dataesr/scientific-paragraphs-categorization huggingface dataset:
Benchmarks With regard to automatically generated SPARQL queries, we are aware that, in some cases, there are multiple possible paths, each of which can be semantically valid depending on the query semantics. Our system identifies the "best" shortest-path with conditions favouring binary table associations and combines the paths corresponding to inheritance, aggregation and composition. However, a manual validation test for the automatically generated SWS is still recommended. Indeed, the SWS that we generated and tested were all validated by the database managers and/or users. Regardless of the validation ability, the main benefit of our platform is that it enables the rapid creation of new and easily detectable SWS.
SPARQL query generation We have tested the speed of SPARQL query generation with two different biological databases: (i) TropGene, a relational database that contains 90 tables and 15 million records; and (ii) OryGenesDB, which contains 11 tables and 22 million records. Although SPARQL query generation is only performed during the first step of Web SWS generation and not during the SWS execution, we also measured the time required for this step. This time depends on the database schema but also strongly depends on the presence of inheritance relationships (Table  1 ). In this table, when inheritance relationships are present, we include the lengths of the paths to be aggregated. The creation of a query without inheritance relationships takes less than 2 seconds. However, creating a query using the same database schema with 4 inheritance relationships takes 15 seconds.
SPARQL query execution The time required for query execution varies significantly for different databases and strongly depends on the number of records to be retrieved. Table  2  compares the time required for SQL query execution and SPARQL query execution. We did not compare with the time required for the querying RDF dump of a relational database because some databases can contain more than 100 million tuples. The RDF dump will then contain more than 100 million triples, and triplestore query performances decrease with the number of triples. When the triplestore contains more than 100 million triples, SPARQL to SQL approaches are fastest  [41]. The time required for SQL query execution was measured in Eclipse using the java. sql library. The time required for SPARQL query execution was measured using the AJAX-based SPARQL Explorer tool of the D2R Server.
Validation of the SPARQL query results We compared the data retrieval resulting from the three approaches (i. e. , the Dijkstra algorithm, BioSemantic and a human SQL query builder) (Table  4 ). We refer to a human SQL query as a query that is manually written by an expert with good knowledge of the database schema. A first general observation demonstrates that the number of results is identical for BioSemantic queries and the manual SQL queries. BioSemantic globally retrieves more results than the Dijkstra algorithm. The gap for Query1 is explained because of the inheritance relationships missed by the Dijkstra algorithm. Indeed, in that case, BioSemantic detects these relationships and regroups the subdivided paths into the final query. Furthermore, BioSemantic preferentially selects binary association tables that promote more data retrieval.
Comparison with other SWS platforms We compared BioSemantic with other SWS platforms, such as BioMoby  [20] , SADI  [19]  and SSWAP  [18]  (Table  5 ). BioMoby adds semantic components to WSs by using an XML datatype ontology developed by WS developers. SSWAP is based on a five-class ontology allowing the definition of Web resources, inputs and outputs of the SWS, data structures and data providers. SADI is a set of fully standard-compliant SWS design patterns that simplify their publication. A SADI plugin has been developed. This plugin helps users to discover SADI SWS and to automatically compose them in workflows. In this comparison, we focused on the ability to create and use SWS because the other SWS approaches are not placed in the context of the automated creation of wrappers for relational databases.
We compared seven criteria: i) the exclusive use of SW standards; ii) the types of input and output annotation for SWS; iii) the compliance with SOAP/WSDL; iv) the constraint for clients to be platform specific; v) the ability of the platform to perform reasoning; vi) the degree of automation in the creation and deployment of SWS; and vii) the degree of automation of the query building. All of the compared approaches use SW standards except for BioMoby, in which semantics come from the data type stored in an XML tree. In terms of output, SADI and SSWAP are based on OWL, and both developed their own SWS API to exploit OWL's reasoning capabilities. BioSemantic uses the standard SAWSDL to semantically annotate the WSDL files. BioMoby, SADI and BioSemantic are compliant with SOAP/WSDL protocols.
