dataesr/scientific-paragraphs-categorization huggingface dataset:
These dimensions are described briefly below. 1) Personal Data Dimension: this refers to the set of user's attributes which frame the user identity. This dimension can be detailed to examine various personal and demographic data although, in our context, we are more interested in a few attributes that will help to build the model. In particular, we focus primarily on information such as the developer's age, location, job title, years of experience, social media user ids, etc. 2) Domain of Interest Dimension: this dimension is included so as to provide specific insights into users' domains of interest. Relevant attributes will be determined using an explicit approach (i. e. online filling-up forms) and/or an implicit approach (social networks content). Items in this dimension include information about the development domain in which the developer specialises (health, business, education, misc.
, etc. ). Also captured data pertaining to the developer's preferred app development methods (Native, Hybrid and Cross Mobile App). This dimension also includes software development methodologies (Waterfall, SCRUM, Spiral, Extreme, etc. ) and software repository hosting services which are those online facilities that provide file archiving services to the affiliated members, enabling developers to store and manage their source codes online (GitHub, Buddy, AzureDevOps, etc. ). The developer's domain of interest also comprises other items such as preferred programming languages, preferred IDEs, etc. 3) Software Project Dimension: the purpose of having this dimension is to gather any available information about the user's current software development project. This entails a set of procedures describing the software project which includes: functional and non-functional requirements, IDEs, modelling types (domain, design, etc.
Also, these frameworks should be able to regularly collect, store and filter solutions, ideas and thoughts acquired from ubiquitous knowledge bases and Q&A repositories. This paper proposes a new recommender system framework comprising a robust set of techniques designed to provide mobile app developers with a distinctive platform to browse and search for personalised artifacts. The proposed system makes use of ontology and semantic web technology as well as machine learning techniques. The following components constitute the proposed new framework: (1) Domain knowledge inference module -This module embodies various semantic web technologies and lightweight ontologies. The main objective of this module is twofold: to build the domain ontology for mobile app development, and to use the incorporated lightweight ontologies to enrich this domain ontology with specific semantic conceptual representations of entities obtained from the collected datasets.
Trajectories bridge the gap between natural scientists and social scientists and make some results more readily available to our partners and to the public  [50]. When possible, the usage of numerical models leads to quantitative results that help to define trajectories. A few examples are provided here. Their time window exceeds the 50 years of environmental monitoring in France and extends to two centuries and even more, owing to particularly abundant archives and historical data for the Seine River basin. Within the PIREN-Seine, the ways of studying the trajectories of the Seine basin have evolved over time. The first representations were spatial: a catchment area in which a human dimension was introduced to reconstruct water and material flows within the whole catchment (e. g. metals) or to model the biogeochemical cycles of selected major elements (e.
We have been able to reconstruct the nitrogen circulation and the related water quality, going as far as back in the Middle Ages, based on quantitative economic archives from ecclesiastic records  [62]. 2. A cartographic database on the river course and its corridor showing its evolution since the end of the eighteenth century has been set up, thanks to the archives of Ponts et Chaussées engineers  [48]. 3. We benefit from historical data (e. g. the nutrient chemistry and microbiological analyses; river hydrological records), which started in the second half of the nineteenth centurythe chemical and bacteriological analyses of Seine water are one of the earliest ever made and have been regularly surveyed since the late nineteenth century  [63]. 4. We have also used sediment archives to reconstruct the river contamination by numerous and diverse legacy pollutants (heavy metals, PCBs, etc.
All these models are mechanistic in nature and evolved towards the next generation for Riverstrahler and MODCOU, now called pyNuts-Riverstrahler  [76]  and CaWaQS  [32] , respectively. They have been continuously improved, by integrating new processes, new interactions with the basin society, and refining the space or time resolution. These models can be considered as general tools that encapsulate most of the knowledge gained by the programme at a given stage (see  [50] ). In fact, this representation conceals highly specialized scientific work: There is a great variety and specificity to the research topics published in scientific journals, such as surface water and groundwater motion, behaviour and transfer of microbial and chemical contaminants across the river continuum, denitrification processes in riparian zones, greenhouse gas emissions, analytical improvements for trace contaminant analysis, ecotoxicology or microbiological activity in field-based and laboratory experiments, databases construction, water use and treatment history, evolution of environmental concerns, changes in agricultural practices, etc.
