dataesr/scientific-paragraphs-categorization huggingface dataset:
Earlier applications of such models on non-molecular data include those of Collar et al. 23  and O'Meara et al. 24  for continuous traits, and Thomas et al. 25  for discrete traits. Beaulieu et al. 6  proposed a model to take hidden transition rate changes into account and applied it to plant habit in Campanulidae. As part of exploring the evolution of traits that enable plants to survive freezing temperatures, Zanne et al. 26  compared a set of transition rate estimates at the level of the angiosperms to those estimated for Magnoliidae, Monocotyledoneae, Superrosidae and Superasteridae. These four clades had noticeably different transition rates from each other and from angiosperms as a whole, as well as different frequencies of the occurrence of character state combinations. Using parsimony on a 761-species dataset, we previously reconstructed ancestral states for perianth symmetry across angiosperms  27.
In the present study, we expand our dataset to include five floral (perianth) traits recorded in 1230 species, representing 414 of the 416 families (all except Macarthuriaceae and Kewaceae) and all 64 orders recognized by APG IV  28. We use this new dataset to test the impact of various assumptions of models on estimated transition rates and ancestral states. First, we test the impact of symmetric (Mk1) vs. asymmetric (Mk2) rate models on discrete binary character evolution  5, [29] [30] [31]. Further, we compare both best-fit models and estimated transition rates among the five distinct characters in order to measure among-character rate heterogeneity  26, 30, [32] [33] [34] [35]. Last, we explore the impact of whole-tree rate constancy by relaxing this assumption and allowing five partitions (ANA + Magnoliidae, Monocotyledoneae, basal eudicots, Superrosidae and Superasteridae, see definition in Methods) of the angiosperm tree to evolve at their own rates  26.
1. Two concern fusion, once in [ANA + Magnoliidae] and once in Superrosidae, and two concern symmetry, once in Monocotyledoneae and once in basal eudicots. In all of these cases, transition rates are so high that the ancestral states of all nodes examined are equivocal; their estimated transition rates range from 0. 064 to 7. 174; for comparison, the highest transition rate in our data not causing all nodes to be equivocal is 0. 015 (fusion in Monocotyledoneae). In addition, there is no change of perianth phyllotaxis in Monocotyledoneae in our dataset, making the inclusion of their estimated Mk1 rate (q = 1. 03 × 10 -10 ) in the histogram irrelevant to the test as well. Best-fit tree models. The evolution of four out of five characters is better fitted by Mk2 than by Mk1 when mapped on the full tree (Fig.
The number of transitions in angiosperms as a whole, along with the number range for origins and reversals, is shown in Table  5. A NEXUS file containing the tree and both the rescored and original data matrices is provided as Supplementary Data S1. A per-partition breakdown of the origins and reversals is shown in Supplementary Table  S6. There are at least 148 origins of bilateral symmetry, 71 of perianth fusion, 23 of spiral phyllotaxis, 25 of a pentamerous perianth, and 44 of a differentiated perianth. Sensitivity of results to the dataset and tree used. In the main results presented here, polymorphic data were rescored according to the presumed derived state (see Methods). We explored the sensitivity of our results to this assumption by conducting all analyses again on the original dataset and found very few differences (see Supplementary Note 1 and Tables  1 2 3 4 5 6 ).
In addition, we conducted all analyses again using a different ultrametric tree of 1215 taxa extracted from a recently published mega-tree of 353,185 taxa  36. Likewise, we found that nearly all of our results are robust to the method used to build an ultrametric tree matching our trait dataset (see Supplementary Note 2 and Tables S1-S6). Caryophyllales Parsimony q = 0. 0001 q = 0. 0010 q = 0. 0100 q = 0. 1000  q = imposed Mk1 transition rate. The indicated state is the most-likely, followed by its relative probability. Symmetry (hr) = high rate equilibrium.
Conclusion and perspectives. We found that most ancestral states for deep as well as shallow nodes of the angiosperm tree were largely unaffected by changes in models (number of transition rates in model, value of transition rates, tree partitioning). However, we found a strong signal for among-lineage rate heterogeneity in perianth evolution, and we observed that accounting for this heterogeneity may influence some reconstructed ancestral states. On the one hand, this may mean that most ancestral states reconstructed in this and other model-based studies  14, 45  are similar irrespective of whether rate heterogeneity is taken into account or not. On the other hand, we do not know whether the number of model-sensitive nodes in our dataset is normal, unusually low, or even unusually high, which makes predictions for other data than our own uncertain.