Some of the approaches are platform specific (i. e. , SSWAP and BioMoby), meaning that they require their own environment to process SWS. For example, SSWAP gains in speed and lightness but loses in genericity. BioMoby develops its own data type definition, allowing for an easy choreography of services, but requires clients to be compliant with the API. BioSemantic and SADI use standard clients to call their SWS. In terms of reasoning abilities, SADI and SSWAP exploit OWL with semantic reasoners to highlight some relationships between classes. On the other hand, BioMoby exploits the taxonomic properties of XML to infer relationships between data types; however, BioMoby is less expressive than OWL. BioSemantic comes without reasoning capabilities. Initially, this task was to be performed by the SWS catalogue (i.
e. , BioCatalogue), but this function is not yet available. The last two criteria define the degree of automation of these approaches. BioMoby and SADI allow for the creation and deployment of SWS skeletons without including core methods. BioSemantic is the only API that processes query creation. This automation is allowed by decoupling annotated RDF view creation and SWS creation. However, this automatic creation of SWS is still dependent on the manual RDF view annotation step performed by the data provider.
Differences between semantic and data type The use of bio-ontology terms to annotate input/output allows for easier detection of our SWS by searching services with a standard vocabulary. Annotations are composed of adding a semantic flag on a component of a database schema, which requires choosing which component of a schema will be annotated. That step is performed manually, and does not guarantee that the same annotation will be associated with similar data. For example, we used the term gcpdm:study to annotate the name of a study because the only identifier of a study existing in the TropGene database is an auto increment with no scientific sense. If another curator uses the same term to annotate an identifier, the data returned by the two different services would not be comparable even if the two services return information on a genotyping study.
Shortest path algorithm One input and one output The major limit of our query comes from the restriction to a single input concept and a single output concept. That restriction is because of the shortest path algorithm, which allows only the joining of a node of a graph to another node. That restriction implies that we create a query coming from a linear path in the graph that represents the database schema. It would be interesting to modify our algorithm to find a path that links a number n of input nodes in our future query to n output nodes.
Manual RDF view annotation Future developments will concern the semantic annotation of RDF views, which is the only manual task in BioSemantic. This task could be a time-consuming task for database annotators if the database schema is large. Constraints for annotators arise many because they must be experts on database schemas and the ontology terms and must also manipulate RDF and D2RQ. We believe that this limitation could be partially overcome by creating a user interface for the annotation of RDF Views. Our solution opens new perspectives for the development of SWS. However, we are still interested in adding more functionality, such as the automatic generation of links between database schemas and existing ontologies. We are currently exploring the use of automatic schema-matching tools developed in the context of the WebSmatch platform  [43].
Conclusions BioSemantic is a framework that is designed to speed the development of Semantic Web Services for existing relational biological databases. This framework has the specific capability of separating the publishing step of the relational schema from the SWS creation. Data consumers can then create Semantic Web Services without knowledge of the resource schema. Currently, it automatically creates and semi-automatically annotates RDF views that enable the automatic generation of SPARQL queries. These queries are created by the following steps: (i) the selection of input and output ontological terms using a Web interface that is available in the BioSemantic API; (ii) the automatic detection of a path linking inputs to outputs; and (iii) the use of the path to automatically generate a SPARQL query.
Figure10Use case workflow created with Taverna 2. The workflow contains BioSemantic SWS querying for both the TropGene and Gramene databases for QTL information retrieval and also contains BioMart WS querying EnSembl for gene information retrieval. This workflow also detects QTLs from TropGene and Gramene when both are annotated with the same TO term and have the same mapping position.
Statistical Analysis For a number of years, manual measurements have been used to assess network lengths. It is desirable that we obtain the same measurements with the automated procedure. First, manual and automatic measurement distributions were superimposed to check for overlapping and similar distribution shapes. Then, boxplots were used to describe these distributions more precisely. As indicators of the strength of a linear relationship between two independent variables, Pearson's correlation coefficients between manual and automatic measurements were calculated. These coefficients were calculated in all experimental conditions. Analyses were performed using the SAS v9. 2 software package.
