dataesr/scientific-paragraphs-categorization huggingface dataset:
Instead of going through Step 1, the user can also provide monthly anthropogenic emissions files built by other means. Soil NO does not have to be provided for these files, being it is considered as biogenic emissions (even if agricultural activities are anthropogenic). This leads to monthly files for each species and for a typical year. For the MELCHIOR chemical mechanism implemented by default in CHIMERE, emitted species are listed in Table  3. Depending on the spatial domain, CHIMERE has been used with several anthropogenic emissions datasets. The largest number of studies was over western Europe and two datasets were used: (i) the EMEP database  (Vestreng, 2003)  and, more recently, the TNO database during the GEMS and MACC projects  (Kuenen et al. , 2011). These data are spatially interpolated to the model grid.
This is performed using an intermediate fine grid with a 1 km resolution (GLCF dataset,  Hansen and Reed, 2000). Soil types described on the fine grid allow for a better apportionment of the emissions according to urban, rural, forest, crops and maritime areas. This preprocessing is provided with the model distribution to all users. The way in which these emissions are estimated could have a strong impact on modelled concentrations. In  Menut et al. (2012) , a sensitivity study was done to quantify the impact of the temporal profile used to disagregate emissions mass fluxes. More recently, continental scale modelling was done over the United States and the model used the US EPA inventory to model air quality during the AQMEII project  (Rao et al. , 2011; Schere et al. , 2012; Solazzo et al.
As one of the strongest "air pollution hotspots" in Europe, the model is also used to simulate the Po Valley pollution with a specific inventory, as described in de  Meij et al. (2009) , among others. Whatever the database and its resolution, the species NO x and NMVOCs (non-methane volatile organic compounds) have to be distributed in the chemical mechanism species. Annual emissions of NO x are first speciated as 9. 2 % of NO 2 , 0. 8 % of HONO and 90 % of NO, following GENEMIS recommendations  (Friedrich, 2000; Kurtenbach et al. , 2001; Aumont et al. , 2003). Emissions of SO x are speciated as 99 % of SO 2 and 1 % of H 2 SO 4. The GENEMIS NMVOC speciation is used for the same districts and for 6 types of emission activity sectors: traffic, solvents, industry (except solvents), energy extraction/production, residential (except solvents), and agriculture.
NO biogenic emissions include contribution from both forest and agricultural (fertilizers) soils. EFs are static and refer to years 2000-2001. They are obtained summing up over several plant functional types (e. g. broadleaf and needle trees, shrubs, etc. ). LAI database is given as a monthly mean product derived from MODIS observations, referred to base year 2000. Hourly emissions are calculated using 2 m temperature and short-wave radiation from a meteorological model output. Terpene and humulene emissions are not calculated in this model version and are set to zero. For European studies with CHIMERE, a comparison of the simulated formaldehyde column was presented in  Curci et al. (2010). Formaldehyde concentrations variability is primarily driven by the oxidation of biogenic isoprene over Europe.
The temporal and horizontal resolutions of the fire emissions depend on the resolution of the different parameters. The area burned parameter is estimated from the global observations of fire activity and areas burned at a daily and 1 km resolution from the MODIS instrument  (Giglio et al. (2010) ), coupled to the SEVIRI/METEOSAT observations  Roberts and Wooster (2008)  for the regions covered (Europe and Africa) to allow the evaluation of a diurnal cycle. An example is presented in Figure  11  for may, 8 2012. Depending on the fire location, a specific vegetation burned is attributed using the USGS landuse database (at 1km resolution) and the corresponding fuel load (or carbon content) is evaluated from simulations by the ORCHIDEE vegetation and carbon cycle model  (Krinner et al. (2005) ).
g. WRF simulations used for the CHIMERE simulations). A database including all CHIMERE species will be made available to users via the ECCAD portal (ECCAD) and the near- real time evaluation of the emissions is on-going at LMD (COSY (  2013 )). Updates on the availability of the fire emission module will be available from the CHIMERE web page. If the data already exist, the run continues. If not, a preprocessor script will create the data directory. The strength of this approach is that the user may easily create very particular chemical schemes. All chemical parts being independent, 1300 the user may choose to have gas chemistry or not, aerosols chemistry or not (and the number of bins), sea salt, dust, secondary organic aerosols, persistent organic pollutants, tracers or not. The nomenclature was defined to be easily understandable and the user can build chemistry sensitivity stud-1305 ies by changing reactions rates, photo dissociation rates, etc.
