dataesr/scientific-paragraphs-categorization huggingface dataset:
This approach can however cause problems if the modelled and observed MDT are significantly different to each other as the assimilation will constantly adjust the model fields (including the subsurface density structure)  (Lea et al. 2008). Some systems mitigate against this by applying a bias correction approach either off-line (Mercator) or on-line as part of the data assimilation (FOAM). Profile temperature and salinity data are obtained either over the GTS or through an intermediate processing centre, CORIOLIS (for Mercator and CONCEPTS), with the Bluelink system accessing data through both these routes as well as from the US GODAE server, and generating a combined data-set. A similar set of platforms collecting profile temperature and salinity data are used by all the systems including Argo floats, XBTs (for temperature), moored buoys, gliders and marine mammals, with the TOPAZ system additionally assimilating ice-tethered profile data.
Sea-ice concentration data from satellites are assimilated only in the CONCEPTS, GOFS, FOAM, North Pacific (NP) MOVE and TOPAZ systems, with ECMWF prescribing (rather than assimilating) the sea-ice concentration based on observations. Most of these systems assimilate data from the SSM/I and/or SSMIS satellite instruments, with the CONCEPTS system also making use of sea-ice concentration data from Canadian Ice Service (CIS) charts, Radarsat and ship reports. TOPAZ is the only system to assimilate sea-ice drift data (estimated through cross-correlations of successive satellite images) operationally.
Assimilation methods A summary of the grid specifications, the algorithm and the initialisation procedure used by each system is provided in Table  2 , together with a reference for each system. The model resolution of the systems described here varies between about 1/12°for GOFS and Mercator HR (the higher resolution Mercator system), 1/8°for TOPAZ, 1/4°f or CONCEPTS, FOAM and Mercator LR (the lower resolution Mercator system), and about 1°for the ECMWF and global MOVE systems. Bluelink runs on a 1/10°grid in the Australian region, with much lower resolution elsewhere. NP MOVE also runs on a 1/10°grid in the western north Pacific region. Most of the systems use the same grid to perform the data assimilation as the model grid. However, Bluelink, CONCEPTS and Mercator use a reduced resolution analysis grid to reduce the computational cost.
In contrast, the ensemble schemes rely on statistical information from model runs in order to determine how observational information is spread by the assimilation. The Bluelink  (Oke et al. 2013) , CONCEPTS  (Smith et al. 2014)  and Mercator  (Lellouche et al. 2013 ) ensemble systems use model data from previous long model integrations, while the TOPAZ  (Sakov et al. 2012)  system uses an up-to-date ensemble of model runs from the current forecast cycle. Most of the systems use a first-guess-at-appropriate-time (FGAT) scheme whereby the model counterpart of the observation is calculated close to the observation time. All of the systems make updates to the full ocean state (three-dimensional temperature, salinity and velocity fields and SSH, or isopycnal layer pressure for GOFS) except for MOVE where only temperature and salinity fields are updated directly by the assimilation.
For the other systems, corrections to the velocity fields are calculated using either dynamical or statistical relationships, rather than assimilating velocity data directly. The corrections (increments) produced by the data assimilation are used to initialise the forecasting systems in a number of ways, most of which are designed to reduce the generation of fast transient model responses from unbalanced increments. ECMWF, GOFS, FOAM, MOVE and Mercator use the Incremental Analysis Update (IAU) method  (Bloom et al. 1996)  which adds in a fraction of the increments to the model fields at each model time-step with the aim of reducing the shock to the model. The period over which these are added in varies from 6 hours in GOFS, to one day in FOAM, out to 5-10 days for Mercator, ECMWF and MOVE.
All of these systems use a constant weighting for the IAU except for Mercator  (Benkiran & Greiner 2008; Lellouche et al. 2013 ) who choose a distribution function which crosses the increments of two consecutive cycles in order to ensure continuity in the application of the increments from one cycle to the next. The Bluelink system uses an adaptive nudging scheme  (Sandery et al. 2011 ) in which the model is nudged towards the analysis using a time-scale which depends on the magnitude of the increments. CON-CEPTS and TOPAZ apply increments directly to the initial model state before the forecast is run. Data assimilation methods generally assume that the model does not exhibit significant systematic errors. This is often not the case in practice, and some groups attempt to correct for model biases by using various schemes.