Image Analysis Software The image analysis procedures use the PINK library developed at ESIEE (http://www. pinkhq. com). This library includes many useful mathematical morphology and discrete geometry algorithms and is freely available. We mostly use the morphological filtering, segmentation, and skeletonization components of the library. Statistical analysis is performed in the open statistical language R and Microsoft Excel. Open-source components were chosen for easy deployment, affordability, and scalability with computer performance.
Conclusion The automated image analysis procedure does not measure exactly the same network features than a human operator,  and as a result, direct measured length comparison is not possible. However, the results obtained show a good correlation between manual and automated measurements. The software is now used routinely by our operators and has been parallelized so each image can be run independently on a separate CPU on a Linux cluster. A postprocessing script collects the results obtained by each operator and calculates overall statistics. This workflow permits analysis of 180 images in 20 min, which corresponds to a speed increase factor of 6×, in comparison to manual analysis. Moreover, this workflow improved reliability, intraindividual reproducibility, and interindividual variations.
This study describes how management of grasslands is included in the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) process-based ecosystem model designed for large-scale applications, and how management affects modeled grassland-atmosphere CO 2 fluxes. The new model, ORCHIDEE-GM (grassland management) is enabled with a management module inspired from a grassland model (PaSim, version 5. 0), with two grassland management practices being considered, cutting and grazing. The evaluation of the results from ORCHIDEE compared with those of ORCHIDEE-GM at 11 European sites, equipped with eddy covariance and biometric measurements, shows that ORCHIDEE-GM can realistically capture the cut-induced seasonal variation in biometric variables (LAI: leaf area index; AGB: aboveground biomass) and in CO 2 fluxes (GPP: gross primary productivity; TER: total ecosystem respiration; and NEE: net ecosystem exchange).
However, improvements at grazing sites are only marginal in ORCHIDEE-GM due to the difficulty in accounting for continuous grazing disturbance and its induced complex animalvegetation interactions. Both NEE and GPP on monthly to annual timescales can be better simulated in ORCHIDEE-GM than in ORCHIDEE without management. For annual CO 2 fluxes, the NEE bias and RMSE (root mean square error) in ORCHIDEE-GM are reduced by 53 % and 20 %, respectively, compared to ORCHIDEE. ORCHIDEE-GM is capable of modeling the net carbon balance (NBP) of managed temperate grasslands (37 ± 30 gC m -2 yr -1 (P < 0. 01) over the 11 sites) because the management module contains provisions to simulate the carbon fluxes of forage yield, herbage consumption, animal respiration and methane emissions.
ORCHIDEE (Organizing Carbon and Hydrology In Dynamic EcosystEms) ORCHIDEE is a process-driven dynamic global vegetation model (DGVM) designed to simulate C and water cycle from site-level to global scale  (Krinner et al. , 2005; Ciais et al. , 2005; Piao et al. , 2007). It is composed of two main modules. The SECHIBA (soil-vegetation system and the atmosphere) parameterization computes the energy and hydrology budget on a half-hourly basis, together with photosynthesis based on enzyme kinetics  (Viovy and de Noblet, 1997).
General structure PaSim is a plot-scale process-based grassland model developed by  Riedo et al. (1998) , which simulates grassland processes at a sub-daily time step. It considers a soil-vegetationanimal-atmosphere system (with state variables expressed per m 2 ) and runs over one to several years. PaSim allows simulating main grassland services such as forage and milk production, as well as the C, N, water and energy fluxes in sown and permanent grasslands. PaSim was applied on a grid to make simulations of grasslands GHG fluxes at the European scale by  Vuichard et al. (2007b)  and was used to run an ensemble of climate change impacts simulations on grassland services and GHG budgets at French sites  (Graux et al. , 2012 (Graux et al. , , 2013)). PaSim comprises six modules, simulating plant growth, microclimate, soil biology, soil physics, animal processes, as well as management options.
