dataesr/scientific-paragraphs-categorization huggingface dataset:
The table illustrates the importance of all services as inputs in the domestic economy across the Commonwealth, but especially for South Asian and Southern and West African members. The retail and financial intermediation and business services sectors contribute the maximum to the domestic economy across the Commonwealth. Table  4  reports backward and foreign participation as a share of gross exports across Commonwealth member countries and services sectors calculated from the EORA MRIO database. Commonwealth countries with GVC participation significantly higher than the average and the total for the Commonwealth   Similarly, the UK reports the maximum use of its exports used as inputs in gross exports of other countries (forward linkages) in financial intermediation and business, maintenance & On the whole, the services sector is relatively more upstream in South Asian and African members and relatively more downstream in Caribbean members (Figure  10 ).
7 97. 1 98. 6 96. 8 97. 4 97. 3 97. 9 95. 4 97. 4 96. 2 CWMS PAK PAK NGA PAK IND PAK PAK PAK IND PAK Source: EORA MRIO database; own calculations Note: H&E = Health & education; FIBS = Financial intermediation and business services; H&R = Hotels & restaurants; M&R = Maintenance & repair; P&T = Post and telecommunications.
Model setups Similar to the philosophy adopted for initMIP-Antarctica, there are no constraints on the method or datasets used to initialize ice sheet models. The exact initialization date is also left to the discretion of individual modeling groups, thus the historical experiment length varies among groups (some groups start directly at the beginning of 2015 and therefore did not submit a historical run). The resulting ensemble includes a variety of model resolutions, stress balance approximations and initialization methods, representative of the diversity of the ice sheet modeling community (see Sect. 3. 2 for more details on participating models). The only constraints imposed on the ice sheet models are that (1) models have to simulate ice shelves and the evolution of grounding lines and that (2) models have to use the atmospheric and oceanic forcings varying in time and based on CMIP5 model outputs provided.
Historical run and 2015 conditions As the initialization date for different models varies, all models run a short historical simulation until 2015. The length of this simulation varies between 165 years for PIK_PISM1, which starts in 1850, and 0 years for the three models (DOE_MALI, PIK_PISM2 and UTAS_ElmerIce) that start Table  3. List of ISMIP6-Antarctica projection simulations and main model characteristics. Numerics are defined as follows: finite difference (FD), finite elements (FE) and finite volumes (FV). Initialization methods used are as follows: spin-up (SP), spin-up with ice thickness target values (SP+; see  Pollard and DeConto, 2012a) , data assimilation (DA), data assimilation with relaxation (DA+), data assimilation of ice geometry only (DA * ) and equilibrium state (Eq). Melt in partially floating cells is listed as follows: melt either applied or not applied over the entire cell based on a floating condition (floating condition) and melt applied based on a sub-grid scheme (sub-grid); N/A refers to models that do not have partially floating cells.
During the 86 years of the ctrl_proj experiment, the simulated evolution of ice mass above floatation varies between -51 500 and 46 700 Gt (between -130 and 142 mm SLE; see Table  B2 ). The trend in the ctrl_proj mass above floatation is significant in several models and negligible in others. As in initMIP-Antarctica, models initialized with a steady state or a spin-up tend to have smaller trends than models initialized with data assimilation. Since constant climate conditions are applied, trends cannot be considered a physical response of the Antarctic ice sheet but rather highlight the effect of model choices to initialize the simulation and represent ice sheet evolution, the lack of physical processes  (Pattyn, 2017) , the limited number or inaccuracy of observations  (Seroussi et al. , 2011; Gillet-Chaulet et al.
Impact of ice shelf melt uncertainties The effect of uncertainties in the melt rate parameterization is assessed exclusively for the standard melt parameterization framework, for which different choices of parameters can be used in a similar way by all models (exp05, exp09, exp10 and exp13 in Table  1 ). Here we assess the effect of two sources of uncertainty that affect the choice of γ 0 and the regional δ T values. The melt parameterization provides a  https://doi. org/10. 5194/tc-14-3033-2020 The Cryosphere, 14, 3033-3070, 2020 distribution of γ 0 , and the median value is used for most experiments (see Table  1 ). Two experiments (exp09 and exp10) use the 5th and 95th percentile values of the distribution to estimate the effect of parameter uncertainty on basal melt and ice mass loss. A third experiment investigates the effect of the dataset used to calibrate the melt parameterization (exp13): instead of using all the melt rates and ocean conditions around Antarctica, it uses only the high melt values near the Pine Island ice shelf grounding line ("PIGL" coefficient; see Sect.
