dataesr/scientific-paragraphs-categorization huggingface dataset:
), (  9 ) waste treatment and (10) agriculture. Natural emissions (11) were calculated by the models as set out in Sect. 3. 2. The gridded distribution of anthropogenic emissions was provided by INERIS and it was based on a merging of different databases from -TNO 0. 125 • × 0. 0625 • emissions for 2007 from MACC  (Kuenen et al. , 2011) ;   1996)  concerning integrated pollution prevention and control. EPER is a web-based register which enables the public to view data on emissions of 50 key pollutants to water and air from large and medium-sized industrial point sources in the European Union. -EMEP 0. 5 • × 0. The TNO-MACC dataset provides two distinct datasets: (i) large point sources (LPSs) with the coordinates of stacks and (ii) surface emissions on a fine grid (0. 125 • × 0. 0625 • ). In the gridding process, the first step consisted in summing up LPS emissions from the TNO-MACC emissions inventory for 2007 with surface emissions to obtain total emissions as in the EMEP inventory.
LPSs were aggregated with surface emissions because no data were available to calculate plume rise heights for point sources emissions. For the various SNAP sectors, the processing steps were the following: -SNAP 2: The country emissions were regridded with coefficients based on population density and French bottom-up data; the methodology  (Terrenoire et al. , 2015)  was extrapolated to all of Europe. For PM 2. 5 emissions, the annual EMEP national totals were kept except in the following countries: Czech Republic, Bosnia and Herzegovina, Belgium, Belarus, Spain, France, Croatia, Ireland, Lithuania, Luxembourg, Moldova, Republic of Macedonia, the Netherlands, Turkey. For these countries, PM 2. 5 emissions from GAINS were used as this database provides higher numbers and certainly more realistic since wood burning is known to be underestimated in the EMEP database (Denier van der  Gon et al.
, 2013)  was used to estimate the fraction of non-carbonaceous species, elemental carbon and organic matter per activity sectors and country. Models used their own split for NO x , SO x and NMVOC emissions. These emission inventories did not account for recent changes in the way to measure semi-volatile organic compounds from wood-burning emissions as discussed in Denier van der  Gon et al. (2015). The full emission dataset is available upon request to INERIS.
Soil nitrogen monoxide (NO) emissions CHIMERE and MINNI used version 2. 04 and CAMx used version 2. 1 of the MEGAN model to calculate the NO emissions. RCG used a parameterization of NO emissions described in  Simpson et al. (1999). LOTOS-EUROS did not include NO emissions in this simulation. CMAQ used the BEIS module. The soil NO emission parameterization for EMEP is described in  Simpson et al. (2012).
Wildfire emissions Fire emissions were provided by the GFASv1. 0 database  (Kaiser et al. , 2012)  only for the 2006 campaign. The Global Fire Assimilation System (GFASv1. 0) calculates biomass burning emissions by assimilating fire radiative power (FRP) observations from the MODIS instruments onboard the Terra and Aqua satellites. It corrects for gaps in the observations, which are mostly due to cloud cover, and filters spurious FRP observations of volcanoes, gas flares and other industrial activities. For all models, the wildfire emissions were assigned in the whole PBL layer. Only the following species were taken into account: CO, CH 4 , NO x , SO 2 , PM 2. 5 , TPM (total primary matter), OC (organic carbon) and EC (elemental carbon).
Meteorology All models except CMAQ and RCG share the same meteorological dataset at 0. 2 • resolution based on ECMWF IFS (Integrated Forecast System) calculations. Because of its importance for applications (e. g. in air pollution modelling), the boundary layer height, as diagnosed in the ECMWF IFS model, was made available. The parameterization of the mixed layer (and entrainment) uses a boundary layer height from an entraining parcel model. But, in order to get a continuous field, in neutral and stable situations the bulk Richardson method proposed by  Troen and Mahrt (1986)  is used as a diagnostic, independently of the turbulence parameterization. Boundary layer height is defined as the level where the bulk Richardson number, based on the difference between quantities of energy at that level and the lowest model level, reaches the critical value Ri cr = 0.
