dataesr/scientific-paragraphs-categorization huggingface dataset:
Even though two supplementary classes "ammonium" and "nitrate" are now included in the Copernicus Atmospheric Monitoring Service (CAMS) reanalysis, these do not impact the method proposed here and were, thus, not considered. An algorithm developed by  Lefèvre et al. (2013)  translates MACC partial aerosol optical depth information into aerosol mixtures designed for the Optical Properties of Aerosols and Clouds (OPAC) software package  (Hess et al. , 1998). These mixtures are associated with aerosol properties: scattering and absorbing coefficients, single scattering albedo, asymmetry parameter, and the Angström coefficient. The total AOD at 550 nm is then calculated as the sum of partial AOD at 550 nm provided by CAMS. As libRadtran needs a total AOD input for the simulated wavelength, the OPAC Angström coefficient of the given mixture is used to estimate the AOD at the required wavelength.
Here we construct a liquid cloud LUT of ρ ovc , setting different cloud and atmosphere properties, geometries, and spectral grids, as described in Table  1. The optical properties of the clouds come from the precalculated Mie tables provided by the libRadtran software package. As no information is provided on the actual cloud vertical structure, ρ ovc are calculated as ρ ovc = 1 2 ρ ovc,high + ρ ovc,low , (7) where ρ ovc,high and ρ ovc,low are reflectances, respectively, derived from the high and low liquid cloud LUTs, interpolated on the viewing and solar geometries of the satellite time series and adapted to the spectral response function of radiometric channels. An ice cloud LUT is also produced to study the sensitivity of surface irradiance estimates to the assumed cloud phase. The ice cloud characteristics follow the parameterization by  Yang et al.
Setup and datasets for validation The method has been tested on images from the Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) aboard the Meteosat-9 meteorological geostationary satellite belonging to the family of Meteosat Second Generation (MSG). We consider measurements in the solar channels 0. 6 and 0. 8 µm for the year 2011 and for 11 locations in the field of view of the satellite, corresponding to locations of pyranometric in situ sensors from the BSRN. We use the calibration gains provided by EUMETSAT that operates MSG. For sensors with a linear count response like MSG/SEVIRI  (Doelling et al. , 2018) , the radiance L sat is related to digital count C via L sat = g(C -C 0 ), where C 0 is the so-called space count. To study the validity of the method, we compare DSSI estimates from MSG satellite measurements with pyranometric DSSI data retrieved from BSRN measurement stations.
Considered stations are listed in Table  3  and displayed in the MSG field of view in Fig. 4. Only the highest-quality BSRN measurements of surface irradiance are used, having passed a quality check  (Lefèvre et al. , 2013). Figure  5  shows the time series when data are considered valid, for each station. We also compare the results of our method to operational satellite-based products of surface irradiance. For this, we use data from the HelioClim3 version 5 (HC3v5) and CAMS Radiation (CAMS-RAD) DSSI databases. Both are  derived from the imagery of the SEVIRI sensor and are produced by a Heliosat method: a modified version of Heliosat-2 for HC3v5  (Qu et al. , 2014)  and Heliosat-4 for CAMS-RAD. Both products and their descriptions are provided by the SoDa service (http://www. soda-pro. com/, last access: 9 September 2019).
Lorente, François-Marie Bréon, Jérome Vidot, Nicolas Ferlay, Philippe Dubuisson, Maximilien Patou, Alexander Marshak, Alexei Lyapustin, Jay Herman, Yuekui Yang, Tamas Varnai, Richard Perez, Paul Stackhouse, Seiji Kato, David Doelling, and Bastiaan Van Diedenhoven for useful discussions. Financial support. Benoît Tournadre was funded for his PhD work 50 % by Region Sud (grant no. 2017_05720) and 50 % by Transvalor SA (950 Avenue Roumanille, CS 40237 Biot, 06904 Sophia Antipolis Cedex France). The postdoctoral research fellowships of Benoît Tournadre and Xuemei Chen are funded by Transvalor SA. Review statement. This paper was edited by Piet Stammes and reviewed by three anonymous referees.
Acknowledgements. We would like to thank the operators of the BSRN stations for producing and providing valuable validation measurements. We are grateful to the libRadtran team for their open radiative transfer model and interactions. The MODIS MCD43C1 version 6 data product was retrieved from the online  NASA Earthdata Search , courtesy of the  NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC)  and  USGS/Earth Resources Observation and Science (EROS) Center ,  Sioux Falls ,  South Dakota , https://search. earthdata. nasa. gov/(last access: 8 July 2019). We also thank  EUMETSAT  and the  ECMWF  for providing, respectively, Meteosat-9 and MACC reanalysis aerosol, water vapor, and ozone data. We used imagery from the  NASA  Worldview application (https://worldview. earthdata.
