dataesr/scientific-paragraphs-categorization huggingface dataset:
Eumedclim Database: Climate Variables We computed the same bioclimatic variables available from  Worldclim (bio1, 2, 5, 6, 12, 13, 14). In addition, monthly   temperature and precipitation data from WorldClim were used to compute seasonal temperature and precipitation variables, potential evapotranspiration (PET) and water balance (precipitation minus PET) (Table  1 ). PET was calculated using Hargreaves' equations (1994) modified by  Droogers and Allen (2002)  in the R package SPEI  (Beguería and Vicente-Serrano, 2013) , from monthly precipitation, monthly temperature (minimum and maximum) and latitude coordinates. Results show good agreement between EuMedClim and station data for both the spatial and temporal components (Figure  1 , Supplementary Table  1 , Supplementary Figure  1 ).
DATA VALIDATION For the spatial component, the explained variance of weather observations  (1960-1990 means)  ranges from 91. 4 to 99. 4% and Mean Bias Error (MBE) ranges from -4. 5 mm to <0. 1 • C for annual water balance and annual mean temperature respectively, with strong agreement for temperature and slight underestimation of precipitation (Figure  1 , Supplementary Table  1 ). Obviously, similar errors are reported between WorldClim and station data (i. e. , WorldClim was used to infer spatial information of EuMedClim) whereas CRU data shows higher errors that are inherent to its lower spatial resolution. For the temporal component comparison, Kendall's coefficients indicate very high correlations between EuMedClim and station data for yearly mean values, ranging from 0. 87 to 0. 99 for annual PET and mean temperature respectively (Figure  1 , Supplementary Table  1 ).
89 for 90% of individual stations (vs. 0. 89 for CRU) for annual temperature (bio1), higher than 0. 69 (vs. 0. 65) for annual precipitation, higher than 0. 71 (vs. 0. 68) for annual PET and higher than 0. 73 (vs. 0. 70) for annual water balance. RMSE between EuMedClim and individual station time series are lower than 0. 8 • C for 90% of individual stations (vs. 2. 23 • C for CRU) for annual temperature, lower than 131 mm (vs. 252 mm) for annual precipitation, lower than 95 mm (vs. 171 mm) for annual PET and lower than 172 mm (vs. 385 mm) for annual water balance. The evaluation at individual stations shows that EuMedClim quantifies the temporal climatic variation over Europe and the Mediterranean Basin similarly to the CRU dataset, but with higher accuracy. Our evaluation indicates that CRU and EuMedClim have higher errors in arid areas of North Africa (particularly for precipitation), where there are the lowest density of stations.
DATA FILES AND R SCRIPTS The entire climate dataset and corresponding R scripts to manipulate it are available at http://gentree. data. inra. fr/climate (Fréjaville, 2017). For each climatic variable, gridded yearly data from 1901 to 2014 are provided by eight GeoTiff integer data files. Each file corresponds to one geographical tile, with longitude ( • E) and latitude ( • N) boundaries given in the file name. The format of each GeoTiff file is a multi-layer raster (raster brick) containing 114 layers, with the first and the last layer corresponding to the years 1901 and 2014, respectively. Data must be divided by 10 to get correct values (in • C or in mm). Three R scripts are available along with the climate data. Extract_climate_time_series. R downloads yearly data of one or several climatic variables at the geographical location (WGS 84) specified by the user.
Transposable and repeat element annotation Although a repeat analysis for masking purposes is part of the NCBI gene annotation steps, we undertook a dedicated analysis using the REPET package  (Quesneville et al. 2005; Flutre et al. 2011). The computational pipeline to predict and classify transposable element (TE) integrations used both de novo-(TEdenovo) and homology-based methods (TEannot). A summary of the REPET pipeline can be found at https://urgi. versailles. inra. fr/Tools/REPET and detailed description of the applications and methods are in the Supplementary Online Material.
Expression Analysis Levels of gene expression were determined by aligning the trimmed RNAseq reads from each library against the chemoreceptor and OBP transcripts from the i5k Workspace@NAL, occasionally truncated to avoid overlap with the UTRs of well-expressed neighboring genes, using the Burrows-Wheeler Aligner (BWA)  (Li and Durbin 2009)  and Read1 of each read pair. Samtools  (Li et al. 2009 ) was used to sort, index, and summarize the BWA. Read counts were standardized as Read counts Per Kilobase transcript per Million mapped reads in each library (RPKM) to facilitate comparisons across the 17 samples (Table  S1 ). CC-BY 4. 0 International license a certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under The copyright holder for this preprint (which was not this version posted July 30, 2018.