Methods We recorded five perianth characters (see below) for a large sample of angiosperm species using an exemplar approach. We included at least one species from each currently recognized family (except Kewaceae and Macarthuriaceae; see details below). Families lacking a perianth, such as Piperaceae and Ceratophyllaceae, were treated as missing data, but maintained in our sample in order to present a complete overview of floral trait evolution mapped on phylogenetic trees  27. All data were scored in the PROTEUS database  46  (the complete list of 5924 explicit records and linked references is provided as Supplementary Data S2; for a general explanation of the database and scoring philosophy, see Supplementary Information of Sauquet et al. 14  ). APG IV  28  was followed for order and family delimitations.
Sauquet et al. 14  inferred the ancestral perianth to be trimerous and clearly demonstrated that pentamery is derived in angiosperms. Differentiation. A perianth made entirely of parts identical to each other, i. e. , undifferentiated, is the most common type in basal angiosperms. By contrast, most flowers of angiosperms are characterized by a differentiated perianth, comprising at least two whorls of parts different from each other in colour, size, shape, and anatomy. According to several studies, the ancestral state in angiosperms has been considered as undifferentiated  7, 8, 14, 19, 20, 55  , or equivocal  18, 56. Following initial recording in PROTEUS, the matrix of five characters and 1230 species for this study was exported as a NEXUS file from the database and transformed in Mesquite 3.
Phylogenetic tree. The backbone tree used here is based on the consensus tree from the Angiosperm Phylogeny Website  13  and the one from Soltis et al. 48 , with the internal phylogenetic structure of families resolved according to the same studies as in Reyes et al. 27  (see Appendix 1 of that article). We used an alternative method to approximate branching times for this study, since many of the species sampled do not have sufficient data in public DNA sequence databases (e. g. , GenBank) and it was beyond the scope of this study to generate new sequence data for these species. We employed the bladj function of phylocom  63  to transform the consensus tree topology into an ultrametric tree, via constraining 399 nodes defining families and deeper clades (Supplementary Fig. S2 ) to fit the ages of Magallón et al.
Acknowledgements We thank the following PROTEUS users for contributing to the data used in this paper:  Charlotte Prieu ,  Julien Massoni ,  Laetitia Carrive ,  Stefan Löfstrand ,  Mario Coiro ,  Yannick Städler ,  Florian Jabbour ,  Marion Chartier ,  Boris Domenech ,  Kristel Schoonderwoerd ,  Guillaume Chomicki  and  Patrícia dos Santos. We thank  Louis Ronse de Craene ,  Felix Forest ,  Pete Lowry ,  Catherine Damerval  and  Thomas Haevermans  for comments on an earlier draft of this paper. This project was supported by  Agence Nationale de la Recherche  grant  ANR-12-JVS7-0015-01  (MAGNIPHY) to H. S.
Since Ryugu has never been visited before, the limited information before Hayabusa2's arrival should be reviewed and appropriately set out in order to guide the Hayabusa2 mission toward success. For this purpose, the International Regolith Science Group (IRSG) was organized as a branch of the Interdisciplinary Science Team in the Hayabusa2 project. In this paper, a product of an IRSG activity, we review the information about Ryugu before the encounter of Hayabusa2. We focus especially on the properties of the surface regolith layer, i. e. its global properties including surface geological features (Section 2), its thermophysical properties (Section 3), and its mechanical properties (Section 4). The information described in each section falls into three categories in terms of our present knowledge: the measured data obtained so far, the properties directly derived/deduced from the present data with basic physics, and the predicted properties.
As a whole, this paper provides a reference model of Ryugu's regolith with the categorized data/properties. This model will be useful for Hayabusa2 operations such as landing site selection and touch-down operations as well as for interpreting and understanding data that will be obtained by the remote sensing observations of Hayabusa2 and by the analysis of samples returned from the asteroid. Hayabusa2 carries a suite of instruments for scientific observation. These include three optical navigation cameras (ONC), consisting of a telescopic camera with seven band filters (ONC-T) and two wide field of view cameras (ONC-W1 and -W2), a near-infrared spectrometer (NIRS3), a thermal infrared imager (TIR), and a light detection and ranging (LIDAR) instrument. The surface of Ryugu will be investigated with these instruments.