The area burned parameter is estimated from the global observations of fire activity and areas burned at a daily and 1 km resolution from the MODIS instrument  (Giglio et al. , 2010) , coupled to the SEVIRI/METEOSAT observations  (Roberts and Wooster, 2008)  for the regions covered (Europe and Africa) to allow the evaluation of a diurnal cycle. An example is presented in Fig. 11  for 8 May 2012. Depending on the fire location, a specific vegetation burned is attributed using the USGS (US Geological Survey) land use database (at 1 km resolution) and the corresponding fuel load (or carbon content) is evaluated from simulations by the ORCHIDEE (Organising Carbon and Hydrology in Dynamic Ecosystems) vegetation and carbon cycle model  (Krinner et al. , 2005). Finally, the emissions are converted from carbon (or DM, considering that it is 45 % carbon, as shown in van der  Werf et al.
, 2010)  to each species using emission factors from the  Akagi et al. (2011)  review. Any species may be included in the inventory provided that emission factors are available. For a full description of each step of the calculation, the reader is referred to  Turquety (2012). In addition to the amount of trace gases and aerosols, the injection altitude is a critical parameter. Indeed, fires can release enough energy to trigger or reinforce convection  (Freitas et al. , 2007; Rio et al. , 2010). These events will be accounted for by using a parameterisation of pyroconvectionstill being implemented -to evaluate emission profiles based on the fire intensity and the meteorological conditions (e. g. WRF (Weather Research and Forecasting) simulations used for the CHIMERE simulations). A database including all CHIMERE species will be made available to users via the ECCAD portal (ECCAD, 2013) and the near real-time evaluation of the emissions is on-going at LMD (COSY, 2013).
, 2009a). The model version with gaseous and aerosols species was used during several projects, such as MILAGRO over the Mexico area  (Hodzic et al. , 2009 (Hodzic et al. , , 2010b,a;,a; Hodzic and Jimenez, 2011) ; MEGAPOLI over the Paris area  (Royer et al. , 2011) , EC4MACS for the climate and air pollution mitigation strategies in Europe, and AQMEII for model inter-comparisons between the United States and Europe  (Pirovano et al. , 2012). More recently, two European projects have had new developments: the CIRCE project where the first online coupling between chemistry and vegetation was done between CHIMERE and ORCHIDEE, and ATOPICA for the development of a new module for the pollen modelling.
Long-term evaluation The long-term evaluation of a model is probably the oldest way to estimate its accuracy during pollution events; not only should the model be able to simulate specific and huge pollution events, it also has to accurately calculate low concentrations in absence of pollution. For a CTM, the air quality networks are able to continuously deliver hourly concentrations of O 3 , NO 2 and particulate matter over the past decades. Depending on the model domain and resolution, CHIMERE results have been compared to surface data. Enjoying the increase of computational capabilities, these comparisons have evolved from a few surface dataset to complete hourly validation over several years. The first comparisons were done for a few days and over a limited region in the Paris area  (Menut et al.
In the near future, it is planned to produce analyses by assimilating simultaneously surface and satellite measurements of ozone. Such a product could be of great interest to study tropospheric ozone variability and trends, especially in regions where in situ observations are scarce such as the Mediterranean basin. This CHIMERE-EnKF software will be tested operationally during the FP7/MACC-II project. Moreover, assimilation of other species such as NO 2 and CO (from satellite) is planned within the framework of the same MACC-II project.