A high quality genome assembly and annotation The draft genome assembly consists of 10,707 contigs with a N50 of 45 kbp that were connected into 1,976 scaffolds with a N50 scaffold size of 622 kbp and a total of 162 Mbp including 3 Mbp of gaps between contigs within scaffolds. This assembly (v1) is available from the National Center for Biotechnology Information (NCBI) as BioProjects PRJNA297591 and PRJNA168335 (NCBI GCF_000341935. 1). It is of a size and quality comparable to those of other Hymenoptera in GenBank (Table  S2 ). We assessed the completeness of this assembly using BUSCO (Benchmarking Universal Single-Copy Orthologs)  (Waterhouse et al. 2018) , which revealed high completeness scores of 97. 6-99. 5%, with few duplicated (0. 3-0. 7%), fragmented (0. 3-1. 7%), or missing (0. 2-0.
These libraries ranged in size from 3. 5-12 Gbp. This was additionally supplemented by an available RNAseq dataset of 717,345 454-pyrosequencing reads from mixed-sex antennae  (Gress et al. 2013). Gene modeling was performed by NCBI using their GNOMON pipeline, and yielded 11,210 protein-coding genes and 41 pseudogenes, as well as 1,307 non-coding RNA genes. A total of 25,937 transcripts were modeled, with 25,189 of those having support from the RNAseq and/or homology with other arthropod proteins. The mean number of transcripts per gene is 2. 3 (range 1-50) and the mean number of exons per gene is 8. 9 (range 1-108). The protein-coding gene set is comparable in completeness (as estimated by alignment to the REFSEQ proteins from D. melanogaster) with that of the eusymphytan sawfly Athalia rosae and the woodwasp Orussus abietinus (NCBI C.
Expression levels of chemoreceptors and OBPs We examined the levels of expression of the chemoreceptors and OBPs in our 17 diverse RNAseq datasets from larvae, pupae, adults, and adult body parts (TableS1 -Auxiliary file 1), using the manually annotated transcripts, truncated if necessary to avoid conflation with overlapping untranslated regions (UTRs) of highly-expressed neighboring genes. The complete results are presented in Tables S10-13 and Figures  S5-8  (Auxiliary file 2), with selected genes shown in Figures  4 5 6 7. For the ORs, Orco has the expected high levels of expression in male and female antennae, roughly twice the level seen in entire adult bodies or pupae, where chemoreceptor expression is initiated. As expected, heads without antennae, abdomen tips, and abdomens without tips had low levels of expression of Orco.
Acknowledgements We thank  Alvaro Hernandez  and the  W. M. Keck Center for Comparative and Functional Genomics  at the  University of Illinois at Urbana-Champaign  for genomic and RNA library construction and sequencing;  Daniel Ence  and  Mark Yandell  ( University of Utah ) for annotation of an earlier version of the genome assembly;  Terence Murphy  for assistance with the NCBI annotation;  Monica Poelchau  and  Chris Childers  for assistance with the i5k Workspace@NAL browser and official gene set v1. 1;  Chris Elsik  for an Apollo browser at NasoniaBase;  Masatsugu Hatakeyama ,  Bernhard Misof ,  Oliver Niehuis , and  Jan Philip Oeyen  for granting pre-publication access to gene annotations of Athalia rosae and Orussus abietinus; and  Megan Hofland  and  Norma Irish  for preparing Figure 1.
Review of the brute force optimization A second procedure sometimes used is the so-called brute force optimization. In this procedure, each parameter varies in a given range. The procedure iterates, on each value of a given parameter, in the whole n-dimension space À n being the parameter number À so that all possible combinations are tested. The total number of characterizations or simulations needed is thus equal to m n , where m is the number of points taken within each parameter range. For instance, if one deals with 5 parameters and 10 samples per parameter, the total number of characterizations or simulations will be equal to 10 5. Even for this small number of samples, this method is absolutely not usable for solar cells, since the individual simulation time 1 SLALOM source code is available for download on GitHub (https://github.
