dataesr/scientific-paragraphs-categorization huggingface dataset:
The Ourika River is an important tributary of the Tensift River in the water-stressed region of Marrakesh (Morocco). The Ourika river flow is dominated by the snow melt contribution from the High Atlas mountains. Despite its importance in terms of water resources, the snow water equivalent (SWE) is poorly monitored in the Ourika catchment. Here, we used MERRA-2 data to run a distributed energy-balance snowpack model (SnowModel) over 2000-2018. MERRA-2 data were downscaled to 250-m spatial resolution using a digital elevation model. The model outputs were compared to in situ measurements of snow depth, precipitation, river flow and remote sensing observations of the snow cover area from MODIS. The results indicate that the model provides an overall acceptable representation of the snow cover dynamics given the coarse resolution of the MERRA-2 forcing.
Unlike degree-day models, energy balance models do not require the calibration of the model parameters, but they require more meteorological forcing data (precipitation, air temperature, air humidity, solar shortwave radiation, atmospheric longwave radiation, wind speed, air pressure). In many mountain regions like the High Atlas, these measurements are generally not available, and there is no direct way to measure the SWE in mountains by satellite remote sensing to this date  [14] ; therefore, a solution is to use atmospheric model outputs as forcing to run a snowpack model  [15] [16] [17]. In particular, climate model reanalysis like NASA modern-era retrospective analysis for research and applications data (MERRA-2) provides an optimal reconstruction of the meteorological variables at the global scale by assimilation of available in situ and satellite observations into a climate model.
Previous studies showed that MERRA-2 can provide a realistic representation of the precipitation for snow and hydrological studies  [18]. In Africa, the MERRA-2 precipitation correction algorithm uses a specific configuration, which has been introduced to improve deficiencies in MERRA-Land  [19]. This configuration has improved the simulation of the terrestrial water storage in Africa at coarse resolution as observed by GRACE, but MERRA-2 skills were not specifically evaluated in North Africa  [18]. The objective of this study is to evaluate the combination of MERRA-2 data with a distributed snowpack model to simulate and analyze the spatio-temporal variability of the snow water equivalent (SWE) in the Ourika catchment from 1 November 2000-1 April 2018. The study period  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018)  was chosen because it corresponds to the observation period of the MODIS sensor, which we use as validation data to evaluate our SWE simulation.
We use SnowModel  [20] , a distributed energy-balance model to simulate the snowpack evolution. A key asset of SnowModel is its MicroMet (meteorological data interpolation) subroutine, which spatially interpolates the meteorological forcing over the domain using the digital elevation model. In addition, SnowModel accounts for the effect of the terrain slope and aspect on the incoming solar radiation, which can be significant in this semi-arid mountainous region. The MERRA-2 forcing was downscaled to 250-m using a digital elevation model to account for the effect of the topography on the temperature, humidity, wind, precipitation and solar radiation  [16]. Finally, we take advantage of the availability of the MERRA-2 forcing in near real time until April 2018 to analyze the snow condition during the winter 2017-2018.
Meteorological Data Despite the importance of the snow cover and its high interannual variability, there is only one automatic snow depth record at the western edge of the catchment at Oukaimeden (3200 m. a. s. l. ) nearly on the water divide with the Rheraya catchment (Figure  1 ). The snow depth was measured by using an acoustic sensor every half-hour and aggregated to the daily time step for this study. There are many gaps in the measurements, but they still enable one to evaluate the model. The available data cover six snow seasons  (2004-2005, 2005-2006, 2008-2009, 2010-2011, 2013-2014 and 2014-2015). The meteorological forcings to run SnowModel were derived from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), available at 0. 5 • × 0. 625 • of spatial resolution.
Each new month of MERRA-2 reanalysis is available approximately between the 15th and the 20th of the next month. The extracted variable are listed in Table  2. Each MERRA-2 pixel is characterized by its latitude and longitude and surface geopotential height (named as PHIS in the data file), which were converted to (x, y, z) coordinates in the WGS-84 UTM-29N projection system (see Section 2. 3).