The historical maps  [48]  enable a description of geomorphological modifications in the river course since the eighteenth century, which can be very slow (as with most natural processes) or stepwise (channelization, damming). All these areas of research are presented here as belonging to the natural sciences (e. g. hydrology, environmental chemistry, biogeochemistry, etc. ), the social sciences (history, geography, economics), or their combinations (agronomy, social experiments based on river models, scenarios of basin evolution, etc. ). These distinctions are now less and less marked, in particular with regard to the scenarios run with the models and the analysis of socioecological trajectories. The field, laboratory, and archive research activities are jointly analysed and processed with many regulatory databases and geographic information system (GIS) representations, thereby allowing a description of the entire basin and its evolution.
Many of these databases were not set up for environmental studies, but for economic purposes, and, apart from the river discharge measurements, none of them is generated with hydrological boundaries, but rather at administrative levels  [52, 53]. All are established at various space and time resolutions, from the dailysubdaily scale and 10 km 2 for the hydro-meteorological data (Fig. 10 , database A) to the yearly statistics of the national economy (database D). These databases are a precious asset of the Seine River basin, with some of them starting at the beginning of the nineteenth century. Despite the diversity of the spatial and temporal scales shown here, the joint work carried out over 30 years has one common goal: to show the evolution of the Seine basin and its possible future, given the construction of the past trajectories and possible future scenarios that are presented in the following chapters.
Box 2 ArchiSeine: An Open-Access Cartographic Database on River History In 2013 the PIREN-Seine program launched a joint project with the French National archives to identify, restore and digitalise historical maps, charts and drawings related to the Seine River and its major tributaries. From 1,000 documents already identified, covering a period from the end of the eighteenth century to the 1930s, and chartered by the state's Pont et Chaussées corps of engineers, one-third have already been geo-referenced and presented on the ArchiSeine website  [37, 38]. Each document is supplemented by metadata providing its historical context (authors, institution, objectives, etc. ). These charts and drawings are mainly (1) local maps related to planned and/or completed river works and (2) large-scale maps or drawings related to complaints and other judiciary acts concerning river works.
These documents (continued)  38  L. Lestel et al. Box 2 (continued) have been complemented in the ArchiSeine data set by several maps directly related to the river course, such as the wood rafting map of the Seine River basin and charts published for commercial and recreational navigation. Aside from ArchiSeine, three old general cartographic sets of France, now digitalised (IGN, Geoportail, https://www. geoportail. gouv. fr/. Accessed 15 May 2019), can be used for the quantitative analysis of the fluvial system and its evolution, from 1830 to 2015: the Etat-Major Map (1830-1860), the 1900 Map and the 1950 Map (Fig. 3 ). The Seine River basin is represented by dozens of sheets in each of these maps. The second push for cartography was given by the creation of the Ponts et Chaussées corps of engineers, who were responsible for drawing up these maps.
Implementation of the Seine Basin Model The surface layer of the model  [45]  covers an area of 81,200 km 2 including the entire 76,300-km 2 Seine basin. The grid is divided into elementary watersheds of an average area of 11 km 2 , on which the surface water balance is calculated. The river network implemented in the model is directly provided by the Carthage national database 1. In this application, both discharges and stream-aquifer exchange calculations are restricted to the main river network (Fig. 6 ) describing 4,520 km of rivers from Strahler orders 3-8  [83]  at the mouth of the Seine River. While the first implementation of the model described three aquifer units  [33, 39] , the aquifer system was refined up to six aquifer units by Pryet et al. [35]. The current version, developed by Labarthe  [45] , discretises the multilayered structure in seven aquifer units, including alluvial plains, using a progressive multi-scale grid of nested square meshes, with cells varying from 200 m to 3,200 m in size.