For example, ECMWF employs a bias correction scheme which combines a correction to 3D temperature and salinity in the extra-tropics, with a correction to the pressure gradient field in the tropics  (Balmaseda 2007). This latter correction is also used in the FOAM system  (Bell et al. 2004) , while Mercator applies a general 3D temperature and salinity slowly-evolving large-scale bias correction globally. These bias correction fields are calculated by accumulating a running average of the data assimilation increments in the ECMWF and FOAM systems, with the Mercator biases calculated using a 3DVar approach based on innovations over the preceding months.
CB05 describes tropospheric chemistry with 54 species and 126 reactions. Wet deposition and lightning nitrogen monoxide (NO) emissions are modelled in C-IFS using the detailed input of the IFS physics package. A 1 year simulation by C-IFS, MOZART and the MACC re-analysis is evaluated against ozonesondes, carbon monoxide (CO) aircraft profiles, European surface observations of ozone (O 3 ), CO, sulfur dioxide (SO 2 ) and nitrogen dioxide (NO 2 ) as well as satellite retrievals of CO, tropospheric NO 2 and formaldehyde. Anthropogenic emissions from the MACC/CityZen (MACCity) inventory and biomass burning emissions from the Global Fire Assimilation System (GFAS) data set were used in the simulations by both C-IFS and MOZART. C-IFS (CB05) showed an improved performance with respect to MOZART for CO, upper tropospheric O 3 , and wintertime SO 2 , and was of a similar accuracy for other evaluated species.
Because of the complexity of the chemical mechanisms for reactive gases, modules for atmospheric chemistry were not initially included in the IFS. Instead, a coupled system  (Flemming et al. , 2009a ) was developed, which couples the IFS to chemical transport model (CTM) Model for OZone and Related chemical Tracers 3 (MOZART,  Kinnison et al. , 2007)  or Transport Model 5 (TM5,  Huijnen et al. , 2010)  by means of the Ocean Atmosphere Sea Ice Soil (OASIS4) coupler software  (Redler et al. , 2010). Van Noije et al. (2014)  coupled TM5 to IFS for climate applications in a similar approach. The coupled system made it possible to assimilate satellite retrievals of reactive gases with the assimilation algorithm of the IFS, which is also used for the assimilation of meteorological observations as well as for aerosol and greenhouse gases.
Likewise, emissions and dry deposition input data are kept the same for all configurations. The purpose of this paper is to document C-IFS and to present its model performance with respect to observations. Since C-IFS (CB05) replaced the current operational MACC model system for reactive gases (IFS-MOZART) both in data assimilation and forecast mode, the evaluation in this paper is carried out predominantly with observations that are used for the routine evaluation of the MACC II system. The model results are compared (i) with a MOZART stand-alone simulation, which is equivalent to a IFS-MOZART simulation, and (ii) with the MACC re-analysis  (Inness et al. , 2013) , which is an application of IFS-MOZART in data assimilation mode. All model configurations used the same emission data. The comparison demonstrates that C-IFS is ready to be used operationally.
Emissions for 2008 The anthropogenic surface emissions were given by the MACCity inventory  (Granier et al. , 2011)  and aircraft NO emissions of a total of ∼ 0. 8 Tg N yr -1 were applied  (Lamarque et al. , 2010). Natural emissions from soils and oceans were taken from the Precursors of Ozone and their Effects in the Troposphere (POET) database for 2000  (Granier et al. , 2005; Olivier et al. , 2003). The biogenic emissions were simulated off-line by the MEGAN2. 1 model  (Guenther et al. , 2006). The anthropogenic and natural emissions were used as monthly means without accounting for the diurnal cycle. Daily biomass burning emissions were produced by the Global Fire Assimilation System (GFAS) version 1, which is based on satellite retrievals of fire radiative power  (Kaiser et al. , 2012).
The calculation of the loss by dry deposition has to account for the implicit character of the dry deposition flux since it depends on the mass mixing ratio X s. The dry deposition velocities were calculated as monthly mean values from a 1 year simulation using the approach described in  Michou et al. (2004). It used meteorological and surface input data such as wind speed, temperature, surface roughness and soil wetness from the ERA-Interim data set. At the surface the scheme makes a distinction between uptake resistances for vegetation, bare soil, water, snow and ice. The surface and vegetation resistances for the different species are calculated using the stomatal resistance of water vapour. The stomatal resistance for water vapour is calculated depending on the leaf area index, radiation and the soil wetness at the uppermost surface layer.