25. For RCG, a different meteorological dataset was used. The 3-D data for wind, temperature, humidity and density www. atmos-chem-phys. net/16/12667/2016/ Atmos. Chem. Phys. , 16, 12667-12701, 2016 were produced employing a diagnostic meteorological analysis system developed at Freie Universität (Berlin, Germany) and based on an optimum interpolation procedure on isentropic surfaces. The system takes into account all available observed synoptic surface and upper air data as well as topographical and land use information  (Reimer and Scherer, 1992). Rain data, cloud data and boundary layer heights were retrieved from the IFS dataset. Boundary layer parameters as friction velocity and Monin-Obukhov length were calculated on-the-fly by applying standard boundary layer theory. The CMAQ model used meteorological variables calculated with the COSMO model in CLimate Mode (COSMO-CLM) version 4.
125 • × 1. 125 •. For aerosols only elemental carbon, organic carbon, dust and sulfate were used. Stratospheric ozone fields from the MACC reanalysis agree with ozone sondes and ACE-FTS (atmospheric chemistry experiment Fourier transform spectrometer) data within ±10 % in most seasons and regions. In the troposphere, the reanalysis shows biases of -5 to +10 % with respect to ozone sondes and aircraft data in the extratropics, while larger negative biases are shown in the tropics. Areaaveraged total column ozone agrees with ozone fields from a multi-sensor reanalysis dataset within a few percent. For aerosols, the observed Aerosol Optical Depth (AOD) is assimilated in the model with a feedback on individual PM species (sea salt, dust, elemental carbon, organic carbon and sulfate). When available, the MACC reanalysis is compared with observations, the model acronym in the supporting material is MACCA.
Air pollutant concentrations The evaluation was carried out with the available EMEP standard monitoring  (Tørseth et al. , 2012)  and intensive period observations for  2006 , 2007 , 2008 and 2009 (Aas et al. , 2012) )  on hourly and daily bases (see Supplement S8 for the description of background sites). Elevated sites above 1500 m in altitude have been excluded from the analysis. The measurements were downloaded from the EBAS database (http://ebas. nilu. no/). Additional AirBase data  (Mol and de Leeuw, 2005)  were used to evaluate the impact of meteorology on air pollutant concentrations in Sect. 7. 2. It is important to note that daily measurement for a day, N , is the averaged value between day N HH:00 and day N +1 HH:00, with HH usually varying in the range [00, 09] in GMT. For most of the species, measurements on a daily and hourly basis are not necessarily performed for the same set of stations.
PBL height The sounding data were extracted from the University of Wyoming database (http://weather. uwyo. edu/). For each site and each day, two soundings are available at 00:00 and 12:00 GMT. The provided meteorological parameters are pressure (hPa), the corresponding height above ground level (m), dew point temperature ( • C), relative humidity (%), mixing ratio (g kg -1 ), wind direction (degrees) and wind speed (expressed in knots and converted to m s -1 by applying the conversion factor 0. 514), potential and virtual potential temperature (K). For the present study, data were extracted over 77 stations in Europe. The boundary layer height is estimated using the calculation of the bulk Richardson number profile and searching for the altitude where the critical value of Ri cr = 0. 25 is reached.
Impact of the PBL parameterization with MINNI results for the 2009 campaign As shown in the previous section, MINNI underestimates the PBL heights calculated at 12:00 GMT from measurements but it is in better agreement with hourly data available at SIRTA (Fig. 2 ). In order to test the effect of PBL heights on air quality predictions, the MINNI model has been run using the PBL from IFS instead of its own parameterization for PBL heights. As shown by  Curci et al. (2015) , processes in the PBL can greatly affect the PM 2. 5 ground concentrations; for instance, temperature and relative humidity can favour the production of ammonium nitrate in the upper PBL. Figure  17  shows the average PBL heights and the average concentrations of O 3 , NO 2 and PM 10 using MINNI's parameterizations (left graphs) and the percentage difference between the average concentrations calculated with PBL heights given by IFS (PBL IFS ) and by MINNI's parameterizations (PBL MINNI ) (right graphs) using the following formula: (PBL IFS -PBL MINNI )/ PBL MINNI.