To analyse the Seine hydrosystem trajectory in the future, we therefore decided to pay careful attention to the ability of the method to reproduce the current state of the system, as recommended by Radanovics et al. [111]. Projection data from four GCMs and one regional climate model from the Fifth Coupled Model Intercomparison Project (CMIP5)  [5]  were therefore disaggregated at the scale of SAFRAN grid and made available for the 1850-2100 period. Data from the following models were analysed: CanESM2 (Canada), MIROC5 (Japan), BCC-CSM-1-1-m (China), CSIRO-Mk3-6-0 (Australia) and Aladin-Climat (France). As shown in Sect. 1. 2, the hydrology of the Seine is responsive to long periods of climatic fluctuations due to large-scale climatic phenomena, such as the NAO. Consequently, it is important to establish the presence and consistency of such fluctuations in the climatic product that is used for hydrological simulations, especially given that low-frequency fluctuations are difficult to simulate  [112].
Water Resources Trajectory from the 1900s to the 2100s How the Seine basin evolved from the 1900s to the 2100s was evaluated. For each case evaluated, the variations in fluxes are expressed according to a reference simulation under the assumption that the bias on climate forcings is stationary between the simulated and the reference periods. The reference simulations are either (1) the average state of the system previously simulated (2000s, Fig. 5 ) for the 1900s scenario 4  or (2) the average simulated state using MIROC5 climate data over the same period, for the 2100s scenario.
Conclusion Like every other river basin in the world, the Seine River basin faces global changes, either anthropogenic or climatic. The long-term environmental research programme, PIREN-Seine, allowed us to perform research on the Seine hydrosystem sustainability. First, the analysis of long-term discharge data over 130 years at Paris displays the past trends, meaning long-term control of the discharge by climate and also the development of large water reservoirs that store 840 Mm 3 of water during winter and its release for sustaining low-flow discharge over 60 m 3 s À1 at Paris. The latter has significantly transformed the hydrological regime of the Seine River by reducing the variability of monthly discharges since its implementation in the last quarter of the twentieth century. In the last few decades, a distributed physically based coupled model of surface and subsurface flows, CaWaQS, was also developed and progressively refined in terms of aquifer units and the river network description, as well as processes such as river-aquifer exchanges.
For the period since 1970, this description was compared with the results of an agronomic reconstitution of the cropping systems of the Seine watershed based on agricultural census and detailed enquiries about farming practices at the scale of small agricultural regions (the ARSeine database), which were then used as input to an agronomical model (STICS) calculating yields, and the dynamics of N and C. STICS was then coupled with a hydrogeological model (MODCOU), so that the entire modelling chain can thus highlight the high temporal inertia of both soil organic matter pool and aquifers. GRAFS and ARSeine revealed that the agriculture of the North of France is currently characterised by a high degree of territorial openness, specialisation and disconnection between crop and livestock farming, food consumption and production.
Material and Methods This chapter is mainly based on the results of two complementary integrated research efforts developed in the PIREN-Seine programme (www. piren-seine. fr), namely, the GRAFS-Riverstrahler and the ARSeine-STICS-MODCOU approaches, which are here compared and merged for the very first time. These two approaches differ in their level of detail, time and space resolution and the duration of the historical period they are able to encompass. GRAFS (for Generalized Representation of the Agro-Food System) is a biogeochemical accounting tool for describing the N, P and C fluxes across the crop-and grassland, livestock and human population of a given territory  [4]. It is conceived as a framework for analysing the functioning of agricultural systems, their requirements in terms of resources and their environmental losses, as well as their longterm trajectories, since 1850  [5] , based on data mostly derived from the compilation  of official agricultural statistics available at the département scale (typically 6,000 km 2 ).