Snow Cover Area We used 18 years of daily MODIS data at a 500-m spatial resolution to validate the simulation. These data provide the snow cover fraction (SCF) within each MODIS pixel. The cloud pixels were interpolated using the algorithm of Marchane et al. [9] , which was specifically developed for the High Atlas. The accuracy of the algorithm was evaluated by comparison to snow depth measurements and a high-resolution snow mask derived from the Formosat-2 observations over the Rheraya catchment. The results showed that the algorithm allowed a reduction of the cloud coverage to less than 1% with an overall accuracy of 89%  [9]. The MODIS data were produced from the Collection 5 MOD10A1 snow product. However, Collection 5 has been discontinued since 1 January 2017; therefore, the entire time series was reprocessed using Collection 6 MOD10A1.
Snow Energy and Mass Balance Model To simulate the SWE, the melt and the discharge in the Ourika catchment, we fed SnowModel, a distributed energy balance snowpack evolution model  [20] , with MERRA-2 reanalysis. The SnowModel has previously been tested with success in the Arctic, Antarctic and high mountain regions, comparing simulated snow accumulation, distribution and ablation processes with observations  [29] [30] [31] [32] [33] [34] [35] [36] [37] [38]. SnowModel is composed of four sub-models: MicroMet (meteorological data interpolation), EnBal (energy balance computations)  [20] , SnowPack (snow depth evolution) and SnowTran-3D (wind transport)  [39]. Given the lack of in situ meteorological observations, SnowTran-3D was not activated. We set all the SnowModel parameters to default values because we have a limited observations' dataset in the study area.
By using only default parameters, we can evaluate the accuracy of the model if it should be transposed to other catchments in the High Atlas without calibration. MicroMet is the key interface between the MERRA-2 data and the snowpack model, since it enables distributing all meteorological data that are required to run the EnBal and SnowPack sub-models over the study area at the resolution of the DEM. These variables are precipitation, air temperature, relative humidity, wind speed, incoming solar radiation and incoming longwave radiation at the hourly time step. MicroMet was originally designed to interpolate station data onto a regular grid, but it can also be applied to downscale large-scale, surface-level forcing from climate model or reanalysis such as MERRA-2  [16]. In this case, each MERRA-2 grid cell intersecting the study domain is considered as a virtual station, which is located at the center of the cell.
Model Evaluation The validation of the model outputs is challenging due to the lack of SWE measurements in Morocco and the difficulty of getting them otherwise (e. g. , the cost of light detection and ranging (LIDAR) campaigns  [41, 42]  is very high, and the use of unmanned aerial vehicles (UAV) to estimate the snow depth  [43]  is limited by its spatial coverage, on the one hand, and the complicated legal procedure to use it in Morocco, on the other). To deal with this, we have combined different hydrometeorological datasets and remote sensing observations. The simulated snow depth (SD) was extracted at the Oukaimeden station (3200 m. a. s. l. ) to assess the model performance using the root mean square error (RMSE). We also evaluate the cumulative precipitation over the snow season (from 1 December-1 April from 2000-2009 by using in situ precipitation).
Ourika River Discharge Figures  8  and 9  show the comparison between the simulated runoff and the observed discharge at the outlet of the Ourika catchment. The mean R 2 between the modeled runoff and the observed discharge at the monthly time step from 2000-2017 is 0. 55; 10 of 15 years have a R 2 value higher than 0. 5 (Figure  8 ). However, there remain large discrepancies between both datasets, in particular the low R 2 in  2000-2001, 2001-2002, 2003-2004 and 2004-2005  are consistent with the lower R 2 , which were obtained for this year for the SCA. However, this is not always the case, as for instance, a low R 2 in discharge was obtained in 2012-2013 despite the correct simulation of the SCA for this hydrological year (Figure  6 ). Figure  9  suggests that at the finer 10-day time step, the model can capture peak discharges; however, the discharge regime is generally poorly represented except in 2012 and 2015.
Summary and Conclusions We showed the application of a distributed snowpack model in the snow-dominated Ourika catchment in the High Atlas. We focused on the Ourika catchment because there is a lack of knowledge regarding the snow dynamics in this catchment although snow melt is an essential water resource to sustain irrigation in the semi-arid Haouz plain. To our best knowledge, this study is the first to evaluate and analyze a spatially-distributed simulation of the SWE in the High Atlas mountains for a period covering almost the last two decades. To cope with the lack of weather station measurements in the High Atlas, we used the MERRA-2 reanalysis and the MicroMet model to generate a distributed meteorological dataset, taking into account the main effects of topography on the meteorological variables.