Influence of meteorology on NO 2 concentrations with CAMx results Pollutant concentrations are strongly influenced by the reconstruction of meteorological fields. In this section, a comparison of model performances in reproducing wind speed and NO 2 concentrations is presented and discussed. Further- The NO 2 observed dataset has been set up from AirBase database  (Mol and de Leeuw, 2005) , selecting just background stations having more than 75 % valid data over the whole year 2009. Modelled concentrations have been derived from the CAMx simulation results, while modelled meteorological fields have been derived from IFS. In the case of the Paris area, the meteorological model showed a very good performance in reproducing the observed wind speed, whose temporal evolution clearly influences the corresponding temporal variability of NO 2 concentrations (Fig.
Discussion The results from a mathematical model depend on three main factors: the model formulation (in terms of its assumptions, sub-models, numerical methods and their implementation in computer code); the model input data including boundary conditions; the skill of the model user particularly with respect to use of default values for certain inputs and parameters. When comparing results from a modelling exercise, the performance, assessed from comparison between modelling results and data, is influenced by all three of these factors. It is therefore difficult to make judgments on the performance of a model without understanding the importance of configuration and use. In this model intercomparison exercise, we have tried to achieve a greater focus on the effects of model formulation by standardizing the model input data as far as possible and running models for specific time periods having CMAQ shows the largest RMSEs between predicted and observed values for NO 2 over all campaigns; LOTOS-EUROS shows the lowest RMSEs for SO 2 over all campaigns; CAMx always exhibits the highest RMSEs for SO 2 over all periods.
The highest RMSEs are shown by CAMx, MINNI and RCG. CAMx. It is worth noting that the modelled diurnal cycles show a weak morning peak, more typical of surface sources not observed in observations. Conversely, measured data present a diurnal peak, usually related to enhancing downward mixing of aloft sources, where most SO 2 is emitted. Discrepancies among models and with respect to observations can also be due to chemistry. For example, in 2009,  Bessagnet et al. (2014)  reported for CHIMERE an underestimation of SO 2 concentrations on an hourly basis, while sulfate was overestimated; conversely RCG, adopting a more simplified approach for sulfur chemistry than CHIMERE, overestimated SO 2 , while it underestimated sulfates. PM 10 model performances are less homogenous within the 4 years than other pollutants.
Conclusions One of the main outcomes of such a multi-seasonal intercomparison is that in most cases model performances are more influenced by the model setup than the season. Another general outcome stemming from the whole exercise is that model performances are more different from one pollutant to another than for the same pollutant within the different seasons. This confirms once again that on average and for the limited dataset used in this exercise, the model formulation (parameterization of chemical/physical processes, calculation of meteorological diagnosed variables) and setup (number of verti-cal levels, value of key parameters, etc. ) are more influencing than raw meteorological conditions on model performance. One of the few exceptions is shown by O 3 in 2009 where model results were characterized by RMSE values very similar to the other years, whereas bias was negative instead of positive as in the three previous years.
Acknowledgements. The EBAS database has largely been funded by the  CLRTAP-EMEP program , AMAP and by  NILU  internal resources. Specific developments have been possible due to projects like EUSAAR (EBAS web interface),  EBAS-Online  (upgrading of the database platform) and HTAP (import and export routines to build a secondary repository for support of http://www. htap. org. A large number of specific projects have supported development of data and metadata reporting schemes in dialog with data providers (CREATE, ACTRIS and others). For a complete list of programs and projects for which EBAS serves as a database, please consult the information box in the framework filter of the web interface. These are all highly acknowledged for their support, particularly Anne-G. Hjellbrekke. INERIS was financed by the  French Ministry of the Environment, Energy  and  Marine Affairs.
(1999) BEIS 3. 1. 4 No No LANDUSE Corine Land Cover 2000 Corine Land Cover 2000 Corine Land Cover (13 classes) (13 classes) 2006 (44 classes) 1/60 × 1/60 • 1/60 × 1/60 • About 250 m CHIMERE Chimere2013 9σ 500 hPa 20 m No MEGAN model v2. 04 MEGAN model v2. 04 (24 GLOBCOVER classes) About 300 m EMEP Version rv4. 1. 3 Vertical layers 20σ Vertical extent (hPa or m) 100 hPa First layer depth 90 m Correction of first-level concentration Yes Biogenic VOC Based upon maps of 115 species from Koe- ble and Seufert (2001) and hourly temperature and light; see Simpson et al. (2012) Soil NO From Simpson et al. (2012) Landuse database CCE/SEI for Europe, elsewhere GLC2000 Resolution CCE/SEI Flexible, ∼ 5 km.