Experimental forecasts Since its early developments, CHIMERE has been used both for analysis and forecasting. For the release of every new model version, the development priorities were driven by the results of test case analyses and daily experimental forecasts. A recent example of this is the COSY project which aims at producing systematic comparisons between observations and a set of CHIMERE forecasts and making them available on a website (www. lmd. polytechnique. fr/cosy/). This project by the IASI instrument and comparisons of the obtained gain with MOZAIC data (Data courtesy of A. Coman). 12. 1 Experimental forecast Since its early developments, CHIMERE has been used both for analysis and forecast. For the release of every new model version, the development priorities were driven by the results of test case analyses and daily experimental forecasts.
As a consequence, these modelling tools provide support to national authorities on air quality management, and they assist the selection of regulatory measures that may be efficient in limiting the intensity of pollution episodes. In this context, CHIMERE is now involved in the prototype toolbox dedicated to air quality episode management in the MACC-II project. Another contribution of such platforms to air quality issues is the provision of daily assessments of the model's ability to predict pollutant concentrations. Daily scores are indeed an important parameter, which gives insights into the research efforts that need to be made to improve the model behaviour. Current research efforts are thus focusing on the implementation of dynamical input data such as biomass burning emissions  (Kaiser et al.
A preliminary study applying this heavy metal CHIMERE version has been presented in  Vivanco et al. (2011) , where the model performance was evaluated for Spain. Figure  18  presents the mean annual concentrations of lead for 2009. This preliminary version has also recently been applied for a European domain at a horizontal resolution of 0. 2 • for 2008  (González et al. , 2012). Important limitations were set for metal emissions. Only anthropogenic sources have been considered, although some metals can be released into the environment by both natural sources and human activities. Moreover, only Cd and Pb emissions are available in the EMEP expert database for 2008  (Vestreng et al. , 2009) ; for the other metals, emissions were taken from TNO totals estimated for 2000  (Van der Gon et al.
, 2005)   The European (FP7) project Atopica aims to evaluate, using numerical modelling, statistical data analysis, and laboratory experiments, the influence of changes in climate, air quality, land use, and the subsequent distribution of invasive allergenic plant species and allergic pollen distribution on human health. Modelling of ragweed and birch pollen emission and dispersion in the atmosphere using CHIMERE plays a key role in this project. Pollen grains are about 5-50 times larger in size than conventional atmospheric aerosols. The scale analysis  (Sofiev et al. (2006))  shows that the assumption of pollen grains being transported together with air masses following the airflow, including small turbulent eddies, still applies. Pollens are implemented in CHIMERE as a special aerosol type having a single size distribution bin of 20-22 µm depending on the pollen type.
As we did not have this information for the metals coming from the TNO database, we used a SNAP disaggregation similar to PM 2. 5 particles for those species. We also used a temporal profile similar to PM fine particles. Better knowledge of the temporal behaviour of metal emissions for each SNAP activity would reduce the input errors. Other aspects, such as boundary conditions, should also be considered in order to improve model results. At this stage, no boundary conditions were applied, as no information on many of these metals is available from global models.
Pollens A new research direction involving CHIMERE is modelling the dispersion of pollen grains in the atmosphere. The prevalence of pollen allergy in European countries is estimated between 12 to more than 35 %  (Burney et al. , 1996). The quantity of some highly allergenic pollens, such as ragweed, is increasing and seems to correlate with allergic diseases  (Rybnicek and Jaeger, 2001). Conclusions from a number of European and international climate research projects suggest that future climate change and variability may strongly affect pollen emission and dispersal in Europe  (Christensen et al. , 2007) , thereby influencing the prevalence of atopic diseases. The European (FP7) project Atopica aims to evaluate, using numerical modelling, statistical data analysis and laboratory experiments, the influence of changes in climate, air quality, land use, and the subsequent distribution of invasive allergenic plant species and allergic pollen distribution on human health.