At a larger scale, images of Ida and Gaspra look broadly similar to those of Eros, perhaps indicating that Eros is representative of an S-type asteroid of its size. Aside from Itokawa, Steins (5. 3-km mean diameter) and Toutatis (3-km mean diameter) are the asteroids that have been observed by spacecraft that are closest in size (both are larger) to Ryugu but have a different taxonomic type; the best global Steins images have a ~160-m pixel scale, again insufficient to give much insight into Ryugu. The Toutatis data are limited, but have a pixel scale of ~3 m. There is limited, very low resolution data available for Annefrank and Braille. (4) Vesta and Ceres are so much larger than Ryugu that they do not provide very good points of comparison for what to expect.
The objective of this paper is to present a new dataset of bias-corrected CMIP5 global climate model (GCM) daily data over Africa. This dataset was obtained using the cumulative distribution function transform (CDF-t) method, a method that has been applied to several regions and contexts but never to Africa. Here CDF-t has been applied over the period 1950-2099 combining Historical runs and climate change scenarios for six variables: precipitation, mean near-surface air temperature, near-surface maximum air temperature, nearsurface minimum air temperature, surface downwelling shortwave radiation, and wind speed, which are critical variables for agricultural purposes. WFDEI has been used as the reference dataset to correct the GCMs. Evaluation of the results over West Africa has been carried out on a list of priority user-based metrics that were discussed and selected with stakeholders.
It includes simulated yield using a crop model simulating maize growth. These bias-corrected GCM data have been compared with another available dataset of bias-corrected GCMs using WATCH Forcing Data as the reference dataset. The impact of WFD, WFDEI, and also EWEMBI reference datasets has been also examined in detail. It is shown that CDF-t is very effective at removing the biases and reducing the high inter-GCM scattering. Differences with other bias-corrected GCM data are mainly due to the differences among the reference datasets. This is particularly true for surface downwelling shortwave radiation, which has a significant impact in terms of simulated maize yields. Projections of future yields over West Africa are quite different, depending on the bias-correction method used. However all these projections show a similar relative decreasing trend over the 21st century.
This bias has not been reduced between CMIP3 and CMIP5 GCM simulations (see  Roehrig et al. , 2013). This too southern ITCZ location over West Africa leads to too weak precipitation over the Sahel and too weak crop yields whose values cannot be used as relevant information for stakeholders and farmers. GCM and RCM output data have to be adjusted to statistical distributions of observation-based reference data. However, the use of different bias-correction methods in combination with different reference datasets contributes to the total uncertainty in climate projections and can contribute in some contexts more than the use of different GCMs or RCMs  (Iizumi et al. , 2017). Thus using multiple bias-correction techniques and reference datasets can be recommended. For instance, a bias correction of a subset of five GCMs of the CMIP5 database was realized at a global scale through the ISIMIP project  (Hempel et al.
, 2013a) , the first Inter-Sectorial Impact Model Intercomparison Project 1. These corrections were applied at a daily scale from 1 January 1950 to 31 December 2099 to historical and all RCP scenarios for five GCMs at a 0. 5 • × 0. 5 • grid using WATCH Forcing Data (WFD) data as observation-based reference. More recently a ISIMIP2b bias correction using an improved reference dataset, EWEMBI, has been realized for three out of the five CMIP5 GCMs' data, and the results have been compared to the bias-corrected ISIMIP/WFD data  (Lange, 2017a). Significant differences have been highlighted that are closely related to differences between WDF and EWEMBI data. The objectives of this paper are to present and evaluate bias-corrected GCM data obtained by performing the cumulative distribution function transform (CDF-t) method over Africa to quantify the sensitivity of the bias-corrected data to different reference datasets and to illustrate this in terms of simulated crop yields.
