reproduce the seasonal flooding in the Amazon basin, which is a major controlling factor of the water  (Guimberteau et al. , 2012)  and carbon flow dynamics along the river network  (Richey et al. , 1990). ORCHIDEE can be run at different spatial and temporal resolutions.
Here, in line with  Guimberteau et al. (2012) , the model runs for calibration and model testing were performed at 1 • spatial resolution over the period 1980-2000, using the regional climate and wetland forcing for the Amazon from  Guimberteau et al. (2012) ; forcing of land cover and land use change after  Belward et al. (1999) ,  Olson et al. (1983)  and  Hurtt et al. (2006) ; river flow directions from  Vörösmarty et al. (2000) ; and soil parameters after  Reynolds et al. (1999)  and the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009). The necessary forcing data are listed in Table  1. As temporal resolution, we use the default 30 min time step for all vertical exchanges of water, carbon and energy between atmosphere, vegetation and soils, as well as the default 1-day time step for the lateral routing of water.
(2006). For each of the 13 ORCHIDEE plant functional types (PFTs) which are potentially present in a grid cell, the wet deposition of DOC onto the canopy (F WD2can ) and the direct precipitation of DOC onto the ground (F WD2ground ) directly scales to the corresponding water fluxes simulated in the hydrology module. According to our simulation, F WD contributes to only about one-third of the F TF at our calibration site (14. 9 g C m -2 yr -1 ;  Johnson et al. , 2006). Thus we assumed that the unaccounted flux of 10 g C m -2 yr -1 must originate from dry deposition onto the canopy or leaf leachates. We further assumed that this dry addition of soluble organic carbon (F add2can ) does not vary over time and scales to the leaf biomass (which, in the model, is directly related to leaf area). Based on the simulated leaf biomass of 457 ± 1 g C m -2 for tropical rainforests at the field-site location, we calibrated F add2can at 6 × 10 -5 g C per day and per gram of carbon in the leaf biomass (Eq.
Production and export of soil DOC through the terrestrial-aquatic interface ORCHILEAK is largely based on ORCHIDEE-SOM, the new soil carbon module simulating microbial production and consumption of DOC, its adsorption and desorption onto or from mineral surfaces, the vertical advective and diffusive fluxes of DOC within the soil profile, and the exports of DOC from the soil via surface runoff and drainage  (Camino Serrano, 2015). Consistent with the soil hydrology module  (Campoy et al. , 2013; de Rosnay et al. , 2002) , the carbon dynamics are resolved using a discretization of a 2 m soil profile into 11 layers geometrically increasing in depth and running at a 30 min time step  (Camino Serrano, 2015). DOC is produced from the decomposition of litter and SOC (Eqs. 37-40), and consumed by further decomposition  (Eqs.
The spatial distribution of Podzols and Arenosols was derived from the Harmonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009). To determine the spatial distribution of igapo forest soils, we used the PRIMA forcing for swamps in combination with the boundaries of the Rio Negro catchment as derived from the 0. 5 • river network (Fig. 6 ). F dec terr,SOC pool,grid x,v,l,t = S soil,SOC pool,grid x,v,l,t • k SOC pool 1 + % poorsoils grid x • 1 -% flood total,grid x,t (37) F dec flood,SOC pool,grid x,v,l,t = S soil,SOC pool,grid x,v,l,t • k SOC pool 1 + % poorsoils grid x • % flood total,grid x,t 3 (38) F dec terr,litter pool,grid x,v,l,t = S soil,litter pool,grid x,v,l,t • k litter pool 1 + % poorsoils grid x • 1 -% flood total,grid x,t (39) F dec flood,litter pool,grid x,v,l,t = S soil,litter pool,grid x,v,l,t • k litter pool 1 + % poorsoils grid x • % flood total,grid x,t 3 (40) F dec terr,DOC pool,grid x,v,l,t = S soil,DOC pool,grid x,v,l,t • k DOC pool 1 + % poorsoils grid x • 1 -% flood total,grid x,t (41) F dec flood,DOC pool,grid x,v,l,t = S soil,DOC pool,grid x,v,l,t • k DOC pool 1 + % poorsoils grid x • % flood total,grid x,t 3 (42) The soil carbon module distinguishes 3 different pools of DOC depending on the source material: active, slow and passive  (Camino Serrano, 2015).
Export of dissolved CO 2 through the soil-water network interface Although mineralization of litter, SOC and DOC in the soil is simulated in ORCHIDEE, the CO 2 partial pressure in the soil air and soil solution of the different layers is not represented. Thus, we implemented simple estimates of these soil-derived CO 2 inputs in order to reproduce the observed CO 2 evasion fluxes from the water surface of the fluvial network. For simulating the export of CO 2 with surface runoff and drainage, we use fixed concentrations of 20 mg C L -1 (pCO 2 of 50 000 µatm at 25 • C) and 2 mg C L -1 (pCO 2 of 5000 µatm at 25 • C), respectively, derived from reported literature values  (Davidson et al. , 2010; Johnson et al. , 2008; Saunders et al. , 2006). The lateral exports of CO 2 dissolved in soil water are then calculated by multiplying these CO 2 concentrations with the water fluxes from surface runoff and drainage simulated at half-hourly time step in the soil hydrology module (Eqs.