Once f is carefully defined, one can use it inside a mathematical algorithm designed to find the parameter vector that maximizes the efficiency f using an initial guess and a numerical iterative procedure. Such algorithms are numerous in the literature and are available in freely usable software libraries such as SciPy  [13]. We mainly used two of these algorithms that we demonstrated, after a comprehensive study involving 10 of them, to be suitable for the optimization of solar cells. The first one is the Sequential Least Squares Programming (SLSQP) method  [14]. The iterative algorithm used in it starts from an initial guess, a given vector of parameters P ðkÞ , and constructs the next vector P ðkþ1Þ by using Newton's method to find the best direction, where the gradient function vanishes, in the n-dimensional parameters space.
5  [6]  illumination in a large voltage range. The corresponding current is calculated for each voltage value. The photovoltaic efficiency is then extracted from this simulated current-voltage characteristic. The mathematical algorithm takes this function f and gives, after a number of iterations, the parameter vector maximizing the efficiency. The two suitable optimization algorithms presented in the present section, SLSQP and BFGS, were used and throughly tested with respect to two major criteria: the number of function evaluations (i. e. , the overall optimization speed) and its robustness (i. e. , convergence in a wide range of initial parameter values). All this was implemented in the SLALOM open-source piece of software, using the open-source SciPy library  [13]  for optimization and opening for various semiconductor simulator engines.
The next section describes in detail this implementation in order to provide the reader with the ability to use the SLALOM software for her or his own purpose. 2 The multivariate optimizer implementation SLALOM is written in vanilla Python  [16]  but it could be re-implemented in other interpreted languages (such as Lua) or compiled ones (such as C++). It uses the SciPy  [13]  and NumPy  [17]  numerical packages, which are Python interfaces to a collection of state-of-the-art numerical and scientific routines written in C and Fortran. These routines are freely available from http://netlib. org and could be accessed by any language that can interface C routines. The monitoring and visualization part of SLALOM was written using Tkinter  [18]  for the user interface and Matplotlib  [19]  for 2D curve plotting.
It is composed of seven main modules: slalom. py, the SLALOM startup module, sets the parameters, the optimization method and controls the whole process. -slalomCore. py, the SLALOM core class, implements the optimization using one of the mathematical algorithms provided by the SciPy package, and controls the device simulation engine. -slalomSimulator. py is the class interfacing the solar cell simulator engine. This class encapsulates to a common python interface the functionalities that are specific to the simulator, either commercial simulators such as Silvaco ® and tiberCAD ® or free simulators such as Afors-HET  [20] , SCAPS  [21] , PC1D  [22] , and AMPS-1D  [23]. It can furthermore be extended to include any simulator that can be launched from the command line and that outputs its results in text files (i.
This architecture offers the ability to control in detail the physical models and to change the parameters in a set of small and consistent files. All these files share one C header file including the main material parameters. These files are used by the optimizer to create the simulation input set that it uploads to the calculation server if needed. The optimizer then controls Deckbuild and monitors its output. For the tiberCAD ® tool, the same methodology as for Silvaco ® is used, and could have been used for any other command line scriptable simulator. The software has only been adapted to the tiberCAD ® syntax constraints: each optimization parameter is recognized with a specific comment line before the corresponding statement.
4 ) and near it (second case, Fig. 5 ). Each index corresponds to a set of parameters choosen by the optimizer in the variation domain defined in slalomDevice. py. Both methods converge to the same set of optimal parameters Table  1. Bandgap E g , affinity X, permittivity e, and densities of states in the conduction band N C and in the valence band N V of GaN and InN  [29] [30] [31]. The absorption is given by equation (  2 ), the C and D values being fitting from experimental data  [32] , with E ph the incident photons energy and E g the InGaN bandgap for a given indium composition x. E g (eV)  in about 2 h for L-BFGS-B and 1 h for SLSQP on a Red Hat Linux server with two 8-core Xeon processors and 32 GB of RAM. When the initial point is choosen in the close vicinity of the global optimum (second case, Fig.
Conclusion A new solar cell optimization method is presented in this paper. This method is implemented in SLALOM, an easy-touse, portable, and open-source software. SLALOM implements mathematical multivariate methods for the rigorous optimization of solar cell parameters taking into account the parameters' interdependencies and speeding up the optimization procedure by two orders of magnitude. SLALOM is freely distributed with a complete set of cases implementing physical models and data extracted from experimental measurements for realistic and precise solar cell simulation. Its use was presented in this paper for the optimization of an InGaN PN solar cell. The absolute optimal efficiency was obtained for a set of five parameters crucial to the solar cell design and elaboration: the indium composition, thicknesses, and doping concentrations for the InGaN structure.