Study areas We use radar data near two deep ice core drill sites in East Antarctica. One is located at Dronning Maud Land (DML), near the German summer station (Kohnen at -75. 00 • S, 0. 00 • ). The other site is located at Dome C, close to Concordia station (-75. 10 • S, 123. 35 • E). We use the measured ice fabric data from both ice cores published by  Weikusat et al. (2017)  and  Durand et al. (2009) , respectively, to validate our polarimetric-radar data inferences. At Dome C, radar data were additionally collected at 20 stations along with a 36 km long profile across the dome, enabling us to track ice fabric variability in the dome-flank transition zone. Surface topography at Dome C  (Helm et al. , 2014; Howat et al. , 2019)  exhibits an ice dome elongated in the SW-NE direction (Fig.
Radar data at Dome C were collected at 20 sites in January 2014. One of the sites is located within walking distance of the ice core site EDC. The remaining 19 sites (termed E(ast)0-E18, and W(est)0. 5-W18, with the numbers relating to the distance in kilometers away from the dome) are aligned in a profile which is approximately perpendicular to the long axis of the dome and parallel to the flowline (Fig. 1a ). At EDML, data were collected in January 2017, approximately 2. 7 km northeast of the ice core site EDML (Fig. 1b ). More information related to the individual ApRES sites is shown in Appendix A.
Site θ r Dome C 1, 0, 0 0, 1, 0 EDML 0, 1, 0 0, 1, 0 where l 1 , l 2 , and l 3 are constants (0 or 1). Note that φ and δP in Eqs. (  15 ) to (  17 ) are standardized. In Table  3 , we show the values of l 1 , l 2 , and l 3 that we used for Dome C and EDML sites. The coherence phase misfit was not applicable in EDML due to strong ice fabric anisotropy. To further constrain the inversion, we set bounds on the model parameters so that 0 < λ i < 0. 5, 0 • < θ i < 180 • , and -30 dB < r i < 30 dB. This is implemented in the cost function in the form of log-barrier functions using MATLAB's fmincon algorithm.
Ice fabric parameters from polarimetric ApRES at EDML Next, we apply the inverse approach to ApRES data collected at the EDML drill site. Contrary to what has been observed at EDC, co-polarization nodes can barely be localized in δP HH as no 90 • symmetry is apparent (Fig. 6a ). This indicates that anisotropic scattering is relevant (r = 0 dB), as already noticed earlier  (Drews et al. , 2012). Moreover, the coherence phase shows many nodes (Fig. 6b ), indicating a much stronger horizontal anisotropy (i. e. , large λ). This is comparable to the ice core at Mizuho, equally located in a flank flow regime  (Fujita et al. , 2006). Although δP HV shows almost no depth variability in ice fabric orientation (Fig. 6c ), it is not straightforward to identify the direction of v1 and v2 using the polarity of because of the strong ice anisotropy (Fig.
(C4) The complex s HH , its amplitude, and its phase are then s HH = 1 (4π z) 2 x cos 2 (θ) exp(j 2zk x ) + y sin 2 (θ ) exp(j 2zk y ) , (C5) |s HH | = x ( 4π z) 2 cos 4 (θ ) + r 2 sin 4 (θ ) +2rsin 2 (θ )cos 2 (θ ) cos(2z(k x -k y )) 0. 5 , (C6) arg(s HH ) = tan -1 sin(2zk x ) + rtan 2 (θ) sin(2zk y ) cos(2zk x ) + rtan 2 (θ) cos(2zk y ) , (C7) respectively. Also, the complex s HV , its amplitude, and its phase, respectively, are s HV = sin(θ) cos(θ) (4πz) 2 x exp(j 2zk x )y exp(j 2zk y ) , (C8) |s HV | = x sin(θ) cos(θ ) (4πz) 2 1 + r 2 -2r cos(2z(k x -ky)) This section details the relationship between the polarity of the phase gradient and the corresponding directions of the eigenvectors. Care has to be taken here as the deramping during ApRES data acquisition is equivalent to a complex conjugation of the received signal.