(Vestreng et al. , 2007)for 2009(Vestreng et al. , 2007);Emission regridding was based on INERIS expertise and performed by means of various proxies:population data coming from the EEA database merged with global data (from the Socioeconomic Data and Applications Center http://sedac. ciesin. columbia. edu) to fill gaps in Europe; -the US Geophysical Survey land use at 1 km resolution (http://www. usgs. gov/); -French bottom-up emission data for wood combustion to derive a proxy based on population density; -EPER data for industries. The EPER decision is based on Article 15(3) of Council Directive 96/61/EC (EC,.
Realistic Instances Instances. We now consider problem instances which are based on real-world data modeling the city of Nice, France. The city is situated in a seismically active region, and has encountered several earthquakes and tsunamis in its history. Figure  2  shows the underlying graph, which is generated from OpenStreetMap data. We used 500 nodes and 1124 edges. To generate loss values, we used seismic simulation data from [L + 14] that simulates an earthquake with similar characteristics as the Ligure earthquake of 1887 with a magnitude of approximately 6. 9M W. Based on these simulation results, loss values were estimated corresponding to the expected damage to infrastructure. Figure  3  shows the level of damages considering the Ligure earthquake scenario. The population data come from the French National Institute for Statistics and Economic Studies (INSEE) who estimate the population of each city including Nice and the repartition of the population over the area.
The phylosignal Package The phylosignal package provides a collection of tools to visualize, measure, test, and explore the phylogenetic signal in continuous traits (Table  1 ). The package is written in R and C++ languages and is fully accessible through the R environment. The latest stable version is accessible from The Comprehensive R Archive Network (https://cran. r-project. org/web/packages/phylosignal/) while the development version is hosted on GitHub (https:// github. com/fkeck/phylosignal). The phylosignal package is a free software released under the GNU GPL-3 license and any contribution is welcome. This package builds on the R ecosystem richness and takes full advantage of ape  (Paradis et al. 2004 ) for tree manipulation and plotting capacities and adephylo  (Jombart et al. 2010a)  for tree walking algorithms and phylogenetic distances computing.
Data format The analysis of phylogenetic signal typically involves working with a phylogeny and trait values associated with each tip (leaf). The phylobase package  (Hackathon et al. 2013 ) defines the S4 class phylo4d designed Table  1. List of the phylosignal package main functions and their description. specifically to handle such kind of data. Thus, a phy-lo4d object connects a phylogenetic tree with a table of trait values and constitutes the basic input for many functions implemented in phylosignal. The phylobase package comes with all the necessary functions to construct and manipulate phylo4d objects. For the users who are not used to handle phylogenetic data within the R environment, phylosignal adds the simple function read. p4d, which constructs a phy-lo4d object from a phylogenetic tree stored in a Newick file and tips data stored in a CSV file.
Data visualization The first step of any statistical analysis should be a graphical exploration of the data. The R language provides very powerful and flexible graphics facilities  (Murrell 2005). They are extended for phylogenetic tree visualization with traits data by many packages: ape  (Paradis et al. 2004) , phytools  (Revell 2012) , adephylo  (Jombart et al. 2010a ). The phylosignal package aims to provide a simple but complete interface to map traits data onto a phylogenetic tree. The users have access to three main functions to generate high quality graphics: barplot. phylo4d, dotplot. phylo4d and gridplot. phylo4d, which can, respectively, represent univariate and multivariate traits data as bars, dots, and colored cells. Each of these functions comes with several arguments to precisely control graphical aspects.