Sub From a health outcome perspective, it is important to know how much local concentrations deviate from grid-averaged values due to the proximity to emission sources (e. g. near roads, in residential zones, industrial parks, etc. ). To address the issue of sub-grid scale emission heterogeneity, an emission scheme has been developed and implemented in the CHIMERE model  (Valari and Menut, 2010). Instead of adding together emissions from all sources and all activity sectors at each model grid cell, we split them into four different categories: traffic, residential, outdoor activities (entertainment) and all other sources (including point sources). Grid area fractions corresponding to each type of emission are calculated based on high resolution land use data (CORINE land cover at 100 m resolution).
Acknowledgements. The CHIMERE development team thanks  Mireille Lattuati ,  Cécile Honoré ,  Hauke Schmidt ,  Claude Derognat , and  Laurence Rouïl , among others, for their contribution in former CHIMERE developments. We also acknowledge  Eric Chaxel  for the WRF model meteorological interface,  Christian Seigneur  and  Betty Pun  ( AER ) for the reduced SOA scheme,  Mian Chin  and  Paul Ginoux  ( NASA ) for the GOCART aerosols concentrations fields used as boundary conditions,  Sophie Szopa  and  Didier Hauglustaine  ( IPSL/LSCE ) for the LMDz-INCA gas concentrations elds used as boundary conditions,  Athanasios Nenes  and the  ISORROPIA team  for the free use of ISORROPIA model, and the  EMEP-MSCWEST team  (http://www. emep. int)for anthropogenic emissions database,  Cathy Liousse ,  Bruno Guillaume  and  Robert Rosset  (LA/OMP, http://www.
Systematic search for a given parameter range In the three different conditions (D lin , D sat , and D spk ), we systematically enumerated all the integer-valued sets of parameters for different parameter ranges up to the limits given in Table  1  for n~6. This Table displays the parameter values for which the number of computable Boolean functions stops growing. For instance for n~5 all positive linearly separable functions can be implemented in a linear model (D lin ) with integer-valued weights between 0 and 5, and a threshold between 0 and 9. For each parameter set we computed the associated Boolean functions; if this function was in the previously generated list of positive representative function we removed it from the list and recorded the set of parameters in a hdf5 data file. We then went to the next set of parameters and repeated the operation.
All these operations were programmed using python 2. 7. 1. We used numpy version 1. 5 (www. numpy. org) for matrix operation, and h5py 1. 3. 1 (A. Collette, HDF5 for Python, 2008; http://h5py. alfven. org) to record the parameter sets in an hdf5 file. This method provides lower bounds on the computational capacity of a neuron with a non-linear dendritic sub-unit. Therefore in D sat and D spk condition the actual computational capacity is superior to the one presented in Figure  2 , because we may have missed parameter values for n~6. A neuron with saturating dendrites cannot implements the FBP using a local strategy For the neuron to correctly produce output of y~1 to both input vectors ½1,1,0,0 and ½0,0,1,1 and for the strategy to be local means that: D sat (w d 1 zw d 2 ) §H for response to ½1,1,0,0, because the dendritic sub-unit triggers a somatic spike and w s 3 zw s 4 §H for response to ½0,0,1,1, because in this case the dendritic sub-unit also triggers a somatic spike.
Biophysical model We built the compartmental biophysical models with NEU-RON software version 7. 1  [54]  coupled with Python 2. 7. 1. (www. python. org) Morphological parameters (e. g. dendrites' diameters) are described in the Figure  4A. The axon is not modeled because of its negligible contribution to the conductance load. For the reduced models, the majority of parameters are set to their default value within this NEURON version. The active membrane parameters are the standard Hodgkin-Huxley channels (hh in NEURON) also used with their default parameters; the default and non-default parameters defining passive and active properties of this model are given in Table  3. To model AMPA synapses we used the built-in Exp2Syn synapses; for NMDA synapses we used nmdanet. mod from  [55]. The range of tested synaptic weights which correspond to the maximum conductance and the synaptic parameters are described in Table  3.