The bias correction has been performed using the CDF-t method  (Michelangeli et al. , 2009) , a method that has been widely used and validated for various variables and in various contexts (e. g. Kallache et al. , 2011; Vrac et al. , 2012; Lavaysse et al. , 2012; Vautard et al. , 2013; Vrac and Friederichs, 2015; Vrac et al. , 2016) , including tropical areas  (Oettli et al. , 2011; Vigaud et al. , 2013) , but not Africa. These corrections have been applied to 29 GCMs over the  1950 -2005 period and RCP2. 6, RCP4. 5, and RCP8. 5 2006 -2099  projections. The observation-based reference dataset used for bias corrections is WFDEI, the WATCH Forcing Data (WFD;  Weedon et al. , 2011)  methodology applied to ERA-Interim data, for the period from 1 January 1979 to 31 December 2013 on a 0. 5 • × 0.
Climate input data The AMMA-2050 dataset comprises bias-corrected daily data for the variables pr, tas, maximum air temperature and minimum air temperature, rsds, and wind speed. It covers the domain 20 • W-55 • E/40 • S-40 • N, including all of Africa. In this paper, results are presented for West Africa (20 • W-20 • E/0-25 • N) in boreal summer as it is the focus of AMMA-2050.
Reference observation datasets The observation-based reference dataset is critical for the correction of GCM biases, especially when corrections are applied to daily data. The reference dataset must also have a global coverage on a regular grid, which may induce large uncertainties in void in situ data areas as in Africa. So we used the available WFD, WFDEI, and EWEMBI reference datasets to compare to each other and to compare biascorrected (with WFD) ISIMIP data with bias-corrected (with WFDEI) AMMA-2050 data. The WFD dataset  (Weedon et al. , 2011 ) is a combination of ERA-40 daily reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) at a grid resolution of 2. 5 • and the Climate Research Unit (CRU) TS2. 1 dataset that provides observed time series of monthly variations in the climate on a resolution grid of 0.
5 •. A correction for monthly mean rainfall is included using the Global Precipitation Climatology Centre (GPCC) version 4 dataset  (Hagemann et al. , 2011). The WFD data are available over the period 1958-2001 on a 0. 5 • grid over land area points. The WFD dataset has been used over  1979-2001. WFDEI, an improved version of WFD, has been produced based on ERA-Interim reanalysis, over the period from 1 January 1979 to 31 December 2013 on a 0. 5 • × 0. 5 • grid  (Weedon et al. , 2014). Improvements come from the 4D-var data assimilation system with 6 h windows in ERA-Interim instead of 3D-var in ERA-40. Compared to ERA-40, ERA-Interim uses a more extensive suite of satellites, atmospheric soundings, and surface observations and provides substantial improvement in surface meteorological variables  (Dee et al.
, 2011) , in particular with a new aerosol loading distributions and corrections for downward shortwave fluxes (leading in particular to larger average WFDEI values over the Sahara and northern Africa), leading to less bias compared to globally distributed observations. ERA-Interim also has a reduced Gaussian grid spectral model resolution of T255 instead of T159 for ERA-40, leading to data much closer to the regular 0. 5 • × 0. 5 • spatial resolution and to the elevation distribution used for WFDEI. A correction for monthly mean rainfall is included using the GPCCv5/v6 dataset. The WFDEI dataset has been used over  1979-2013. More recently, the EWEMBI dataset has been produced within ISIMIP  (Lange, 2016 (Lange, , 2017b)). Over land, EWEMBI is identical to the WFDEI dataset for pr, daily mean, minimum, and maximum near-surface air temperature, and 10 m wind speed but different for surface downwelling shortwave radiation.
Data sources of EWEMBI are ERA-Interim data, WFDEI, eartH2Observe forcing data (E2OBS;  Dutra, 2015) , and NASA/GEWEX Surface Radiation Budget data (SRB  ; Stackhouse Jr. et al. , 2011)  primary-algorithm estimates of daily mean rsds from SRB release 3. 0  (Frieler et al. , 2017; Lange, 2017b ). Significant differences have been highlighted between WFD-based and EWEMBI-based biascorrected data that are closely related to similar improvements from WDF to EWEMBI data. The EWEMBI dataset has been used over 1979-2013.