If this is not accounted for, the inferred eigenvector v1 and v2 will be swapped. More specifically, for a received signal at θ = 0 • , s HH = A ( x cos(2zk x ) + j x sin(2zk x )) , (D1) s VV = A y cos(2zk y ) + j y sin(2zk y ) (D2) so that the coherence phase results in + sin(θ ± γ ) cos(θ ± γ )(s HH (θ ) -s VV (θ )), (E4) s VV (θ ± γ ) = cos 2 (θ ± γ )s VV (θ ) + sin 2 (θ ± γ )s HH (θ ) + sin(θ ± γ ) cos(θ ± γ )(s HV (θ ) + s VH (θ )). (E5) Figure  E1  demonstrates this approach for the EDML site, where quad-polarimetric measurements were additionally complemented by a dataset collected with rotating antennas. There are no structural differences between both datasets. Appendix F: Correlation between HH power anomaly (δP HH ) nodes and anisotropic reflection ratio (r) Here, we quantify the angular distance of co-polarization nodes (AD) as a function of the anisotropic reflection ratio (r).
Code and data availability. The entire code was written by the author and is publicly available on GitHub (https://github. com/ RezaErshadi/ApRES_InverseApproach, last access: 19 April 2022; DOI: https://doi. org/10. 5281/zenodo. 4447487; Ershadi, 2021). EDML radar data are cited as Christmann et al. (2020); DOI: https://doi. org/10. 1594/PANGAEA. 913719. Dome C radar data are cited as Corr et al. (2021); DOI: https://doi. org/10. 5285/634EE206-258F-4B47-9237-EFFF4EF9EEDD.
Methods. We propose a method that overcomes these two obstacles by producing and comparing, at each pixel of the analyzed RoI, spatially integrated count spectra for data and model. The comparison follows a log-likelihood approach that extends the χ 2 test to histograms with low statistics. This method can take into account likelihood weights that are used to account for systematic uncertainties. Results. We optimize the new method so that it provides a fast and reliable tool to assess the goodness-of-fit of Fermi-LAT data and we use it to check the latest gamma-ray source catalog on 10 years of data.
2. All these results have been obtained with a TS computed as normally done in the computation of TS maps, that is to say assuming a power-law spectrum. This choice, along with the fact that the PS and TS are computed using the same data and model 3D maps, ensures a fair PS/TS comparison. However ∼30% of the sources in 4FGL-DR2 are better modeled with a curved spectrum so the power-law assumption may bias the optimization of the spatial selection parameters. In order to check this possibility, we performed the same comparison but using the TS reported in the 4FGL-DR2 catalog, which is computed with the curved spectral shape (either a log normal or a subexponentially cutoff power law) when the source spectrum is found to be significantly curved. We find that, compared to the results presented in Fig.
A more detailed analysis of these imperfections will be included in the forthcoming 4FGL-DR3 catalog publication. The PS map of the RoI centered on (l = 160 • , b = -20 • ) containing the negative spot at (l = 157. 6 • , b = -21. 2 • ) is shown in Fig. 11. The data and model integrated count spectra corresponding to the pixel with the lowest PS  (-19. 5 ) are shown in Fig. 12. One can see that the model overpredicts the data in the energy range between 0. 3 and 10 GeV. Since the PS is relatively flat around the minimum, we performed a PS scan over the spatial selection parameter p 2. A minimum of -47. 4, much lower than 7 https://fermi. gsfc. nasa. gov/ssc/data/analysis/ software/aux/4fgl/Galactic_Diffuse_Emission_Model_ for_the_4FGL_Catalog_Analysis. pdf -6. 24 6. 24 -19. 5, is obtained around p 2 = 1.
Conclusion Fermi-LAT analyses are generally based on a binned loglikelihood fit of a 3D count map but there is no fast, reliable and sensitive tool available to check the goodness-of-fit (including  the case of negative residuals). In order to overcome the lack of such a tool, we have developed a new method that allows Fermi-LAT data users to quantify efficiently the data-model agreement after performing a fit of an RoI. The method is based on integrating the observed and predicted counts over an energydependent region around each pixel of the map and on computing a deviation estimator, named PS, between the integrated data and model count spectra. This method can incorporate the likelihood weights that are used in Fermi-LAT analyses to take into account some systematic uncertainty. In order to minimize the computation time, the PS algorithm has been optimized while ensuring a PS precision of 3%.
Acknowledgements. We thank our  Fermi-LAT Collaborators Jean Ballet  and  Matthew Kerr  for fruitful discussions. The  Fermi-LAT Collaboration  acknowledges generous ongoing support from a number of agencies and institutes that have supported both the development and the operation of the LAT as well as scientific data analysis. These include the  National Aeronautics and Space Administration  and the  Department of Energy in the United States , the  Commissariat à l'Energie Atomique  and the  Centre National de la Recherche Scientifique/Institut National de Physique Nucléaire et de Physique des Particules in France , the  Agenzia Spaziale Italiana  and the  Istituto Nazionale di Fisica Nucleare in Italy , the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) ,  High Energy Accelerator Research Organization (KEK)  and  Japan Aerospace Exploration Agency (JAXA) in Japan , and the  K.
