dataesr/scientific-paragraphs-categorization huggingface dataset:
On the other hand, highresolution (HR) characterization of lake fraction in the ridge-hollow-lake wetland landscapes (i. e. Terentieva et al 2016) does not investigate the detailed small ponds size distribution for permafrost-affected WSL territory. The most recent circum-Arctic Permafrost Region Pond and Lake database (Muster et al 2017) deals with a small part of the WSL territory and does not allow quantitative estimation of lake and pond areal coverage and water volume in this region. It is known that small thaw ponds exhibit concentrations of CH 4 , CO 2 , and DOC that greatly exceed those in large lakes  (Bastviken et al 2004 , Kortelainen et al 2006 , Juutinen et al 2009 , Abnizova et al 2012 , Kankaala et al 2013 , Shirokova et al 2013 , Holgerson 2015 , Pelletier et al 2015a , 2015b).
For the entire permafrost-affected WSL territory of 1. 05 million km 2 , we used a superposition of 134 MR images. Images were treated using standard tools of ArcGIS 10. 3 software (Kennedy et al 2013). For automatic identification of lakes we used the Fmask algorithm  (Zhu et al 2015). It allows resolving the lakes even with some cloud coverage and uses several spectral ranges of Landsat-8 using a set of empirical spectral indexes. As input data we used the intensity of the upper atmosphere reflectance and the brightness temperature presented as nominal units of pixel brightness. First, for the mosaic of Landsat-8 imagery, cloud masks were defined for individual images. Then the cloud masks were removed from the images and replaced by cloudfree fragments taken from the adjacent images during the same period of observation.
Comparison of lake inventory in western Siberia with other studies The number of lakes and lake area-lake size distribution diagrams obtained in this work for the permafrostaffected part of WSL are in fairly good agreement with the inventory of all lakes of the circum-Arctic permafrost region  (Muster et al 2017) ; deviation between the two estimations does not exceed several percent (figure  3 ). A comparison with the world lake database of  Cael and Seekell (2016)  is shown in figure  4  where empirical distribution lakes in Sweden and throughout the world are shown. It can be seen that for lakes smaller than 10 4 -10 5 m 2 , at both the planetary and regional (WSL, Sweden) level, there is a sizeable difference between the results of direct measurement and calculation from the power law of lake distribution.
For very small ponds of 100 m 2 , this difference is a factor of 6. 5. As such, the estimation of total concentrations and emissions of CH 4 and CO 2 from small lakes on a worldwide scale, performed based on Monte Carlo numerical modeling using the Pareto power law (Holgerson and Raymond 2016), cannot be applied to very small thaw ponds of western Siberia. In the WSL direct remote sensing using HR images from 78 TSs allows for a more rigorous lake and pond inventory and thus provides a solid basis for the evaluation of GHG and carbon pools. It is important to note that the use of Muster et al's (2017) Pond and Lake circum-Arctic database for the WSL territory is not possible. In the work by Polishchuk et al (2017), only two sites (Central Yamal and the Surgut region) were inventoried where lake coverage (15%-20%) was much higher than that of the total WSL permafrost-affected zone (5%-10%).
Acknowledgments This work was supported by the  RNF (RSCF)  grant No  15-17-10009  'Evolution of thermokarst lake ecosystems in the context of climate change' (analyses, interpretation,  50% ) and RFBR grants No. 15-45-00075  and  17-55-16008 C NRS-a. We are grateful to three anonymous reviewers for their very constructive remarks. Chris Benker  is thanked for the English editing.
Both the treebanks and the scripts used to create them are included in this paper as supplementary materials, and the treebanks are part of the Universal Dependencies 2. 2 release  (Nivre et al. , 2018). The treebanks are called Lattice and IKDP, due to the fact that most of the work on them has been carried out at the LATTICE-CNRS laboratory in Paris, and the work has been done collaboratively with the IKDP-2 1  project, which is a continuation of earlier work that produced a language documentation corpus of Komi called IKDP  (Blokland et al. , 2009 (Blokland et al. , -2018)). A comprehensive descriptive grammar of Komi with a focus on syntax is currently being written by members of the team. The present treebanks are intended to support the grammatical description. The authors' recent research at LATTICE laboratory has focused on dependency parsing of lowresource languages, using Komi and North Saami as examples  (Lim et al.