The model outputs, however, are still subject to large uncertainties. These results should be taken with caution since we could not use direct SWE measurements to validate the model, but only indirect observations of the snow cover dynamics. We suggest to conduct SWE measurement campaigns in the following years to better validate the SWE simulation over the catchment. Further work is required to evaluate the simulated SWE against discharge measurements at finer temporal timescales. The biases in the MERRA-2 forcing could also be reduced by assimilating snow cover maps retrieved from remote sensing data  [49, 50]. Satellite-derived snow cover maps are usually available with a shorter delay than MERRA-2 (less than a week). Therefore, it should be possible to develop a near-real-time data assimilation scheme to produce more accurate SWE maps.
This is the focus of our ongoing work. Yet, we think that the simple approach shown here could already be useful for water managers. The availability of the MERRA-2 forcing with a short delay (about one month) now enables producing interesting diagnostics regarding the state of the snowpack, over the course of the snow season, without ground measurements. These data could help assess the snow water resource in the Ourika catchment, and in other catchments in the High Atlas, to optimize the planning of the irrigation during the spring.
Acknowledgments: M. W. B. acknowledges the  CNES and the Région Occitanie  for funding his PhD grant. S. G. acknowledges support by the  CNES  through the  Tosca program. We thank the  Agence du Bassin Hydraulique du Tensift and Laboratoire Mixte International (LMI TREMA)  for providing the discharge and precipitation data. The MERRA-2 data used in this study have been provided by the  Global Modeling and Assimilation Office (GMAO)  at  NASA Goddard Space Flight Center.
, 2014). EUPORIAS focussed specifically on the climate predictions timescale but whilst the project was open to consider services based on decadal prediction all the prototypes proposed were based on seasonal prediction data. Both hindcast and forecast data used by the prototypes originated from the Global Producing Centres for Long-Range Forecasts (GPCLRFs) of the World Meteorological Organisation operating in Europe namely Météo France, Met Office and ECMWF. The ambition was to identify and develop only those services that had a tangible user drive and which were also most likely to provide valuable results. Having a user component in the definition of the issue to be addressed was believed to be an effective way to promote a sustained dialogue between users and providers which can itself contribute to the creation of legitimacy and trust  (Lemos and Morehouse, 2005; Vaughan and Dessai, 2014).
But this becomes utterly impossible in the absence of trust between the parties involved in the development of the service. Trust building has been recognised to be essential to the development of successful services  (Brooks, 2013). Confidentiality was therefore of paramount concern for many stakeholders in the project. Within SPRINT some wanted their operational data to be keep confidential, while for others it was about not openly publishing the actual forecast for their impact before they got a chance to analyse it and make their decisions based on that forecast. All organisations are interested in managing reputational risks as well as financial risks; additionally, some stakeholders in the project may be competing with other similar organisations. Conversely, in line with EC regulation, the ethos of EUPORIAS is to publish and present its findings openly; hence the EUPORIAS deliverables required some flexibility in presenting project outcomes while still respecting confidentiality.
Having someone who understands the issue, is committed to the project and has time to invest to ensure the project reaches its objectives is an asset for the service whose value is difficult to overestimate. A major challenge for the prototypes has been to use limited end-user data to derive useful, robust information to the users. This was particularly evident in the case of SPRINT but to a lesser extent was true also for the other prototypes. In fact the length of the impact data record is crucial for the assessment of the role of natural climate variability while also providing a way to increase the robustness of the statistical analysis. For climate services to thrive a renewed effort toward the collection the storage and the maintenance of these essential datasets would be needed. The prototypes represented in this paper show how much user interaction can alter the scope, the setup and ultimately the outcomes of a climate service proposition.