The latter is equivalent to a simulation of IFS-MOZART without data assimilation of atmospheric composition. The assimilated retrievals were CO and O 3 total columns, stratospheric O 3 profiles and tropospheric NO 2 columns. No observations of atmospheric composition have been feed in to the MOZ run. No observational information has been used to improve the tropospheric simulation of the C-IFS run. Another difference between MOZ and REAN is that the IFS diffusion and convection scheme, as used in C-IFS, controls the vertical transport in REAN, whereas MOZART's generic schemes were used in the MOZ run. MOZ, REAN and C-IFS used the same anthropogenic emissions (MACCity), biogenic emissions (MEGAN 2. 1;  Guenther et al. , 2006 , http: //acd. ucar. edu/~guenther/MEGAN/MEGAN. htm) and natural emissions from the POET project.
In situ observations The ozonesondes were obtained from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC) and from the ECWMF Meteorological Archive and Retrieval System. The observation error of the sondes is about ±5 % in the range from 200 to 10 hPa and -7-17 % below 200 hPa  (Beekmann et al. , 1994; Komhyr et al. , 1995, and Steinbrecht et al. , 1998). The number of soundings varied for the different stations. Typically, the sondes are launched once a week but in certain periods such as during O 3 hole conditions soundings are more frequent. Sonde launches were carried out mostly between 9 and 12 h local time. The global distribution of the launch sites is even enough to allow meaningful averages over larger areas such North America, Europe, the tropics, the Artic and Antarctica.
, 2003). Since the aircraft carrying the MOZAIC unit were based in Frankfurt, the majority of the CO profiles (837 in 2008) were observed at this airport. A further 10 of the 28 airports with observations in 2008 had a sufficient number of profiles: Windhoek (323), Caracas (129), Hyderabad (125) and London-Gatwick (83) as well as North American airports Atlanta (104), Portland (69), Philadelphia (65), Vancouver (56), Toronto (46) and Dallas (43). The North American airports were considered to be close enough to make a spatial average meaningful. Because of the varying data availability the North American mean is dominated by the airports in the eastern United States. Apart from Frankfurt, typically two profiles (takeoff and landing) are taken within 2-3 h or with a longer gap in the case of an overnight stay.
The data coverage for CO and O 3 was global, whereas for SO 2 and NO 2 , only a few observations in Europe were available at the data repository. The Airbase and EMEP databases host operational air quality observations from different national European networks. All EMEP stations are located in rural areas, while Airbase stations are designed to monitor local pollution. Many AirBase observations may therefore not be representative of a global model with a horizontal resolution of 80 km. However, stations of rural regime may capture the largerscale signal, in particular for O 3 , which is spatially well correlated  (Flemming et al. , 2005). The EMEP observations and the rural Airbase O 3 observations were used for the evaluation over Europe.
The DOAS method is applied in a 425-497 nm wavelength window  (Richter et al. , 2011)  for NO 2. and between 337 and 353 nm for HCHO  (Vrekoussis et al. , 2010). Second, the reference sector approach is applied to total slant columns for stratospheric correction. In a last step, tropospheric slant columns are converted to tropospheric vertical columns by applying an air mass factor. Only data with cloud fractions smaller than 0. 2 according to the FRESCO cloud database  (Wang et al. , 2008)  are used here. Furthermore, retrievals are limited to maximum solar zenith angles of 85 • for NO 2 and 60 • for HCHO. Uncertainties in NO 2 satellite retrievals are large and depend on the region and season. Winter values at middle and high latitudes are usually associated with larger error margins. As a rough estimate, systematic uncertainties in regions with significant pollution are of the order of 20-30 %.
Computational cost The computational cost is an important factor for the operational applications in CAMS. The computational costs of different configurations of IFS, C-IFS and IFS-MOZART are given in Table  3. Computational cost is expressed in billing units (BU) of the ECMWF IBM Power 7 super-computer. BUs are proportional to the number of used central processing units (CPU) times the simulation time. The increase in cost because of the simulation of the CB05 chemistry with respect to an NWP run is a factor of about 4 at resolutions T159 (110 km), T255 (80 km) and T511 (40 km). C-IFS (CB05) is about 8 times more efficient than the IFS-MOZART coupled system at a T159 resolution and about 15 times more at a T255 resolution. This strong relative increase in cost of IFS-MOZART is caused by the increasing memory requirements of the IFS at higher resolution, or also in data assimilation mode.