This allows the treebank sentences to be connected to rich nonlinguistic metadata. Additionally, the coded timealignment in the original utterances provides information about turn-taking and overlapping at the millisecond level. The documentary corpus refers to the materials collected and processed within the language documentation activities, which are usually fieldwork-based and aim to represent various genres and speech practices, all of which are often under a threat of disappearance. In language documentation, traditional annotation methods have mainly consisted of so-called interlinear glossing. 2 This is normally done manually or semi-manually, i. e. with little or no use of natural language processing tools (cf. Gerstenberger et al. , 2016). With the available Komi data in our project, however, we wanted to apply an annotation method that would connect our work more closely to established corpus linguistics and NLP.
Manual correction and verification The current workflow involves a rather large amount of manual work. We are interested in testing various approaches to morphological and syntactic analysis so that different (rule-based, statistic-based and hybrid) parsers can eventually replace the manual work. Some tests have already been carried out with the dependency parser used by the Lattice team in the CoNLL-U Shared Task 2017  (Lim and Poibeau, 2017 ) and a follow-up project  (Partanen et al. , 2018). The treebank processing pipeline has been tied to several scripts and existing tools. The primary analysisis done within the Giellatekno toolkit (building on FST Morphology and Constraint Grammar), where tokenization, morphological analysis and rule-based disambiguation are tied to the script 'kpvdep'.
The script returns a vislcg3 file that contains all ambiguities left after the analysis. Once the ambiguities are resolved manually, the vislcg3 file can be imported into the UD Annotatrix tool. As a final step, the Giellatekno POS-tags and morphological features are converted to follow the UD standard with a Python script, originally written by Francis Tyers 7. A modified version of the script with the conversion pattern file is stored in not-to-release folder in the dev-branch of the Lattice treebank, which is the location where all development scripts of both treebanks will be maintained.
The context of the spoken data here is therefore not only a faithful representation of the spoken signal, which could include also more exact phonetic transcriptions, but also the larger landscape of spoken language resources which we would like to integrate into our NLP ecosystem. Furthermore, because local Komi speech and research communities are often conscious of orthographic norms, we wanted to draw a clear boundary between written and spoken representations. Additionally, the spoken language treebank contains a large number of Russian phrases due to code-switching, which makes it to some degree a multilingual treebank. In the IKDP treebank, Russian items are currently marked with a language tag in the misc-field, but verification that Russian annotations are consistent with monolingual Russian treebanks is a topic that requires further attention.
In any case, based on our experience, we would strongly encourage endangered language documentation projects to take a small segment of their materials and add to it an additional layer of annotations in the Universal Dependencies framework. Language documentation data is usually stored in archives that require access requests. This is not very compatible with openly available treebanks. Still, it should be possible to collect small subsets of materials with the clear intention and permission for these recordings to be openly licensed, or to use texts old enough that they are copyright free. New material is currently being brought into the Lattice treebank. The main genres obtained from Fenno-Ugrica collection are newspaper texts, nonfiction works and schoolbooks. Samples of these, along with some larger Wikipedia texts, will be included in the next UD release 2.
Acknowledgements We want to thank the reviewers for their useful comments. This work has been developed in the framework of the  LAKME  project funded by a grant from  Paris Sciences et Lettres  ( IDEX PSL  reference  ANR-10-IDEX-0001-02 ). Thierry Poibeau is partially supported by a  RGNF-CNRS  (grant between the  LATTICE-CNRS Laboratory  and the  Russian State University  for the Humanities in Moscow). Kyungtae Lim is partially supported by the  ERA-NET   Atlantis  project. Niko Partanen 's work has been carried out at the  LAT-TICE laboratory , and besides Partanen, both  Rogier Blokland  and  Michael Rießler  collaborate within the project Language Documentation meets Language Technology: the  Next Step in the Description of Komi , funded by the  Kone Foundation. Thanks to  Jack Rueter  for numerous discussions on Komi and Erzya, and to  Alexandra Kellner  for proofreading the paper.
Finland 11. We have exclusively chosen to use openly available data for the Lattice treebank in order to ensure as broad and simple reuse as possible. The forthcoming releases will include more genres of text, such as newspaper texts and longer sections of Wikipedia articles. All sentences in the Lattice treebank are presented in the contemporary orthography, even when they were originally published using various earlier Komi writing systems. The proportion of texts originally written in the Molodcov alphabet will rise dramatically in the next releases, as this is probably the most commonly used orthography in the upcoming texts. Storing several orthographic variants may be necessary. Conversion between systems has been carried out using FU-.