We used biophysical modeling to illustrate the mapping between our abstract binary neuron model and a full multi-compartment dynamical neuron model. We designed the scripts for testing the biophysical models input-output mapping such that they can be used with any type of arbitrarily detailed biophysical neuron model; the script is available in the ModelDB (when this manuscript will be accepted). For the realistic model of cerebellar stellate cells, the morphological and biophysical parameters are taken from  [10].
Background: DNA barcoding of rain forest trees could potentially help biologists identify species and discover new ones. However, DNA barcodes cannot always distinguish between closely related species, and the size and completeness of barcode databases are key parameters for their successful application. We test the ability of rbcL, matK and trnH-psbA plastid DNA markers to identify rain forest trees at two sites in Atlantic central Africa under the assumption that a database is exhaustive in terms of species content, but not necessarily in terms of haplotype diversity within species. Methodology/Principal Findings: We assess the accuracy of identification to species or genus using a genetic distance matrix between samples either based on a global multiple sequence alignment (GD) or on a basic local alignment search tool (BLAST).
Where a local database is available (within a 50 ha plot), barcoding was generally reliable for genus identification (95-100% success), but less for species identification (71-88%). Using a single marker, best results for species identification were obtained with trnH-psbA. There was a significant decrease of barcoding success in species-rich clades. When the local database was used to identify the genus of trees from another region and did include all genera from the query individuals but not all species, genus identification success decreased to 84-90%. The GD method performed best but a global multiple sequence alignment is not applicable on trnH-psbA. Conclusions/Significance: Barcoding is a useful tool to assign unidentified African rain forest trees to a genus, but identification to a species is less reliable, especially in species-rich clades, even using an exhaustive local database.
It is based on short standardized DNA sequences that should ideally be routinely amplifiable and easily sequenced with ''universal'' primers (primers anchored in DNA regions highly conserved over large taxonomic range). The utilisation of barcoding for taxonomic identification at species level relies on the expectation that the query species is included in the reference database and that the different species in the database have distinct barcode sequences. In this case, the taxonomic effort can be concentrated on the separation of taxa in the field and in the collection of specimens from which a reference database is constructed. Non-taxonomy specialists can then send samples to a laboratory to get the barcode sequenced, and identify their specimen by matching the sequences to the reference database.
In a tropical rain forest in Queensland, Australia, in an area with poorly known flora, a DNA-barcoding approach correctly estimated the number of species present in two 0. 1 ha plots with almost 90% accuracy. This estimation of species richness at the local scale was obtained from a single survey and without the need of a high level expertise in field identification. It was also obtained in a much shorter time than with the traditional taxonomic methods  [19]. The success of a barcoding approach for species identification obviously depends on the exhaustiveness of the database used, because missing species cannot be identified. In the best case, a method could detect that a new species is present  [20] , but there is a risk that it will assign a tested sample to a wrong species. In addition, the presence of shared haplotypes (i.
e. identical DNA sequences) between closely related species is reducing barcoding success and largely explains the limitation of the approach in species-rich communities. An aspect little investigated so far is that while a barcode database might be exhaustive at a local scale in terms of species representation, it is much less likely to be exhaustive in terms of haplotype diversity within species because databases are typically created using a few samples per species. Within species DNA polymorphism is typically low or absent in coding pDNA regions like rbcL and matK but can be frequent in non-coding regions like trnH-psbA. Nowadays, sequences for African rain forest trees are available on GenBank (http://www. ncbi. nlm. nih. gov/genbank/), but only for a small proportion of the flora: there is no regional-scale barcode database.
It is very likely that building an extensive and exhaustive database of barcode sequences for African rain forest trees will take several decades. However, it is possible to construct a local DNA barcode database for an area of special interest. We constructed the first local database for African rain forest trees in   Two methods were used to evaluate barcoding identification success: the minimum genetic distance between sequence pairs (GD) and the maximal percentage identity in a basic local alignment search tool (PI). Correct identification = the individual is assigned to the correct species or genus only, multiple identification = the individual is assigned to several species or genera including the right one, wrong identification = the individual is assigned to one or several species or genera not including the right one.