Indices for general measurements of phylogenetic signal The function phyloSignal provides a generic interface to compute indices and tests on multiple traits from a phylo4d object. The package implements two methods directly based on the autocorrelation principle. • The Moran's I index  (Moran 1948 (Moran , 1950) )  is the standard measure of autocorrelation used in spatial statistics and has been proposed as a way to measure the phylogenetic signal by  Gittleman and Kot (1990). The function phyloSignal computes I using Equation  1 with y i and y j being the trait value measured for species i and species j, respectively, n being the number of species and, by default, w ij ¼ 1 dij ; d ij being the patristic distance between species i and species j. I ¼ n P n i¼1 P n j¼1 w ij P n i¼1 P n j¼1 w ij ðy i À yÞðy j À yÞ P n i¼1 ðy i À yÞ 2 (1) • The Abouheif's C mean index  (Abouheif 1999 ) has been shown to be a Moran's I index computed with a specific matrix of phylogenetic weights  (Pavoine et al.
Therefore, the use of the correlogram may be strongly limited for small trees and when tips are not uniformly distributed within the phylogeny. In response to this potential problem, the phylosignal package comes with an original implementation of the phylogenetic correlogram for which the autocorrelation can be computed continuously. This is achieved by computing the Moran's I index using a specific matrix of phylogenetic weights w based on a normalized Gaussian function (Equation  2 ). w ij ¼ 1 r ffiffiffiffiffi 2p p e ðd ij ÀlÞ 2 2r 2 (2) Therefore, a phylogenetic weight matrix can be computed giving l, which defines the distance at which a tip will have the strongest influence and r which defines the decrease of influence around l. This matrix can be computed using the function phyloWeights, but the phylogenetic correlogram can be estimated directly with the function phyloCorrelogram.
Additional functionalities The phylosignal package comes with some additional features to analyze phylogenetic signal. The function phyloSignalINT computes phylogenetic signal indices and tests for each internal node of a given phylogeny. Combined with lipaMoran, it can be helpful to identify an interesting region, exhibiting strong conservation, for example, in the phylogenetic tree. If bootstrapped replicates of the phylogeny are available, the function phyloSignalBS can be used to compute signal indices and tests for each bootstrap. The function renders the results as boxplots allowing assessing the effect of phylogenetic reconstruction uncertainty on phylogenetic signal estimates. Finally, the function graphClust implements a simple method to perform traits clustering under phylogenetic constraints  (Keck et al.
Example: Phylogenetic Signal of Pollution Sensitivity in Diatoms In order to demonstrate the application of phylosignal, we comment on an analysis of the phylogenetic signal for 17 diatoms species. The trait analyzed is the specific pollution sensitivity index, IPSS (Coste 1982). The diatoms are taken from the order Naviculales and the phylogenetic tree is taken from  Keck et al. (In press b). This dataset is deliberately kept simple for demonstration purposes: this is a very brief overview of the diversity existing in this clade but it constitutes a good case study (for a more comprehensive discussion about phylogenetic signal in diatoms sensitivity to pollutions, see  Keck et al. In press a,b). The dataset is included in the package and can be loaded with the following command.
Conclusion We have presented the phylosignal package and shown how it can be used to describe and analyze the phylogenetic signal in biological traits. The fact that phylosignal is integrated in the R ecosystem and uses the standard format phylo4d makes it interoperable with several other methods implemented in the R language. For example, users can complete these results with a phylogenetic principal component analysis  (Jombart et al. 2010b ) implemented in adephylo to detect combinations of traits that are phylogenetically autocorrelated. They can also use the tools implemented in ape to investigate evolutionary models through a generalized least squares approach  (Paradis 2011). The combination of these tools will help to characterize the phylogenetic signal and to identify historical and ecological processes which drive patterns of trait values in the phylogeny.
Experimental protocol To assess the performance of MO-ParamILS we consider five configuration scenarios, described by Table  1. These scenarios use three datasets and two target algorithms, which belongs to ACLib 5  , a comprehensive algorithm configuration library, and are already known and have been studied in single-objective algorithm configuration. Details of the two algorithms are precised by Table  2. Note that the neighbourhood relation of ParamILS considers all parameters as categorical; henceforth for integer or continuous parameters the set of values have been discretised before all experiments. Our experimental protocol involves three consecutive steps, namely training, validation and test. In the training step, the configurator is run 25 times on 25 different permutations of the training set, resulting in 25 archives.