Lab's Molodcov converter12. The data originates from scanned books through text recognition, currently with loss of page coordinates. This connects to the question of how to retrieve arbitrary information from different sources that can be connected to the sentence IDs: metadata, page positions, page images, time codes and audio segments. We considered it very important to also include spoken language in the treebank, ideally eventually covering all dialects. During the last years, one of the largest research projects investigating spoken Komi has been the IKDP project, led by Rogier Blokland in 20142016, which resulted in a large transcribed spoken language corpus (Blokland et al. , 2009 (Blokland et al. , -2018)). The IKDP treebank contains dialectal texts taken from this corpus, and since written Komi does not follow the exact same principles employed in the transcriptions, it seems problematic to mix these materials together.
The uncertainty of retrieved daily emissions in error free conditions is below 25. 8 % for DD, 5. 9 % for BC and 26. 9 % for OC. In addition, the BC emission retrieval is sensitive to BC refractive index, which could produce an additional factor of 1. 8 differences for total BC emissions. The approach is then applied to 1 year (December 2007 to November 2008) of data over the African and Arabian Peninsula region using PARASOL/GRASP spectral AOD and AAOD at six wavelengths (443, 490, 565, 670,  865 and 1020 nm). Analysis of the resulting retrieved emissions indicates 1. 8 times overestimation of the prior DD online mobilization and entrainment model. For total BC and OC, the retrieved emissions show a significant increase of 209. 9 %-271. 8 % in comparison to the prior carbonaceous aerosol emissions.
The model posterior simulation with retrieved emissions shows good agreement with both the AOD and AAOD PARASOL/GRASP products used in the inversion. The fidelity of the results is evaluated by comparison of posterior simulations with measurements from AERONET that are completely independent measurements and more temporally frequent than PARASOL observations. To further test the robustness of our posterior emissions constrained using PARASOL/GRASP, the posterior emissions are implemented in the GEOS-5/GOCART model and the consistency of simulated AOD and AAOD with other independent measurements (MODIS and OMI) demonstrates promise in applying this database for modelling studies.
, 2013). AAOD is another valuable product to quantify the solar absorption potential of aerosols; however, only a few satellite aerosol products can provide retrievals of AAOD, and only with limited accuracy, for example OMI (Ozone Monitoring Instrument) on the Aura satellite making measurements in the UV range that have sensitivity to aerosol absorption  (Torres et al. , 2007; Veihelmann et al. , 2007). Despite their ability to provide global coverage in high spatial resolution, satellite measurements alone are not sufficient for addressing the question regarding the distributions, magnitudes and fates of aerosols in the atmosphere. These aspects can be studied using chemical transport models (CTMs), which rely on meteorological data from external databases with atmospheric physics, considering the physical and chemical processes in the atmosphere, and allow modelling of the detailed distribution of aerosol for any chosen time period (e.
g. Drury et al. , 2010; Wang et al. , 2010). Therefore, the retrieval of aerosol emissions from satellite observations remains very challenging. Recently a new dataset of spectral AOD and AAOD was generated using the GRASP (General Retrieval of Atmosphere and Surface Properties) algorithm from POLDER/PARASOL (Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) instrument  (Dubovik et al. , 2011 (Dubovik et al. , , 2014 ; data available from ICARE data distribution portal: http://www. icare. univ-lille1. fr/, last access: 8 August 2018). Since several POLDER instruments were launched on different satellites, in the text an abbreviation of PARASOL satellite is used instead of instrument. The PARASOL/GRASP data present new opportunities for constraining DD, BC and OC sources because their optical properties vary dramatically in the spectrum of shortwave visible to near infrared (VIS-NIR) viewed by PARASOL.
GEOS-Chem model and its adjoint GEOS-Chem is a global three-dimensional CTM driven by assimilated meteorological data from the NASA Goddard Earth Observing System Data Assimilation System (GEOS-DAS)  (Bey et al. , 2001). We use the GEOS-Chem (v9-02) model for aerosol simulation with 47 vertical layers and 2 • (latitude) × 2. 5 • (longitude) horizontal resolution. DD, BC and OC aerosols are simulated in our study, including seven size bins for resolving dust  (Fairle et al. , 2007)  and the total aerosol mass of BC and OC  (Park et al. , 2003). Dust simulations in GEOS-Chem  (Fairlie et al. , 2007)  combine the mineral Dust Entrainment and Deposition (DEAD) model  (Zender et al. , 2003)  with the GOCART dust source function  (Ginoux et al. , 2001). The daily biomass burning sources are calculated from version 3 of the Global Fire Emissions Database (GFED) inventory  (van der Werf et al.