g. ,  Rignot et al. , 2011; Shepherd et al. , 2012; Gardner et al. , 2018)  :::::::::::::::::::::::::::::::::::::::::::: (e. g. ,  Rignot et al. , 2011; Gardner et al. , 2018; Shephe. In order to obtain a good agreement with observations, atmospheric models require accurate large-scale circulation patterns together with a proper representation of snow surface processes, clouds, turbulent fluxes, and a relatively high horizontal resolution to properly resolve the complex ice sheet topography at the margins. compare ::::::::::::: MAR-simulated ::::: SMB :::: with ::: the ::::::::::::: state-of-the-art ::::::: regional :::::: climate :::::: model ::::::::: RACMO2 :::::::::::::::::::::  (van Wessem et al. , 2018). :::: We use available SMB observational datasets to show that both MAR and RACMO2 perform similarly well in simulating the SMB field ::::: spatial :::::::: gradients.
In addition, we identify significant processes that still need to be included and/or improved in both RCMs. In Section 2, we describe MAR and its specific set-up for Antarctica, together with RACMO2, the forcing fields, observational datasets and methods designed for model evaluation. In Section 3, we show that both RCMs share common biases against observed SMB, resulting from erosion-deposition processes :::::: drifting ::::: snow ::::::: transport :::::: fluxes. Secondly, we analyse SMB differences between models and show that many of the discrepancies can be attributed to low-level sublimation of precipitation in katabatic channels ::: and :: to ::: the ::::::::: difference :: in ::::::::::: precipitation :::::::: advection :::::: inland. Finally, in Section 4, we summarise our main findings and discuss further efforts to be achieved for a better assessment of the AIS surface mass balance.
2 Data and methods 2. 1 Regional modelling 2. 1. 1 Regional atmospheric models For the first time, the polar-oriented regional atmospheric model MAR is applied for decades-long simulations over the whole Antarctic ice sheet. MAR atmospheric dynamics are based on the hydrostatic approximation of the primitive equations, fully described in  Gallée and Schayes (1994). Prognostic equations are used to depict five water species: specific humidity, cloud droplets and ice crystals, raindrops and snow particles  (Gallée, 1995). Sublimation of airborne snow particles is a direct contribution to the heat and moisture budget of the atmospheric layer in which these particles are simulated. The radiative transfer through the atmosphere is parametrised as in  Morcrette (2002) , with snow particles affecting the atmospheric optical depth  (Gallée and Gorodetskaya, 2010).
Boundaries: The topography is derived from the Bedmap2 surface elevation dataset  (Fretwell et al. , 2013). Because the Antarctic domain is about 4 times larger than the Greenland domain, the circulation has to be more strongly constrained. This is why we use a boundary relaxation of temperature and wind in the upper atmosphere starting from 400 hPa (∼6000 m above the ground) to 50 hPa (upper level), as in van de Berg and Medley (2016), whereas relaxation starts from 200 hPa in  Fettweis et al. (2017).
Parameterisations: a) The fresh :::::: surface snow density ρ s is computed as a function of 10 m wind speed ws 10 (m s -1 ) and surface temperature T s (K): ρ s = 149. 2 + 6. 84 ws 10 + 0. 48 T s , (1) with minimum-maximum values of 200-400 kg m -3. This parameterisation was defined so that the simulated density of the first 50 cm of snow fits observations collected over the Antarctic ice sheet (see Fig. S1 , with snow density database detailed in Table  S1 ). b) The aerodynamic roughness length z 0 is computed as a function of the air temperature, as proposed in  Amory et al. (2017). The parameterisation was tuned so that z 0 fit the observed seasonal variation between high (> 1 mm) summer and lower (0. 1 mm) winter values in coastal Adélie Land, for air temperatures above -20 °C. For lower temperatures, z 0 is kept constant and set to 0.