For Antarctica, we find that uncertainties in the sea level estimates come from the spread in AOGCM forcing (see Sect. 4. 4), the melt parameterization adopted and its calibration (see Sects. 4. 6 and 4. 7), and the spread caused by the choices made by the ice flow models for their initialization and the physical processes included (see  Sect. 4. 3 and Seroussi et al. , 2019). All of these sources of uncertainty affect the results, and uncertainties in ocean conditions and their conversion into basal melt rates through parameterization lead to the largest spread of results, especially when different datasets are used for parameter calibration. Additional Antarctic mass losses of more than 20 cm SLR by 2100 under RCP 8. 5 compared to constant climate conditions are reached only for the simulations based on the PIGL calibration (Fig.
The calving front can evolve freely on a sub-grid scale  (Albrecht et al. , 2011). In addition to calving below a certain thickness threshold (here 150 m), a kinematic first-order calving law, called eigencalving  (Levermann et al. , 2012) , is utilized with the calving parameter K = 10 17 m s. Floating ice that extends far into the open ocean (seafloor elevation reaches 2000 m below sea level) is also calved off. The grounding line position is determined using hydrostatic equilibrium. Basal friction in partially grounded cells is weighted according to the grounded area fraction  (Feldmann et al. , 2014). The nonlocal quadratic melt scheme and the related datasets provided by ISMIP6 are used to compute the ice shelf basal melt in the spin-up and all "standard" experiments. For the "open" experiments, the local quadratic melt scheme is used.
For those stages, the non-sliding SIA is used on a 16 km horizontal grid. After re-gridding the output (except the geometry) onto the final 8 km grid, the model runs for 30 kyr using full model physics and a freely evolving geometry. The initial ice sheet geometry for the spin-up is based on Bedmap2  (Fretwell et al. , 2013)  and is refined in the Recovery Glacier area with additional ice thickness datasets  (Humbert et al. , 2018; Forsberg et al. , 2018). The historical simulation from January 2005 until the end of December 2014 employs the NorESM1-M-RCP8. 5 atmospheric and oceanic forcing.
The model is first initialized for 100 kyr using the average 1979-2014 SMB and surface ice temperature from  RACMO 2. 3 (van Wessem et al. , 2014). Bedrock elevation is fixed in time with data taken from the Bedmap2 dataset  (Fretwell et al. , 2013) , and geothermal heat flux data are from  (Shapiro and Ritzwoller, 2004). We then run this for 30 kyr with constant ice temperature from the first run to get to a dynamic steady state, which was our initial condition for initMIP. For IMAUICE1 we assign this steady state to the year 1978 and run the historical period 1979-2014 unforced, keeping the initial SMB constant and sub-shelf basal melting at zero. This model setup is provided for comparison with initMIP. For IMAUICE2 we assign the steady state to the year 1900 and run a 79-year experiment with constant SMB and subshelf basal melt rates estimated for the modeled ice draft at 1900 using the shelf melt parameterization of  Lazeroms et al.
C8 PIK_PISM With the Parallel Ice Sheet Model (PISM,  Bueler and Brown, 2009; Winkelmann et al. , 2011 , https://pism-docs. org/wiki/ doku. php, last access: 8 July 2020; version 1. 0, pism version available at https://doi. org/10. 5281/zenodo. 3903343), we perform an equilibrium simulation on a regular rectangular grid with 8 km horizontal resolution. The vertical resolution increases from 100 m at the top of the domain to 13 m at the (ice) base, with a domain height of 6000 m. PISM uses a hybrid of the Shallow-Ice Approximation (SIA) and the twodimensional Shelfy-Stream Approximation of the stress balance  (SSA, MacAyeal, 1989; Bueler and Brown, 2009)  over the entire Antarctic ice sheet. The grounding line position is determined using hydrostatic equilibrium, with sub-grid interpolation of the friction at the grounding line  (Feldmann et al.
C9 UCIJPL_ISSM We initialize the model by using data assimilation of present-day conditions, following the method presented in  Morlighem et al. (2013). The mesh horizontal resolution varies from 3 km near the margins to 30 km inland. The mesh is vertically extruded into 10 layers. We use a higherorder stress balance  (Pattyn, 2003)  and an enthalpy-based thermal model  (Aschwanden et al. , 2012; Seroussi et al. , 2013). The initialization is a two-step process: we first invert for ice shelf viscosity (B) and then invert for basal friction under grounded ice assuming thermo-mechanical steady state. Our geometry is based on BedMachine Antarctica  (Morlighem et al. , 2020). The thermal model is constrained by surface temperatures from  Comiso (2000)  and geothermal heat flux from  Shapiro and Ritzwoller (2004) , both of which are included in the SeaRISE dataset  (Shapiro and Ritzwoller, 2004; Nowicki et al.