Dry deposition is modelled using pre-calculated monthly mean dry deposition velocities following  Wesely (1989)  with a superimposed diurnal cycle. Surface emissions and dry deposition fluxes are applied as surface boundary conditions of the diffusion scheme. Lightning emissions of NO can be calculated either by cloud height  (Price and Rind, 1993)  or by convective precipitation  (Meijer et al. , 2001). The latter parameterisation was used in this study. The anthropogenic emissions were taken from the MACCity inventory and biomass burning emissions from the GFAS data set for 2008. An evaluation for the troposphere of a simulation in 2008 with C-IFS (CB05) and the MOZART CTM (MOZ) as well as with the MACC re-analysis (REAN) was carried out. The model results were compared against ozonesondes, MOZAIC CO aircraft profiles, European surface observations of O 3 , CO, SO 2 and NO 2 , and global satellite retrievals of CO, NO 2 and HCHO.
Acknowledgements. MACC II is funded by the  European Union 's  Seventh Framework Programme  ( FP7)  under grant agreement no. 283576. The MOPITT data were obtained from the  NASA Langley Research Atmospheric Science Data Center. We are grateful to the  World Ozone and Ultraviolet Radiation Data Centre (WOUDC)  for providing ozonesonde observations. We thank the  Global Atmospheric Watch  programme for the provision of NO 2 , CO and SO 2 surface observations. We thank the  European Environmental Agency  for providing access to European O 3 observations in the AirBase database. We also thank the  MOZAIC (Measurements of OZone , water vapour, carbon monoxide and nitrogen oxides by in-service AIrbus aircraft) and  IAGOS (In-Service Aircraft  for a Global Observing System) programmes for providing CO profile observations.
Simulated surface O 3 , CO, NO 2 and SO 2 fields were compared against Global Atmo- spheric Watch (GAW) surface observations and additionally O 3 against observations from the European Monitoring and Evaluation Programme (EMEP) and the European air qual- ity database (AirBase). The global distributions of tropo- spheric NO 2 and HCHO were evaluated with retrievals of tropospheric columns from Global Ozone Monitoring Exper- iment 2 (GOME-2). Measurements Of Pollution In The Tro- posphere (MOPITT) retrievals were used for the validation of the global CO total column fields.
Sediment samples Seven sediment samples were collected in May 2006 to generate ecotoxicity data with the bioassay procedures described below. A handheld dredge, specially-designed by the University of Lorraine (Metz-LIEC) enabled superficial sediments (0-10 cm depth) to be raked from sites by an operator wearing hip waiters at distances varying from 2-7 m off the shoreline. They were extracted from sites (Figure  1 ) located on the Moselle river itself (Bousse, Bertrange, Thionville, Cattenom), from one of its tributaries (Orne) and from two adjacent surface ponds (La Maxe, Manom). Sites were chosen to reflect mixed and/or varying degrees of contamination based on historical knowledge of human activities linked to (in)organic chemicals (Table  1 ). After sampling, sediments were directly processed on site.
Toxicity testing procedures The characteristics of the solid phase and elutriate bioassays conducted to assess sediment ecotoxicity are highlighted in Table  2. 3, [16] [17] [18]  Only the Hydra solid phase assay (H-SPA), previously unpublished, is more fully described below, while the testing principle and procedures of the other assays are more briefly recalled. References listed in Table  2  can be consulted for more ample details on testing procedures. Measurement endpoints of the bioassays were determined with statistical methods and software recommended for each procedure. Correlation analyses (Pearson's product moment or Spearman's rank order tests with significance set at P<0. 05) were performed with Statistica software, version 5. 5. Measurement endpoints for solid phase tests were invariably reported on a dry-weight basis (see below).
This modelling strategy has two main motivations. First it reduces the burden of feature engineering, making it easier to generalize to multiple languages. Second it avoids modeling explicitly bilexical dependencies for which parameters are notoriously hard to estimate from small data sets such as existing treebanks. On the other hand this strategy becomes less intuitive when it comes to modeling free word order languages where word order and constituency should in principle be less informative. As such, the good results reported by  Hall et al. (2014)  are surprising. It suggests that word order and constituency might be more relevant than often thought for modelling free word order languages. Nevertheless, free word order languages also tend to be morphologically rich languages. This paper shows that a parsing model that can effectively take morphology into account is key for parsing these languages.