A python script computing PS maps is available at the User contribution page of the Fermi Science Support Center website: https: //fermi. gsfc. nasa. gov/ssc/data/analysis/user/. The links to the script and the documentation are https://fermi. gsfc. nasa. gov/ssc/data/analysis/user/gtpsmap/gtpsmap. py and https://fermi. gsfc. nasa. gov/ssc/data/analysis/user/ gtpsmap/README.
Morphometric Software We used two packages, the CLIC package version 97  [25] , available at (https:// xyom-clic. eu, accessed on 16 August 2020), and the recent online morphometric package, XYOM (https://xyom. io, accessed on 16 August 2020)  [39]. Computer-assisted manual digitization was performed using XYOM software, which allowed an increase in the number of pseudo-landmarks by automatically adding points between those digitized by the user, provided they fall exactly on the contour. This process was under visual control, and permitted an increase in shape capture.
Acknowledgments: We would like to thank  Sylvia Meek scholarships  ( Malaria Consortium ) for supporting the student in this study and we would like to thank the staff of the  Department of Medical Entomology, Faculty of Tropical Medicine, Mahidol University, Thailand  for their kind support. This study was supported by  Research Grant  from the  Faculty of Tropical Medicine, Mahidol University , Fiscal Year 2018.
We provide an implementation of relative age constraints in RevBayes  (Höhna et al. 2016 ) that can be combined in a modular manner with the wide range of molecular dating methods available in the software. We use both realistic simulations and empirical datasets of 40 Cyanobacteria and 62 Archaea to evaluate our method. We show that the combination of relative age constraints with fossil calibrations significantly improves the estimation of node ages.
Introduction Dated species trees (chronograms or timetrees, in which branch lengths are measured in units of geological time) are used in all areas of evolutionary biology. Their construction typically involves collecting molecular sequence data, which are then analyzed using probabilistic models. Commonly, a relaxed molecular clock approach is adopted. Such methods combine three components: a model of sequence evolution, a model of clock rate variation across the phylogeny, and calibrations of node ages based on the geological record. Inference is typically performed using Bayesian Markov chain Monte Carlo (MCMC) algorithms. Inferring the age of speciations based on molecular data is challenging because it amounts to factoring divergence between sequences, estimated in units of substitutions per site, as a product of time (ages of splits) and rates of evolution.
Implementation We implemented this model and the two-step approach in RevBayes so that it can be combined with other available relaxed molecular clock models and models of sequence evolution and species diversification. Using the model in a RevScript implies calling two additional functions: one to read the constraints from a file, and another one to specify the timetree prior accounting for the constraints. Evaluation of the accuracy of the two-step approach We compared our two-step, composite-likelihood approach to the one-step, full Bayesian MCMC approach in combination with two different models of rate evolution, White Noise (WN), and Uncorrelated Gamma (UGAM) (see  Lepage et al. 2007  for a presentation of both). Analyses were performed in RevBayes  (Höhna et al. 2016 ). We used an empirical sequence alignment and phylogeny of 36 mammalian Species from Reis et al.
Simulations of deviations from the clock The shuffled tree was rescaled to yield branch lengths that can be interpreted as numbers of expected substitutions (its length from root to tip was 0. 451). Then it was traversed from root to tips, and rate changes were randomly applied to the branches according to two Poisson processes, one for small and frequent rate changes, and one for big and rare rate changes. The magnitudes of small and large rate changes were drawn from lognormal distributions with parameters (mean=0. 0, variance=0. 1) and (mean=0. 0, variance=0. 2), respectively, and their rates of occurrence were 33 and 1, respectively. After this process, branches smaller than 0. 01 were set to 0. 01. A Python code using the ete3 library (Huerta-Cepas, Serra, and Bork 2016) is available at https://github.
com/Boussau/DatingWithConsAndCal/blob/master/Scripts/alterBranchLengths. py , along with the command lines used and a plot of the resulting tree with altered branch lengths (https://github. com/Boussau/DatingWithConsAndCal/blob/master/SimulatedTrees/proposedTree _rescaled_altered. dnd. pdf). The trees at the various steps of this simulation pipeline are also represented Fig. S1. We compared the extent of the deviations from ultrametricity we had introduced in our simulated tree to empirical trees from the Hogenom database  (Penel et al. 2009). Fig. S2  shows that our simulated tree harbours a realistic amount of non-ultrametricity.