SMB observations and sectors of strong SMB gradients We use surface mass balance observations of the GLACIOCLIM-SAMBA dataset detailed in  Favier et al. (2013)  and updated by  Wang et al. (2016). This dataset is an update of the one assembled by  Vaughan et al. (1999)  following the quality-control methodology defined by  Magand et al. (2007). It includes 3043 reliable SMB values averaged over more than 3 years. ::: We airborne-radar ::::::: method :::::::: combined :::: with ::::::: ice-core ::::::::::::: glaciochemical ::::::: analysis. : The first order feature of the Antarctic SMB is a strong coastal-inland gradient, with mean values ranging from typically greater than 500 kg m -2 yr -1 at the ice sheet margins to about 30 kg m -2 yr -1 in the dry interior plateau (Fig. 1 , see also, e.
g. ,  Wang et al. , 2016). We divide the sparse observation dataset (Fig. 1  -5% of MAR grid cells coverage of the ice sheet) into 10 sectors detailed in Table  1  and shown in Fig. 2. Six of them are stake transects with a stake every ∼1. 5 km, which have been proven very valuable for evaluating modelled SMB  (Agosta et al. , 2012; Favier et al. , 2013; Wang et al. , 2016). The four other sectors are composed of more scattered observations covering large elevation ranges (Victoria Land, Dronning Maud Land, and Ross Ice Shelf-Mary Byrd Land).
1d ) , which tends to confirm a SMB overestimation in MAR even if those data are composed of point observations possibly not capturing the spatial heterogeneity over 35 grid cell areas. Interestingly, this unobserved area where MAR simulates larger snowfall amounts than RACMO2 is classified as a large wind glaze area in Scambos et al. (2012, see their Fig. 9 ). In their study,  Scambos et al. (2012)  outlined that those wind glaze areas could arise from eroded snow sublimating in the undersaturated katabatic layer. This would result in a net mass loss for the ice sheet, as surface snow is moved to suspended snow by the erosion process, then to water vapour by the low-level sublimation process, and subsequently exported outside the ice sheet by the katabatic flow :::::::: resolution :: in ::::: MAR).
, 2017)  :::::::::::::::::::::  (van Wessem et al. , 2018). We find no significant differences between MAR and RACMO2 SMB when integrated on the AIS and its major basins (Table  2 ). As the dominant feature of the Antarctic SMB is its strong coast to plateau gradient, we extract stake transects and sectors with large elevation ranges from the GLACIOCLIM-SAMBA SMB observational dataset. We show that both RCMs show similar performances when compared to observations, with a good representation of the SMB gradient (Fig. 2 ). But more importantly, we outline and quantify missing :: or ::::::::::::: underestimated processes in both RCMs. Along stake transects, we relate 100 km-scale oscillations ::::::::: fluctuations : of observations around the smooth modelled SMB pattern to the shape of the ice sheet captured on the 35×35 km MAR grid.
Drifting ::::: snow :::::::::: sublimation ::::: might :: be ::: the :::::: largest :::: mass :::: sink :: in ::::::::: Antarctica, ::::: much ::::: larger :::: than ::: the ::::::: drifting :::: snow :::::::: transport ::::: fluxes :: at ::the ::::: scale :: of ::: the :::::::: ice-sheet ::::::::::::::::::::::::::::::::: (Palm et al. , 2017;  Lenaerts et al. , 2012a)  :. :::::::: However, ::: we :::::: cannot :::::::: constrain ::: this :::: flux :::: with ::: the :::::::: available :::: SMB :::::::::: observation ::::::: dataset, :: as :: it :::::: occurs :::::: bellow :::: 2000 :: m ::: asl ::::: (Fig. :::::: S11d), ::::: where ::::::::::: observations ::: are :::::::: extremely :::::: scarce. :::: Our ::::::: drifting :::: snow :::::::: transport ::::::: estimate ::: can :: be ::::: used :: to ::::::: constrain ::: the ::::::: drifting :::: snow ::::: fluxes :: in ::::::: models ::::: above :::: 2000 :: m ::: asl, :::::: which ::::: might :::: have :: in :::: turn ::::::::: implication ::: for ::: the drifting snow sublimation in MAR is generally compensated by stronger ::::::: amounts :::::::: simulated :: at ::: the :::::::: ice-sheet :::::::.
Code and data availability. Python scripts developed for this study as well as all required data are available at https://gitlab. com/cecileagosta/antarcticasmb-20c. git. The last version of MAR is freely distributed at http://mar. cnrs. fr/. Monthly MARv3. 6. 4 outputs from this study are freely available at ftp://ftp. climato. be/fettweis/MARv3. 6/Antarctic/, together with the associated MAR source code. The ECMWF reanalyse ERA-Interim 6-hourly outputs were downloaded from http://apps. ecmwf. int/datasets/. The MERRA2 reanalyse 6-hourly outputs were downloaded from https://disc. sci. gsfc. nasa. gov/. The JRA-55 reanalyse 6-hourly outputs were downloaded from https://rda. ucar. edu/datasets/ds628. 0/.