C11 UTAS_ElmerIce The Elmer/Ice model domain covers the present-day Antarctic ice sheet, and its geometry is interpolated from the Bedmap2 dataset  (Fretwell et al. , 2013 ). An unstructured mesh in the horizontal is refined using the Hessian of the observed surface velocity, as in  Zhao et al. (2018). Mesh resolution in the horizontal varies from approximately 4 km near the grounding lines of fast-flowing ice streams to approximately 40 km in the interior. The mesh is extruded to 10 layers in the vertical. The forward simulations solve the Stokes equations directly  (Gagliardini et al. , 2013). Initialization was comprised of the following steps: 1. short surface relaxation (20 time steps of 0. 001 years); 2. inversion for sliding coefficient with constant temperature T = -20 • (Gillet-Chaulet et al.
Then, holding the ice geometry fixed, we run a 250 000 year, 32 km resolution, thermal evolution simulation in which temperatures are allowed to equilibrate. Refining the grid to 16 km and resetting bed elevations and ice thicknesses we run a further 1000 years using full model physics and a present-day climate, refine the grid to 10 km for a further 500 years and then refine the grid to 8 km for a GCM-forced historical run from 1950 to 2000. The resultant configuration is then used as the starting point for each of our forward experiments. Data availability. Model outputs from the simulations described in this paper will be made available in the CMIP6 archive through the Earth System Grid Federation (ESGF; https://esgf-node. llnl. gov/ search/cmip6/ (ESFG, 2020), last access: 11 July 2020) for twodimensional variables.
Scalars computed from two-dimensional fields for this study will be available from archive on Zenodo with the following doi: https://doi. org/10. 5281/zenodo. 3940766. In order to document CMIP6's scientific impact and enable ongoing support of CMIP, users are obligated to acknowledge CMIP6, participating modeling groups and the ESGF centers (see details on the CMIP Panel website: https://www. wcrp-climate. org/wgcm-cmip, last access: 11 July 2020). The forcing datasets are available through the ISMIP6 wiki (http://www. climate-cryosphere. org/wiki/index. php? title=ISMIP6_wiki_page, ISMIP6, 2020) and will also be archived in a publicly available repository (see assets tab).
Acknowledgements. We thank the  Climate and Cryosphere (CliC)  effort, which provided support for ISMIP6 through sponsoring of workshops, hosting the ISMIP6 website and wiki, and promoting ISMIP6. We acknowledge the  World Climate Research Programme , which, through its Working Group on Coupled Modelling, coordinated and promoted  CMIP5  and  CMIP6. We thank the climate modeling groups for producing their model output and making it available; the  Earth System Grid Federation (ESGF)  for archiving the CMIP data and providing access to it; the  University at Buffalo  for ISMIP6 data distribution and upload; and the multiple funding agencies who support  CMIP5 ,  CMIP6 , and  ESGF. We thank the IS-MIP6 steering committee, the ISMIP6 model selection group and ISMIP6 dataset preparation group for their continuous engagement in defining ISMIP6.
This is ISMIP6 contribution no. 11. Research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology. Helene Seroussi  and  Nicole Schlegel  are supported by grants from  NASA   Cryospheric Science and Modeling, Analysis , and Predictions Programs. AB was supported by the  U. S. Department of Energy (DOE) Office of Science Regional and Global Model Analysis (RGMA)  component of the Earth and  Environmental System Modeling (EESM) program  ( HiLAT-RASM  project), and the  DOE Office of Science (Biological and Environmental Research) ,  Early Career Research program. Heiko Goelzer  has received funding from the program of the  Netherlands Earth System Science Centre (NESSC) , financially supported by the  Dutch Ministry of Education, Culture and Science (OCW)  under grant no.