As empirical data for calibration and validation are limited, we started with parameter values taken from the literature or based on assumptions. The parameter red DOC,base was set to a value of 0. 2 following  Braun (2002). The S fast+slow,H 2 O,ref was set to 160 mm, which is about the 90th percentile of S fast,H 2 O + S slow,H 2 O within the Amazon basin. The decomposition rates for labile and refractory DOC within the inland water network, k DOC lab = 0. 3 day -1 and k DOC ref = 0. 01 day -1 , were also taken from the literature (see Sect. 2. 2. 5). Nevertheless, we made sure that the simulated DOC concentrations in F RO and F DR are comparable to values reported in the literature, and that deviations between simulated and observed DOC concentrations in the rivers are minimal. In that context, we performed a sensitivity analysis with regard to model performance for changes in S fast+slow,H 2 O,ref , red DOC,base , k DOC lab and k DOC ref.
Therefore, the good match between observed and simulated inundation in these regions highlights the good performance of our new flooding module in ORCHILEAK (Fig. 8b ). Nevertheless, it is important to keep in mind that while the overall seasonality of inundation is well reproduced in all regions, the total inundated area across the Amazon basin is likely underestimated because of our choice of forcing data. After recalibrating the outflow velocity from the floodplains and reducing the amount of water redirected to swamps (τ flood = 1. 4, f swamp = 0. 1), the simulated discharges are in general quite close to those simulated by  Guimberteau et al. (2012)  (Fig. 10, Table 3 ). In the southern tributaries of the Amazon basin (Rio Jurua, Purus, Madeira, Tapajos, Xingu), we overestimate the discharge during highflow periods (February to April) while for the rest of the year our simulation is well in line with observations.
Transport and decomposition of DOC in the river network To evaluate the simulated DOC concentrations and fluxes, we used data from the CAMREX (Carbon in the Amazon River Experiment) program  (Richey et al. , 2008)   al. , 2006) , which was designed to capture the land-ocean matter transfer through the Amazon River network from the Andes down to Obidos with regular sampling campaigns; and the data from the study of  Moreira-Turcq et al. (2003). Comparing observed versus simulated DOC concentrations, we were able to reproduce the average concentrations at least in the main stem of Rio Solimoes-Amazon River and in the Rio Negro (Fig. 13 ). However, apart for the Rio Negro, we generally underestimate the seasonal variability of DOC concentrations. For Obidos, the most downstream sampling location for which we have data, the mean simulated DOC concentration deviates by only -2 % from the observed ones (Table  9 ).
Surface areas of small (width < 100 m) and large (width ≥ 100 m) rivers (A river small , A river large ) are taken from Lauerwald et al. (2015). Of importance for representation of DOC cycling in watersheds of black-water rivers is the identification of "poor soils" (Podzols, Arenosols and soils in black-water swamps), which we derived from the Harmonized World Soil Database (HWSD, FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009) and %swamp.
Acknowledgements. Funding was provided from the  French National Research Agency  (" Investissement d'Avenir ", no. ANR-10-LABX-0018 ), from the  Université Libre de Bruxelles , through the  Bureau des Relations Internationales (BRIC) , the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 703813  for the  Marie Sklodowska-Curie European Individual Fellowship "C-Leak " and the  European Union 's  Horizon 2020 research and innovation program  under  Marie Sklodowska-Curie  grant agreement no. 643052  ( C-CASCADES  project). Marta Camino-Serrano  and  Matthieu Guimberteau  acknowledge funding from the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P. We are particularly grateful to  Camille Risi  who shared her version of the river routing code with tracer transport equations at the early stage of the project, as well as  Laurent Bopp  and  Marion Gehlen  for discussions regarding the river's carbonate system.
Code availability. ORCHILEAK is derived from ORCHIDEE with the modifications presented in Sect. 2. A description of the general code of ORCHIDEE can be found here: http://forge. ipsl. jussieu. fr/ orchidee/browser#tags/ORCHIDEE_1_9_6/ORCHIDEE. For OR-CHIDEE, the main part of the code was written by  Krinner et al. (2005). For the general description of the basic river routing scheme see d 'Orgeval et al. (2008). For the upgraded soil C module see Camino  Serrano (2015). The Supplement to this paper contains the source code of ORCHILEAK and a short instruction how to install the code. For more general information about how to install ORCHIDEE and its different branches, please consult the user guide: http://forge. ipsl. jussieu. fr/orchidee/ wiki/Documentation/UserGuide.
(1991) (b) Depth Soil carbon stocks (kg C m -2 ) This study HWSD a Literature b 30 cm 5. 2 ± 1. 7 6. 4 ± 5. 6 4. 0-4. 8 100 cm 7. 4 ± 2. 3 11. 2 ± 9. 5 7. 9-9. 0 200 cm 8. 3 ± 2. 6 Ceddia et al. (2015)armonized World Soil Database (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2009). bAfter literature review inCeddia et al. (2015).