Multi-view treebanks Multi-view treebanks are treebanks annotated both for constituents and dependencies that have the property to be token-wise aligned  (Bhatt et al. , 2009; Seddah et al. , 2013; Qiu et al. , 2014). These double annotations are typically obtained by converting a constituency or dependency annotation into the other annotation type. This method was used for the construction of the dataset for the SPMRL 2013 shared task  (Seddah et al. , 2013) , which contains multi-view treebanks for a number of morphologically rich languages, for which either constituency or dependency treebanks were available. The same kind of process was applied to the Penn TreeBank using the Stanford conversion system to produce dependency annotations  (de Marneffe et al. , 2006). In this paper, we use both of these datasets.
For each of X X a X b X c a b c root - - X X X a X b X c a b c root - - X X a X b X c a b c root root root X X X a X b X c X d Base: h(wi) = i ∀wi ∈ W Recurrence: h(τ ) = h(ai) if ∀ a j :a j ∈γ,i =j (h(ai), h(aj)) ∈ E + ⊥ otherwise The three other cases are more problematic, since their correction would somehow require altering the structure of either the c-tree or the d-tree. Refraining from altering the constituency data set we instead use a catch-all procedure that essentially creates the problematic head annotation by analogy with the rest of the data, yielding a fully Robust procedure that is guaranteed to succeed in any case: Base: h(wi) = i ∀wi ∈ W Recurrence: h(τ ) = h(ai) if ∀ a j :a j ∈γ,i =j (h(ai), h(aj)) ∈ E + h(a KNN(τ →γ) ) otherwise where KNN(τ → γ) is a function returning a guess for the position of the head in γ, the right hand side of the rule, based on similarity to successfully head annotated rules.
The details are as follows. KNN(τ → γ) supposes a dataset D = (R i , H i ) N i=1 of successfully head annotated rules. In this dataset, each rule R i = τ → γ is associated with H i the position of the head in γ. We define the similarity between two rules R 1 = τ (1) → a (1) 1. a (1) k and R 2 = τ (2) → a (2) 1. a (2) k to be the Levenshtein distance between τ (1) , a (1) 1. a (1) k and τ (2) , a (2) 1. a (2) k. In practice for a given rule R the function returns the most frequent H among the 5 most similar rules in the data set. The full head annotated data set D is built by reading off the rules from the trees successfully annotated in the treebank by the Corrected procedure in a first pass. A second pass yields the final annotation by running the Robust procedure. 1  an overall quantification of the conversion procedure: % Success (Corrected) reports the number of trees succesfully annotated by the Corrected procedure and Silver UAS reports an UAS score obtained by comparing the reference dependency trees to the conversion of those obtained from the Robust conversion of the head-annotated phrase structure trees back to dependency structures.
The conversion works well apart from four languages (Arabic, Basque, German and Hungarian) which cause more difficulties. In order to better understand the problems faced by the conversion procedure, we manually inspected the errors returned by the Corrected pro-cedure. For each language, we sampled 20 examples of failures encountered and we manually categorized the errors using the four patterns illustrated in Figure  1. Across languages, 49. 9% of the errors come from the pattern Local Restructuration II and 50% from the pattern Forest effect and more suprisingly, we found only one example in our sample from the pattern Non projectivity in the Hungarian treebank. This overall average hides however an important variation across treebanks. The Forest effect is indeed massively found in the Basque 2 (100%) and German treebanks and more marginally in the Hungarian data set.
For Basque most of the additional morphological features further encode case and verbal subcategorization. For French the mwe field abbreviates IOB predicted tags derived from multi-word expression annotations found in the original dataset. Now let M be the set of values enumerated for a language in Figure  2  (right), we systematically added the following templates to model B: s 0. w t. m&s 1. w t. m&q 1. tag ∀m ∈ M s 0. w t. m&s 1. c t &q 1. m ∀m ∈ M s 0. c t. m&s 1. w t. m&q 1. m ∀m ∈ M s 0. w t. m&q 1. m&q 2. tag ∀m ∈ M s 0. w t. m&q 1. tag&q 2. m ∀m ∈ M s 0. c t &q 1. m&q 2. m ∀m ∈ M Essentially the model expresses interactions between morphological features from the constituent heads on the top of the stack and the morphological features from the tokens at the beginning of the queue.