024. 002. 001. Rupert Gladstone  and  Thomas Zwinger  were supported by  Academy of Finland  grant nos. 286587  and  322430. Chen Zhao  and  Ben Galton-Fenzi  were supported under the  Australian Research Council's Special Research Initiative for Antarctic Gateway Partnership  (Project  ID   SR140300001 ) and received grant funding from the  Australian Government for the Australian Antarctic Program Partnership  (Project  ID   ASCI000002 ). Support for  Xylar Asay-Davis ,  Matthew Hoffman ,  Stephen Price  and  Tong Zhang  was provided through the  Scientific Discovery through Advanced Computing (SciDAC)  program funded by the  US Department of Energy (DOE), Office of Science, Advanced Scientific Computing Research, and Biological and Environmental Research Programs. MALI Earth System Grid Federation simulations used resources of the  National Energy Research Scientific Computing Center , a  DOE Office of Science user facility  supported by the  Office of Science of the U.
S. Department of Energy  under contract no. DE-AC02-05CH11231. Nicolas Jourdain  is funded by the  French National Research Agency (ANR)  through the  TROIS-AS  project ( ANR-15-CE01-0005-01 ) and the  European Commission  through the  TiPACCs  project (grant no. 820575 , call  H2020-LC-CLA-2018-2 ). Philippe Huybrechts and  Jonas Van Breedam  acknowledge support from the  iceMOD  project funded by the  Research Foundation -Flanders (FWO-Vlaanderen). Ralf Greve  was supported by the  Japan Society for the Promotion of Science (JSPS) KAKENHI  (grant nos. JP16H02224 ,  JP17H06104  and  JP17H06323 ). Support for  Nicholas Golledge  and  Daniel Lowry  was provided by the  New Zealand Ministry of Business Innovation and Employment  contract no. RTVU1705. The work of  Thomas Kleiner  has been conducted in the framework of the  PalMod  project (FKZ:  01LP1511B ), supported by the  German Federal Ministry of Education and Research (BMBF)  as part of the  Research for Sustainability initiative (FONA).
Leaf reflectance spectra have been increasingly used to assess plant diversity. However, we do not yet understand how spectra vary across the tree of life or how the evolution of leaf traits affects the differentiation of spectra among species and lineages. Here we describe a framework that integrates spectra with phylogenies and apply it to a global dataset of over 16 000 leaf-level spectra (400-2400 nm) for 544 seed plant species. We test for phylogenetic signal in spectra, evaluate their ability to classify lineages, and characterize their evolutionary dynamics. We show that phylogenetic signal is present in leaf spectra but that the spectral regions most strongly associated with the phylogeny vary among lineages. Despite among-lineage heterogeneity, broad plant groups, orders, and families can be identified from reflectance spectra.
To achieve these goals, we compiled an unparalleled dataset of over 16 000 leaf-level reflectance spectra (400-2400 nm) for 544 seed plant species spanning temperate and tropical latitudes in the Americas and Europe (Fig. 1b-d ). We used the PROSPECT5 leaf radiative transfer model  (Féret et al. , 2008)  to estimate five leaf attributes, number of leaf layers, chlorophyll and carotenoid content, equivalent water thickness, and leaf mass per area (LMA). To explore the evolutionary patterns in leaf spectra, we generated a time-calibrated molecular phylogeny for the species in our dataset using five chloroplast markers.
Spectral data acquisition and processing We compiled a dataset of over 16 000 leaf-level reflectance spectra (400-2400 nm) for 544 seed plant species spanning temperate and tropical latitudes in the Americas and Europe (Fig. 1d ). The dataset only includes spectra of mature, sun-exposed leaves measured in the spring or summer seasons. Leaf spectra were measured with two fullrange field spectroradiometersan ASD FieldSpec 3 (Analytical Spectral Devices, Boulder, CO, USA) and an SVC HR-1024i (Spectra Vista Corp. , Poughkeepsie, NY, USA)using leaf clips and artificial light sources. We processed the reflectance spectra using the R package SPECTROLAB  (Meireles et al. , 2017)  by trimming wavelengths shorter than 400 nm or longer than 2400 nm, splicing the sensor overlap regions of each spectrum, and subsampling the spectra to 10 nm resolution using spline interpolation.
Phylogenetic reconstruction The taxonomic name of every taxon in the spectral dataset was updated using a Taxonomic Name Resolution System (TNRS)  New Phytologist (2020) 228: 485-493  Ó 2020 The Authors New Phytologist Ó 2020 New Phytologist Trust www. newphytologist. com implemented in taxize  (Chamberlain & Szöcs, 2013). We obtained DNA sequences from GenBank (https://www. ncbi. nlm. nih. gov/genbank/) for each species for at least one of five chloroplast markers commonly used in plant phylogenetics (rbcL, matK, ndhF, atpB, and trnL) using PHYLOGENERATOR2  (Pearse & Purvis, 2013). Sequences were aligned with MAFFT  (Katoh & Standley, 2013)  using the FFT-NS-i iterative refinement algorithm. We reconstructed a maximum likelihood phylogeny with RAXML (Stamatakis, 2014) using a GTR + γ model of evolution and a backbone family-level-tree taken from the Open Tree of Life project  (Hinchliff et al.