Acknowledgements The authors wish to acknowledge all of those who contributed indirectly to the development of the EUPORIAS prototypes, through scientific discussion, review, data provision, stakeholder engagement and facilitation: for SPRINT,  Adam Scaife ,  Anca Brookshaw ,  Alberto Arribas ,  Emily Wallace ,  Jeff Knight ,  Margaret Gordon ,  Kate Brown ,  Brent Walker ,  Mathew Richardson ,  Jodie Wild , and the DfTled stakeholder group; for  RESILIENCE ,  Melanie Davis  for conceiving the prototype and for her vision on Climate Services. In the LMTool,  Clinton Devon Estates , the  National Farmers Union , and all the farmers involved in the development of the tool. The  UK Government Department for Transport  is acknowledged for providing financial support, in parallel to that received from EUPORIAS, for the SPRINT prototype.
Entity Extraction Relation Taxonomy induction involves three sub-tasks: entity extraction, relation prediction, and taxonomic organization. In many cases these subtasks are undertaken sequentially to build a taxonomy from the ground up. While many works directly compare methods for relation prediction (e. g. Turney and Mohammad (2015) ,  Shwartz et al. (2017)  and others), none directly compare methods for the final taxonomic organization step with varying constraints. Each paper that proposes a taxonomic organization method starts with its own set of predicted relations, making it impossible to determine -even with benchmark datasets -the extent to which improvements in identifying ground-truth relations are due to (a) better relation prediction, or (b) better taxonomic organization. In this work, we present an empirical applesto-apples comparison of six algorithms for unsupervised taxonomic organization.
Entity Extraction We extract sets of entities from the Paraphrase Database (PPDB)  (Ganitkevitch et al. , 2013; Pavlick et al. , 2015). Our goal is to construct local taxonomies, where each entity set E consists of terms sharing a common target paraphrase. For example, a local taxonomy centered around the target coach might contain entities bus, vehicle, trainer, person, car, and railcar. The local taxonomy for a target word does not contain the target word itself. We build a dataset for constructing local taxonomies centered around 50 target nouns drawn from the 2010 SemEval word sense induction dataset  (Manandhar et al. , 2010). For each target noun, we extract as taxonomy terms the set of PPDB paraphrases having a PPDB2. 0SCORE of at least 2. 0 with the target. 3 The number of entities in each local taxonomy ranges from 13 to 126, with a median of 40 entities per set.
Hypernym Prediction For hypernym prediction, we adopt the state-ofthe-art HypeNET method of  Shwartz et al. (2016). HypeNET integrates distributional  (Lin and Pantel, 2002; Roller et al. , 2014; Levy et al. , 2015; Benotto, 2015)  and path-based  (Hearst, 1992; Snow et al. , 2004; Nakashole et al. , 2012)  ap-proaches to hypernym prediction. It uses a recurrent neural network to represent the set of observed dependency paths connecting an input word pair, and concatenates this representation with distributional word embeddings to produce a set of features for predicting hypernymy. We create a dataset of noun pairs for training and evaluating the HypeNET model. It combines noun pairs from four benchmark relation prediction datasets (BLESS  (Baroni and Lenci, 2011) , ROOT09  (Santus et al. , 2016) , EVALution  (Santus et al.
, 2015) , and K&H+N  (Necsulescu et al. , 2015) ) with a set of related and unrelated noun pairs extracted from PPDB. Since each of these is a multi-class dataset, we binarize the data by labeling noun pairs with a hypernym relation as positive instances, and all others as negative. The combined benchmark+PPDB training set contains 76,152 noun pairs with a 1:4 hypernym:nonhypernym ratio, and the evaluation set contains 29,051 pairs. We ensure lexical separation from our taxonomy induction dataset; no terms in the classifier training set appear in any of the local taxonomies. We train HypeNET using our 76K-pair test set, and provide the results of evaluation on the 29K-pair test set in Table  1. The trained model achieves an overall average F1-score of 0. 93 on the entire benchmark+PPDB test set.
The full details of our dataset creation and classifier training are provided in the supplementary material. Finally, we use the trained model to predict hypernym likelihoods for each potential edge r ij in one of our local taxonomies, corresponding to an ordered pair of terms (e i , e j ) that appear together in one of the 50 entity sets. We assign a hypernym score s h (r ij ) to each potential directed edge that equals the HypeNET predicted likelihood for that pair of terms.