It provides the required data for running the Riverstrahler model  [6, 7]  (www. fire. upmc. fr/rive), which calculates the nutrient transfers and the ecological functioning of each tributary of the river system, given the diffuse and point sources of nutrient and organic matter from the watershed. The calculated nutrient fluxes at the outlet of the river system can then be used by a coastal marine model such as ECO-MARS 3D to assess the eutrophication generated by these fluxes  [8] [9] [10] [11]. The ARSeine database  [12]  offers a spatially detailed and distributed description of the Seine-Normandie cropping systems over the 1970-2015 period, including land use, crop rotations and detailed management techniques at the Petites Régions Agricoles scale (typically 1,000 km 2 ). It has been designed to provide the inputs to a 2D-distributed version  [13]  of the STICS model  [14] [15] [16] [17] [18].
STICS is an agronomical crop model simulating crop production and the components of the N cycle at the same space and time resolution. Input soil parameters have been defined for each soil unit of the Soil Geographic Database of France at the 1:1,000,000 scale  [19] , using local pedotransfer functions  [20]. Daily values of nitrate leaching predicted by STICS are used as an input to the hydrogeological MODCOU model  [21] , which calculates the recharge and nitrate contamination of the basin's main aquifer formations  [13, 22]. Evaluating the uncertainty on the results of such long-term reconstruction of environmental data is a critical task. As far as modelling approaches are concerned, two types of uncertainty can be distinguished: structural uncertainties related to the adequacy of the model's representation of the system and operational uncertainties related to the accuracy in the data and parameters used  [23].
Changes in Land Use and Crop Rotations At a finer scale, the changes in land use and agricultural practices have been documented since the 1970s (the ARSeine database  [12] ). The specialisation in a stockless cropping system in the centre of the watershed went together with a strong reduction of permanent grassland surfaces (Fig. 4a ), which are now restricted to the Eastern and Western fringes of the basin. A significant reduction of the length and diversity of arable crop rotations has also occurred during the same period. Grain and forage legumes, which were basic components of crop rotations in the middle of the twentieth-century agriculture, were abandoned in many places (Fig. 4a ). A sharp drop in the frequency of spring crops (Fig. 4b ), such as spring barley and grain maize, is also observed, while rapeseed has gained ground.
After 1980, owing to improvements in agronomic practices, a shift occurred towards another trajectory with higher yields, in spite of lower fertilisation rates in the most recent period. The new trajectory is coherent with the yield-fertilisation relationship observed, although with considerable variability, for individual crop rotation systems, in both conventional and organic farming systems (Fig. 5b ). It is remarkable that no significant difference in the yield-fertilisation relationship, expressed in total protein production over the whole crop rotation, is apparent between organic and conventional systems of the same pedoclimatic contexts, contrary to the common opinion that organic systems would be intrinsically less productive. Fig. 5  (a) Long-term trajectory of cropland yield and soil N inputs averaged over the Seine watershed, as revealed by the GRAFS analysis for 1852-2014 (circles)  [5]  and by the application of the STICS model to the crop rotation and technical itinerary (diamonds)  [13, 22]  reconstituted in the ARSeine database.
CH 4 emissions from ruminants and monogastrics were estimated using livestock numbers and time-dependent emissions factors. CO 2 emissions were calculated following the ClimAgri methodology  [36]. While CH 4 emissions did not change much over the period under study in the Seine basin, owing to the gradual reduction of Fig. 6  (a) Long-term trends of humified organic carbon inputs to cropland soil of the Seine basin. (b) Calculated evolution of the labile and inert C stock in the 30-cm top soil of cropland. (c) Rate of C sequestration in kgC/ha/year and (d) in ‰ year À1 of the total stock in the 30-cm top soil of cropland (data from  [30] ). (e) Distribution of SON storage per agricultural district simulated by STICS in arable land from 1971 to 2013  [20]  The Seine Watershed Water-Agro-Food System: Long-Term Trajectories of C.