, 2015). We timecalibrated the phylogeny with TREEPL (Smith & O'Meara, 2012) using 35 node age constraints derived from the TIMETREE database  (Hedges et al. , 2015)  (http://timetreebeta. igem. te mple. edu). We assessed clade support and age estimates using bootstrap replicates and by comparing our tree to a recent comprehensive time-tree  (Li et al. , 2019)   (Figs S2, S3 ; Methods S2).
Classifying major seed plant lineages using spectra We used a partial least squares discriminant analysis (PLS-DA) model  (Chevallier et al. , 2006)  implemented in CARET  (Kuhn, 2008)  to classify spectral samples at different phylogenetic levels, including major seed plant groups (gymnosperms, Magnoliidae, monocots, non-core eudicots, asterids, and rosids  (Chase et al. , 2016) ), orders, and families. For each level, we created 10 pseudoreplicated datasets by randomly resampling spectra (with replacement) to ensure that each category was represented by the same number of samples. We selected the optimal number of PLS components using a 10-fold cross-validation analysis using 80% of the data to train the model and 20% to test its predictive performance. We assessed classification accuracy by conducting independent PLS-DA analyses on 10 pseudo-replicated datasets and computing their classification matrices (or confusion matrices).
Discussion This large-scale analysis reveals that leaf spectra capture the phylogenetic history of seed plants. However, phylogenetic signal varies substantially across the spectrum and among lineages, suggesting that the leaf traits that underlie spectra are differentially conserved or labile across the tree of life. These results corroborate previous findings that phylogenetic signal varies across the spectra  (McManus et al. , 2016)  and are congruent with evidence that trait distinctiveness varies greatly across traits and across lineages  (Cornwell et al. , 2014; Flores et al. , 2014). The ongoing accumulation of spectral datasets and expansion of taxonomic sampling will enable us to better interpret why specific lineages display contrasting patterns of phylogenetic signal in spectra.
Overall, it is encouraging that we can identify broad phylogenetic groups using a global spectral dataset, especially given the impact that factors such as leaf age, soil type, and elevation can have on leaf chemistry and the resulting spectra  (Asner & Martin, 2016; Wu et al. , 2017). No single spectral region concentrates most of the information about evolutionary history across all seed plants and yet, phylogenetic groups can be identified based on their spectral profiles. These findings have two important consequences for planning biodiversity assessments based on remotely sensed spectral data  (Jetz et al. , 2016)  and for using plant spectra directly in studies of biodiversity  (Cavender-Bares et al. , 2017). First, efforts to assess and monitor plant diversity will require the acquisition of spectra across the entire solar reflected spectrum (400-2400 nm) instead of focusing on a few predefined spectral bands or on limited spectral regions.
Second, spectra can potentially be used to identify phylogenetic lineages when species-level detection is impractical, for example, in high diversity systems where a large proportion of species are understudied or unknown to science. Critically, the phylogenetic resolution at which diversity is detected can be decided based on the accuracy of the classification models rather than defined a priori. Our spectral evolution model can be fitted to data or be used in simulations to understand how different evolutionary processes affect the spectra. By applying this framework to our dataset, we found that spectral variation in seed plants has been constrained by evolution to varying degrees, especially in the visible region, which is associated with pigment content. This result indicates that only a narrow range of photosynthetic pigment content is evolutionarily viable, possibly reflecting functional constraints in the photosynthetic machinery  (Ollinger, 2011).
Acknowledgements The authors thank  Angela Moles  and two anonymous reviewers for their helpful comments on the manuscript. This study was funded by the  National Science Foundation and National Aeronautics and Space Administration  through the  Dimensions of Biodiversity program  ( DEB-1342872  grant to  JC-B ,  DEB-1342778  grant to PAT, and  DEB-1342823  grant to JAG) and by the  National Institute for Mathematical Biology and Synthesis  (' Remote Sensing of Biodiversity ' working group led by JC-B, JEM,  BCO  and PAT). The contribution of MES is supported by the  University of Zurich   Research Priority Program on  ' Global Change and Biodiversity '. This publication was supported by the  USDA National Institute of Food and Agriculture ,  Hatch  project  ME0-22022  through the  Maine Agricultural & Forest Experiment Station.