3. Here, we use level 2 AERONET version 2 data, which are cloud screened and quality assured  (Smirnov et al. , 2000). From all 1-year measurements collected from 28 sites, we extract data between 13:00 and 14:00 local time. This provides a 60 min window centred at the PARASOL over-passing time of ∼ 13:30 LT. The averaged AERONET sun-direct AOD and AAOD by inversion of almucantar measurements  (Dubovik et al. , 2000; Dubovik and King, 2000)  over this 60 min window are averaged for comparison with PARASOL/GRASP retrievals. We aggregate the PARASOL/GRASP products into 2 • latitude × 2. 5 • longitude horizontal resolution to match the spatial resolution used by GEOS-Chem; any 2 • × 2. 5 • grid box with less than 500 available PARASOL/GRASP retrievals for averaging is omitted. Depending on geographical location, the number of GRASP retrievals in a single 2 • × 2.
Owing to the limited observations available for assimilation, this assumption helps to make the retrieval sufficiently accurate and stable with a rather generic initial guess. 5. The BC emission retrieval is sensitive to BC refractive index assumption, which could produce a factor of ∼ 1. 8 difference between the two sets of commonly used BC refractive index data for total BC emissions. We will produce two BC emission datasets with two scenarios of BC refractive index, Case 1: m = 1. 75-0. 45i and Case 2: m = 1. 95-0. 79i.
Results In this section, we discuss retrieval of DD, BC and OC emissions simultaneously from the actual PARASOL/GRASP spectral AOD and AAOD data from December 2007 to November 2008. The SU and SS aerosol simulations are kept as the prior model. PARASOL/GRASP retrievals were aggregated to the same horizontal resolution as the GEOS-Chem model (2 • × 2. 5 • ) and averaged within the grid cells prior to assimilation. When iteratively minimizing Eq. (  3 ), the maximum iteration number was chosen to be 40, which takes about 60 days to complete on a computer workstation with 32 × 3. 3 GHz CPUs. Based on conducted tests, the decrease in the cost function is very minor starting from the 20th iteration (e. g. see Fig. 6 ).
Hence, compared to PARASOL/GRASP observations, the prior GEOS-Chem AOD is overestimated in northern Africa, while it is underestimated in the southern Africa biomass burning and Arabian Peninsula regions. Some recent studies by  Ridley et al. (2012 Ridley et al. ( , 2016) )  and  Zhang et al. (2015)  also indicate that the GEOS-Chem model overestimates dust AOD in northern Africa. Meanwhile,  Ridley et al. (2012)  and  Zhang et al. (2013)  propose a new and realistic dust particle size distribution according to the mea- surements from  Highwood et al. (2003) , which can partially adjust the misrepresentation of dust near the source and over transport areas. This new particle size distribution has been adopted in our prior and posterior GEOS-Chem simulation. In addition, the underestimation of model-simulated AOD in biomass burning regions with the GFED emission database was also shown in other modelling studies  (Chin et al.
Evaluation with AERONET In order to objectively evaluate our retrieved aerosol emissions based on PARASOL/GRASP spectral AOD and AAOD, we made a series of evaluations using independent datasets and models not used by our inversion. First, the posterior simulated 1-year AOD and AAOD (using Case 1 BC emissions) are compared with the sun-photometer-measured AOD and AAOD at 28 AERONET sites (shown in Fig. 1 ). Figures  17  and 18  show the comparison of GEOS-Chem simulations using prior and posterior emissions with      17a ) are 0. 62, 0. 67, 0. 66 and 0. 66 for the four wavelengths respectively, and the corresponding RMSEs are 0. 28, 0. 25, 0. 24 and 0. 24. Yet, the correlation coefficients are increased to 0. 73, 0. 75, 0. 75 and 0. 74 when the posterior emissions are used in the GEOS-Chem simulation (Fig.