6 to 3. 0 mm. The SPIDER software package  (Frank et al. , 1996)  was used for most subsequent steps. The CTF was corrected by multiplying each image by the theoretical CTF, both reversing phases where they need to be reversed and improving the Signal-to-Noise ratio. This multiplication of the images by the CTF is actually a Wiener filter in the limit of a very poor SNR. The program e2helixboxer within EMAN2  (Tang et al. , 2007)  was used for boxing long filaments from the micrographs, and 546 such boxes of varying length were extracted. Overlapping boxes, 384 px long with an 8 px shift between adjacent boxes (~twice the axial rise per subunit) were extracted from these long filaments, yielding 215,549 segments that were padded to 384 Â 384 px. The CTF determination and particle picking came from the integrated images (all seven chunks), while the segments used for the initial alignments and reconstruction came from the first two chunks.
Model-building with Rosetta Models were initially constructed using RosettaCM  (Song et al. , 2013). As template structures we considered the cryoEM structure of SIRV (PDB id: 3J9X) as well as the crystal structures of the two components (PDB ids: 3FBZ and 3FBL). Sequence alignments for both molecules in the asymmetric unit were generated with hhsearch  (So ¨ding et al. , 2005). Threaded models were then docked into density. Initially, visual inspection was used to assign each of the two components into the asymmetric unit. Modelling was carried out in the context of the entire assembly, using the helical parameters determined from map construction. Using RosettaCM, a total of 5000 models were generated, of which the top 10% were chosen by energy and the top model was chosen by agreement to density.
Modeling the membrane Simulations were performed using GROMACS molecular dynamics package  (Pronk et al. , 2013)  using the CHARMM36 parameter set  (Best et al. , 2012; Klauda et al. , 2010)  modified to include GDGT-0 lipids following the example of previous work on tetraether lipids  (Shinoda et al. , 2004). In the absence of definitive mass spectrometry data on the headgroups of the GDGTs in the viral envelope, phosphoinositol was selected as a common archaeal tetraether lipid headgroup. To construct models of the AFV1 envelope, lipids were placed in a cylindrically symmetric arrangement around a capsid cylinder 10 nm in length. The lipid lateral density, fraction inward versus outward facing, and radius of the envelope cylinder were all systematically varied in initial system setup, and the resulting simulations were assessed with regard to physical stability and maintenance of the capsid thickness observed on cryo-EM.
Emphasis has been put in recent ecological research on investigating phylogenetic, functional and taxonomic facets of biological diversity. While a flourishing number of indices has been proposed for assessing functional diversity, surprisingly few options are available to characterize functional rarity. Functional rarity can play a key role in community and ecosystem dynamics. We introduce here the funrar R package to quantify functional rarity based on species trait differences and species frequencies at local and regional scales. Because of the increasing availability of big datasets in macroecology and biogeography, we optimized funrar to work with large datasets of thousands of species and sites. We illustrate the use of the package to investigate the functional rarity of North and Central American mammals.
Functional Uniqueness and Taxon Restrictedness respectively assess these two aspects at the regional scale. The four indices together provide a framework for characterizing functional rarity (Figures  1  & 2 ). Because functional rarity is expected to play a major role in ecosystem and biodiversity dynamics, the indices can be used to assess the influences of rare trait values on local and regional dynamics  (Ricotta et al. , 2016; Violle et al. , 2017). We here introduce an R (R Core Team, 2016) package named funrar, to quantify functional rarity based on abundance or occurrence data and trait data. The funrar package, available through the Comprehensive R Archive Network (CRAN), computes Functional Distinctiveness, Functional Uniqueness, Taxon Scarcity, and Taxon Restrictedness and is optimized to handle high-dimensional data (large number of sites and/or large number of species) using sparse matrix algebra.
, 2017). In funrar, a functional distance matrix can be calculated from a table of one or several traits measured for each taxa with compute_dist_matrix(). Different kinds of traits (continuous, categorical, ordinal) can be scaled or weighted in various ways when combined  (Pavoine et al. , 2009). By default compute_dist_matrix() computes the unweighted  Gower's (1971)  distance because it covers classes of trait data and makes them comparable, but the user can also specify euclidean or manhattan distances. It is possible to scale traits using the center and scale arguments when traits are continuous. Functional Distinctiveness and Functional Uniqueness are computed from the functional distance and species composition matrices. Functional Distinctiveness (Di, distinctiveness()) of a species, i.
Restrictedness nearly equals one when a species is present in a single site (examples on Figure  1 ): R i =1- K i K tot , ( 5 ) where Ki is the number of sites where species i occurs and Ktot the total number of sites in the dataset. Ri equals one when the species is completely absent from the dataset. Restrictedness can also be computed for predicted species distributions from ecological models  (Guisan & Thuiller, 2005). A threshold of the predicted probabilities of occurrence  (Liu et al. , 2005; Jiménez-Valverde & Lobo, 2007)  is then used to derive the matrix of species occurrences per pixel. Because of the increasing availability of large-scale and intensive datasets in ecology  (Hampton et al. , 2013) , a site-species matrix can contain thousands of sites and thousands of species. However, as not all species are everywhere, site-species matrices are usually filled with many zeroes.