The Orgeval Case For the Orgeval basin, other data were available to refine the spatial accuracy of the annual rotation. First, rotation tables were provided directly by farmers, describing their crop succession on their plots (771 plots accounting for 45% of the landscape area, dating from 1970 to 2005). Moreover, data from the French Land Parcel Identification System (LPIS) geographic database  [25]  has been available since 2006. Here, we chose to collect records of their past practices directly from farmers located in the study area. These documents, called "lowland logbooks" or "field logs", help to get closer to farmers' "real" practices and to reconstruct the dynamics of phytosanitary practices over 20 years for a representative sample  [26]. As an example, the chart in Fig. 2  shows that atrazine was long the main AI applied on maize until its ban in 2003.
It therefore requires three kinds of input: (1) climate data from the SAFRAN atmospheric analysis system  [55, 56] , (2) soil data at the 1:1000000 scale  [57]  for which parameters are estimated through the use of pedo-transfer functions  [58] [59] [60] [61]  and (3) crop rotation and management  [29, 62, 63]  for which pesticide use was computed with crop rotation in the ARSEINE database  [21]  over a 45-year period  in Fig. 6. MODCOU  [64, 65 ] is a regional spatially distributed model which describes both surface and underground water flows at a daily time step. Mainly conditioned by land use, climate and parent soil material, surface water balance calculations are based on a conceptual reservoir-based approach  [66]. Infiltration leaks through a vadose zone were implemented using a set of reservoirs introducing a delay between the infiltration and the aquifer recharge  [67, 68].
Material and Methods This study was based on the integrative approach previously developed to investigate the heavy metal contamination within the Seine River basin  [1, 2]. To study the fate of PAHs in this basin, contamination data in various environmental compartments were gathered from previous studies, and the mass balance was established at two nested scales. The calculations of individual fluxes are detailed below. To evaluate the robustness of the mass balance calculation, each flux was associated with both an uncertainty and a grade assessing the quality of the database used to estimate such fluxes.
Areas Studied The PAH fluxes were assessed at two complementary scales: the Paris conurbation and the rural areas (Fig. 1 ). All parameters concerning the Paris conurbation sewer system were provided by the SIAAP (Paris conurbation wastewater treatment authority). Hydrological data and information about the dredging operations were provided by the Seine-Normandie Water Agency (AESN), the French department of waterways (VNF) or the Île-de-France Regional Department for Equipment and Planning (DRIEAIF). The meteorological parameters came from either the studies cited herein or Météo-France. Land use was determined using ArcMap (ver10. 5) with the Corine Land Cover 2012 database. Information about the road network was obtained from the Route500 ® database published by the French National Geographic Institute (IGN).
Dual-Scale Mass Balance Approach To quantify the fluxes at the investigated area scale, a two-step method was applied. First, all urban fluxes were estimated for the Paris conurbation that constitutes a well-defined and densely urbanised area located in the downstream sector of the investigated catchment. Thus, the urban fluxes at the basin scale were assessed either (1) using the same calculation method as for the Paris conurbation but with an adapted database including all the data available for the investigated area or (2) multiplying the flux estimated for the Paris conurbation by 1. 4 considering the change in the population from 10 (Paris conurbation) to 14 million inhabitants (the entire investigated area). This dual approach was chosen as it reduced the number of complex retroactions, thereby facilitating the consideration of a high level of detail in the flux charts.
Data Selection and Exploration The PAH content databases used to compute the flux estimations were gathered from about 40 previous studies investigating PAH contamination across the Seine River basin. Whenever possible, only data collected within the investigated area (Fig. 1 ) were considered. Otherwise, databases from other studies in France or Western Europe were used, therefore weakening the estimation of the quality grade (see below). A specific effort was made to avoid data duplication by crosschecking the sampling sites and dates in the various studies. When only summarised data were available, data were reconstructed assuming a log-normal distribution. When data for some compounds were lacking, fluxes were quantified for the sum of the remaining compounds, and the missing compounds are specified in the text.