Results are reported in Table  3. We compare the results of the six algorithms to two types of baselines. As a lower bound, we implement a random baseline where edges are selected randomly with likelihood tuned on the benchmark+PPDB training set. As an upper bound, we run 'oracle' versions of MAXTRANSFOREST and DMST where we set the score of any edge appearing in WordNet to 1. The transitive, tree-constrained MAXTRANS-FOREST algorithm achieves the best average combined F-score (0. 21) over all the local taxonomies, followed closely by the non-transitive, tree-constrained clustering method DMST+CLUS (0. 20). These two methods, which are the only two tree-constrained methods that incorporate synonymy, outperform all DAG-constrained methods on this dataset. While they perform similarly in terms of combined F-score, their results are complementary; MAXTRANSFOREST obtains a relatively high score on hypernym edges and lower score for synonym edges, while for DMST+CLUS the results are reversed.
Conclusion In this paper we have conducted a direct comparison of six taxonomy organization algorithms that vary in terms of their transitivity and graph structure constraints, and their treatment of synonyms. Evaluating their performance over a dataset of local taxonomies drawn from PPDB, we find that transitive algorithms generally out-perform their non-transitive counterparts. While the bestperforming algorithm -an ILP approach that constrains graphs to be transitive and tree-structuredis too slow to use on large inputs, a much simpler maximum spanning tree algorithm that consolidates synonyms into a single taxonomic node has complementary performance, with a small fraction of the runtime. Our results suggest that incorporating synonym detection into tree-constrained taxonomy organization algorithms is a promising area for future research.
Acknowledgments This material is based in part on research sponsored by  DARPA  under grant number  FA8750-13-2-0017  (the  DEFT program ) and  HR0011-15-C-0115  (the  LORELEI program ). The U. S. Government is authorized to reproduce and distribute reprints for Governmental purposes. The views and conclusions contained in this publication are those of the authors and should not be interpreted as representing official policies or endorsements of  DARPA  and the  U. S. Government. The work has also been supported by the  French National Research Agency  under project  ANR-16-CE33-0013 , and by the  National Physical Science Consortium. We are grateful to our anonymous reviewers for their thoughtful and constructive comments.
Most if not all previous work, however, focus on either measuring the semantic relatedness between nodes  (Wan and Xiao, 2008; Tsatsaronis et al. , 2010)  or devising node ranking functions  (Tixier et al. , 2016; Florescu and Caragea, 2017). So far, little attention has been paid to the use of different types of graphs. Yet, a key challenge in keyphrase extraction is to ensure topical coverage and diversity, which are not naturally handled by graph-of-words representations  (Hasan and Ng, 2014). Most attempts at using topic information in graph-based approaches involve biasing the ranking function towards topic distributions  (Liu et al. , 2010; Zhao et al. , 2011; Zhang et al. , 2013). Unfortunately, these models suffer from several limitations: they aggregate multiple topic-biased rankings which makes their time complexity prohibitive for long documents 1  , they require a large dataset to estimate word-topic distributions that is not always available or easy to obtain, and they assume that topics are independent of one another, making it hard to ensure topic diversity.
Datasets and evaluation measures We carry out our experiments on three datasets: SemEval-2010 Hulth-2003 Marujo-2012 Model F 1 @5 F 1 @10 MAP F 1 @5 F 1 @10 MAP F 1 @5 F 1 @10 MAP  (Bougouin et al. , 2013)  9   SemEval-2010 (Kim et al. , 2010) , which is composed of scientific articles collected from the ACM Digital Library. We use the set of combined author-and reader-assigned keyphrases as reference keyphrases. Hulth  -2003 - (Hulth, 2003)) , which is made of paper abstracts about computer science and information technology. Reference keyphrases were assigned by professional indexers. Marujo-2012  (Marujo et al. , 2012)  that contains news articles distributed over 10 categories (e. g. Politics, Sports). Reference keyphrases were assigned by readers via crowdsourcing. We follow the common practice and evaluate the performance of our model in terms of fmeasure (F 1 ) at the top N keyphrases, and apply stemming to reduce the number of mismatches.