Alignment simulation The tree rescaled with deviations from the clock was used to simulate one alignment 1000 bases long according to a HKY model  (Hasegawa, Kishino, and Yano 1985) , with ACGT frequencies {0. 18, 0. 27, 0. 33, 0. 22} and with a transition/transversion ratio of 3. The Rev script is available at https://github. com/Boussau/DatingWithConsAndCal/Scripts/simu_HKY_No_Gamma. Rev.
PEER COMMUNITY IN EVOLUTIONARY BIOLOGY 11 We inferred branch length distributions under a Jukes-Cantor model  (Jukes and Cantor 1969)  to make our test more realistic in that the reconstruction model is simpler than the process generating the data. The tree topology was fixed to the true unrooted topology. The script is available at https://github. com/Boussau/DatingWithConsAndCal/blob/master/Scripts/mcmc_JC. Rev. The obtained posterior distributions of branch lengths were then summarized by their mean and variance per branch (script available at https://github. com/Boussau/DatingWithConsAndCal/blob/master/Scripts/DatingRevScripts/comp uteMeanAndVarBl. Rev). These means and variances were given as input to a script that computes a posterior distribution of timetrees according to a birth-death prior on the tree topology and node ages, an uncorrelated Gamma prior on the rate of sequence evolution through time  (Drummond et al.
Empirical data analyses We used alignments, tree topologies and sets of constraints from Archaea and Cyanobacteria that had been previously analyzed in  Davín et al. (2018). In both cases, the constraints had been derived from transfers identified in the reconciliations of thousands of gene families with the species tree, and filtered to keep the largest consistent set of supported constraints. We used 431 constraints for Archaea, and 144 for Cyanobacteria. Alignments, trees and sets of constraints are available at https://doi. org/10. 5061/dryad. s4mw6m958. We used the CAT-GTR model in Phylobayes  (Lartillot et al. 2013)  to generate branch length tree distributions with a fixed topology, and our two-step approach in RevBayes  (Höhna et al. 2016)  to compute posterior distributions of timetrees, under the UGAM model of rate evolution  (Drummond et al.
Two-step inference provides an efficient and flexible method to estimate time trees We compared posterior distributions of node ages obtained using the classical full Bayesian MCMC approach to those obtained using our two-step approximation on a dataset of 36 mammalian species (dos  Reis et al. 2012). As shown in Supplementary Figs. S3-6 , the two posterior distributions of node ages are practically indistinguishable. Further, the impact of the PEER COMMUNITY IN EVOLUTIONARY BIOLOGY 12 approximation is negligible in comparison to the choice of the model of rate evolution. We used the uncorrelated Gamma (UGAM) or the White Noise (WN) models, both uncorrelated, and found that using one or the other results in more differences in the estimated node ages than using our two-step inference compared to the full Bayesian MCMC.
Acknowledgements Version 8 of this preprint has been peer-reviewed and recommended by  Peer Community In Evolutionary Biology  (https://doi. org/10. 24072/pci. evolbiol. 100127). We thank the reviewers and the editor for their comments on earlier versions of the manuscript. DS and GJSz received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  under Grant Agreement 714774, GJS received funding Grant  GINOP-2. 3. 2. -15-2016-00057. TAW is supported by a  Royal Society University   Research Fellowship  and  NERC grant   NE/P00251X/1. BB and TAW acknowledge support from a " Projet de Recherche Collaborative " co-funded by the  CNRS  and the  Royal Society. We thank  Eric Tannier  for fruitful discussions.
Data availability Scripts and data used to run the simulation analyses are available at https://github. com/Boussau/DatingWithConsAndCal Data for the empirical data analysis has been deposited at: https://doi. org/10. 5061/dryad. s4mw6m958 A tutorial is available at: https://revbayes. github. io/tutorials/relative_time_constraints/ to use both our two-step approach and for dating with relative node age constraints.
Data Analysis Raw data from Amazon's Mechanical Turk were exported to Matlab for analysis. The dependent variable was accuracy (d-prime), and independent variables were music condition and time (first half versus second half of each block). To test the effects of individual differences in attention difficulties, as quantified by the ASRS  69  , we did a median split on all subjects' ASRS scores, yielding a high-ASRS group (i. e. those with more ADHD-like symptoms) and a low-ASRS group (those with less ADHD-like symptoms).
Stimuli and Procedure Participants completed 680 trials of SART, a visual GO/NOGO task with inter-trial interval of 1150 ms, same as Experiment 1. This was done under four auditory conditions, presented in counterbalanced order: AM+, Silence (within-subjects), Control-Music, and Pink noise (betweensubjects). EEG was recorded with a 64-channel BrainVision actiCHamp system with PyCorder software in a sound attenuated and electrically shielded chamber.