Furthermore, prior GEOS-Chem simulation can capture the variation in and magnitude of AOD (R = 0. 79 and K = 0. 79) in Ilorin. However, for AAOD, the simulation shows underestimation with a slope K = 0. 40, which is an indicator of the model underestimation of the aerosol absorption species, such as BC. Overall, the posterior GEOS-Chem simulation with retrieved emissions can better capture the time serial variation in and magnitude of AOD and AAOD in both Mongu and Ilorin. To investigate this, we implement our PARASOL/GRASPbased aerosol emission database in the GEOS-5/GOCART model  (Chin et al. , 2002 (Chin et al. , , 2009 (Chin et al. , , 2014;; Colarco et al. , 2010). The prior and posterior GEOS-5/GOCART model-simulated seasonal AODs are compared with MODIS observations in Fig. 20. GEOS-5/GOCART uses similar meteorological fields as GEOS-Chem, with the prior anthropogenic emissions from the Hemispheric Transport of Atmospheric Pollution (HTAP) Phase 2, biomass burning emissions from the Fire Energetics and Emission Research (FEER) database  (Ichoku and Ellison, 2014) , dust emissions calculated as a function of 10 m winds and surface characteristics  (Ginoux et al.
20a-c. With the posterior emissions, the GEOS-5/GOCART simulation shows improvements compared with AERONET and MODIS observations, with a higher correlation coefficient and lower RMSE in all four seasons than the prior GEOS-5/GOCART simulation. The posterior GEOS-5/GOCART-simulated AOD is a little lower than MODIS on average by 13 % (normalized mean bias, NMB = -13 %, NMB= (M i -O i )/ O i , where sums are over the ensemble of all data i, and M i and O i are the modelled and observed values), likely associated with the fact that the MODIS AOD is observed at noon; however the GEOS-5/GOCART AOD is averaged over 24 h during a day. Commonly, the ultraviolet, shortwave visible channels and polarimeter measurements are considered to be main observation types sensitive to aerosol absorption properties; therefore, AERONET, PARASOL/GRASP and OMI datasets are often used as major long-term records of AAOD.
We use the latest OMI aerosol products (OMAERUV version 1. 7. 4)  (Torres et al. , 2007 (Torres et al. , , 2013) )  to evaluate the GEOS-5/GOCART model-simulated AAOD from prior aerosol emission inventories and our retrieved aerosol emission database. Meanwhile, collocated AERONET data over the study area are also employed for the evaluation. Detailed assessments of OMI aerosol products are described in other studies  (Torres et al. , 2013; Ahn et al. , 2014; Jethva et al. , 2014). Figure  21  shows the validation results. Figure  21a  presents the OMI seasonal mean AAOD with original OMAERUV version 1. 7. 4 spatial resolution 0. 5 • × 0. 5 •. We plot a gridto-grid comparison between OMI and GEOS-5/GOCART AAOD in Fig. 21d-e ; here OMI AAODs are re-scaled to the same resolution with a model simulation of 2.
, 2003; Zhang et al. , 2015). However, we cannot rule out the possibility that differences between model and observations could also be attributed to the errors in removal processes and aerosol microphysical properties, in addition to the deficiencies in emissions  (Bond et al. , 2013). Nevertheless, the fidelity of our results is confirmed by comparison of posterior simulations with measurements from AERONET that are completely independent from and more temporally frequent than PARASOL observations. Specifically, to analyse the PARASOL/GRASP-based aerosol emission database further, we implemented these emissions in the GEOS-5/GOCART model and compared the resulting simulations of AOD and AAOD with independent MODIS and OMI observations. The comparisons show better agreement between model and observations with the posterior GEOS-5/GOCART results (lower biases and higher correlation coefficients) than prior simulations.
Acknowledgements. This work is supported by the  Laboratory of Excellence CaPPA -Chemical and Physical Properties of the Atmosphere -project , which is funded by the  French National Research Agency (ANR). We would like to thank the  GEOS-Chem  and adjoint GEOS-Chem model developers;  Daven K. Henze  recognizes support from  NASA   ACMAP NNX17AF63G. We also thank the entire AERONET team, and especially the principal investigators and site managers of the 28 AERONET stations that we acquired data from. The authors are also grateful for the MODIS and OMI aerosol team ( Omar Torres  and  Hiren Jethva ) for providing the data used in this investigation and  Huisheng Bian  and  Tom Kucsera  for incorporating the PARASOL/GRASP emissions into the GEOS-5 model and provide the GEOS-5/GOCART simulation results.