Intercomparison of WFD, WFDEI, and EWEMBI on mean seasonal fields over West Africa In the following, to reduce the number of figures, the results are presented only for the summer season, July-September (JAS), which is the main rainy season over the Sahel. Similar computations have been performed over the other seasons, especially over spring, which is the main rainy season over the Guinean coast, and some of the results will be commented on. Figure  1  presents the July-September mean seasonal fields of WFD, WFDEI, and EWEMBI for tas, pr, and rsds. Regarding tas, the mean fields of the three reference datasets are very close, showing the set-up in northern spring and summer of the high-temperature area associated with the Saharan and Saudi Arabia heat lows. Regarding pr, the seasonal fields are also very close, showing the seasonal migration of the ITCZ between spring and summer.
Local maxima associated with highlands like the Fouta Djalloon or Cameroon mountains    are also clearly highlighted. Regarding rsds, more differences are evident between the three reference datasets. The mean seasonal fields show similar patterns with low values within the ITCZ area due to the high cloud coverage and high values over the Sahara due to low moisture and cloud coverage, but the range of values are quite different. Over the ITCZ, WFD rsds values are the weakest and EWEMBI values the highest. Over the Sahara WFD values are also the weakest but WFDEI values are a bit higher than for EWEMBI. In the remaining panels, differences are produced in respect to SRB data. Compared to SRB, EWEMBI data are very similar, which is logical since SRB data were used to correct ERA-Interim. WFDEI has moderate negative biases in the ITCZ area and weak positive biases over the Sahara, while WFD has high negative biases over the whole area.
The CDF-t method In this work, we use the CDF-t method developed by  Michelangeli et al. (2009)  to adjust climate models. It consists in matching the CDF of a climate variable simulated by a model (here the GCM) to the CDF of this variable in observations (here WFDEI) through a mathematical function. CDF-t is a variant of the non-parametric quantilequantile (QQ) method  (Déqué, 2007). But contrary to the QQ method that projects the GCM CDF of simulated future data onto the CDF of historical data, CDF-t considers the CDF change between historical and future GCM simulations. Let F Gh and F Sh define the CDFs of a variable from the GCM (subscript G) and from a given reference location A. M. Famien et al. : A bias-corrected CMIP5 dataset for Africa using the CDF-t method (subscript S) over a historical calibration period (subscript h).
Sensitivity of the correction to the calibration period over West Africa Before applying the CDF-t correction through the moving window process over 1950-2099, the bias-correction method has to be calibrated individually for every GCM over a reference period. In order to have a calibration dataset as representative as possible of the variability in the various variables, especially pr, the time period 1979-2013 has finally been used for calibration of the bias-correction method. However the sensitivity to the calibration period has been explored over West Africa by testing it on two  sub-periods, 1979-1996 and 1996-2013 , to prevent any overestimation of the biascorrection performance. This has been performed on the five GCMs used in ISIMIP, and it is more specifically shown in the IPSL-CM5A-LR model in summer for tas, pr, and rsds (Supplement).
Mean seasonal fields over West Africa In the following, the Taylor diagram  (Taylor, 2001)  will be used to quantify the distance between the raw, biascorrected GCM data and WFDEI data. This diagram provides three statistics, the spatial correlation coefficient between the tested field and the reference field, the normalized standard deviation of the tested field in respect to the standard deviation of the reference field, and the centred rootmean-square error (RMSE) between the tested field and the reference field. The Taylor diagram has also been used to evaluate the distance between the reference datasets WFD and EWEMBI relative to WFDEI. Table  2  sums up the three Taylor statistics of these reference datasets for all the metrics. Regarding the seasonal mean metrics, WFDEI and EWEMBI statistics are similar except for rsds, for which they are quite different over the Guinean coast.
25 to more than 2. Spatial correlations are higher in the Sahel (from +0. 7 to +0. 95) than in Guinea area (from +0. 2 to +0. 8). The CDF-t bias correction is quite effective in removing these biases and bringing the raw data closer to WFDEI, with some small remaining discrepancies, higher than for tas. The ISIMIP bias correc-  A. M. Famien et al. : A bias-corrected CMIP5 dataset for Africa using the CDF-t method tion is also effective due to the proximity between WFD and WFDEI (see also Table  2 ). Figures  6, 7 , and S5 show similar results, but for rsds. The mean seasonal field of WFDEI is a pattern with low values within the ITCZ area due to the high cloud coverage and high values for the Sahara due to low moisture and cloud coverage. We have noticed previously (Fig. 1 ) that high differences exist among WFDEI, WFD, and EWEMBI.