Management simulation In PaSim, management includes mineral and/or organic N fertilization, and cutting and grazing, which can either be set by the user or optimized by the model  (Riedo et al. , 2000; Vuichard et al. , 2007a; Graux et al. , 2011). At each cutting operation, a fraction of the shoot biomass is harvested and exported away from the grassland, and a fixed amount corresponding to the residual shoot dry matter (DM) (e. g. , 0. 15 kg DM m -2 ) is left in field. PaSim assumes that a certain amount (e. g. , 5 %) of the grass harvest is lost as litter. For grazing, the version of the animal module of PaSim (version 4. 5) developed by  Riedo et al. (2000)  was further improved by  Vuichard et al. (2007a). Riedo et al. (2000)  simulated animal herbage intake, milk production (MP), returns, respiration and CH 4 emissions from pastures grazed by dairy cows or sheep with simplified equations.
Coupling strategy To incorporate into ORCHIDEE a description of management, our approach is to take the cutting, grazing and fertilization options, and the animal module of PaSim (version 5. 0, see above) and integrate them into ORCHIDEE. Each day, ORCHIDEE provides AGB to the management module to be used for cutting or grazing (Fig. 1 ). Taking into account the types of herbivores (different types of cattle or sheep; Sect. 2. 2), the management module simulates harvested biomass, herbage intake and animal trampling during grazing, and following C fluxes by animal respiration, milk production, CH 4 emissions, and excreta returns. Then it feeds back two variables into ORCHIDEE, the residual AGB fraction, and the newly formed litter. The litter pool of ORCHIDEE is modified from the input of harvested grass residues, manure additions, and from animal trampling effect and excreta returns.
Specific modifications in the ORCHIDEE-GM As ORCHIDEE is designed to represent the C cycle of unmanaged grassland, we adapted the model to include (i) the possibility of reaching high LAI values such as observed in productive managed European grasslands; (ii) the leaf shed in highly dense tillers; (iii) a reduction of the leaf fraction in total AGB, and iv) a translocation of carbon from a reserve pool after cut in order to shape new leaves. In addition, we improved the representation of specific leaf area (SLA) for stimulating regrowth after cutting or grazing. These structural changes made to the ORCHIDEE-GM model code are described below.
This process results in an increasing age of the canopy during the growing period. We calculated SLA as SLA = 4 i=1 sla i × f i , (3) where f i is the fraction of leaves in age class i, and sla i is the prescribed maximum SLA (SLA max ) for age class i. The value of sla 1 equals to SLA max , then to a fraction of 0. 90, 0. 85 and 0. 80 of SLA max for leaf age class 2, 3 and 4, respectively. In ORCHIDEE-GM, SLA max is set to 0. 048 m 2 g -1 C, a value chosen to fit the mean SLA value of C3 grass in the global TRY database (0. 0201 m 2 g -1 DM, equal to 0. 0422 m 2 g -1 C with mean leaf carbon content per dry matter of 47. 61 %;  Kattge et al. , 2011). 3 Evaluation at European grassland sites.
Meteorological data Meteorological data required as input by ORCHIDEE and ORCHIDEE-GM are half-hourly air temperature, precipitation (each event), wind speed, atmospheric water vapor pressure, net radiation, long-wave incoming radiation, mean near-surface atmospheric pressure and annual CO 2 atmospheric concentration. All the meteorological variables were measured on top of each flux tower on a half-hour time step, meeting the requirement of the models, except for CO 2 , which is taken from atmospheric background measurements. The forcing data were firstly cleaned and then gap-filled according to the methods as follows (see  Falge et al. , 2001). For small gaps (e. g. , one or two hours), linear interpolation was used; for bigger gaps (e. g. , one or two days), data from days with similar pattern were adopted; for gaps longer than 10 days (e.
g. , in winter at cold sites, the data were gap-filled with data from similar previous year; if necessary, precipitations have been corrected in order to get the correct total annual sum. The long-wave incoming radiation (few measurements are available at the sites) and mean near surface atmospheric pressure (not measured at the sites) were extracted from the 6-hourly CRU-NCEP 0. 5 • × 0. 5 • global database (http://www. cru. uea. ac. uk/cru/data/ncep/), which are linear interpolated to half-hourly.
0201 m 2 g -1 DM (equal to 0. 0422 m 2 g -1 C, with mean leaf carbon content per dry matter of 47. 61 %) reported by  Kattge et al. (2011)  in the TRY database for 594 species (5033 observations) around the world. The dynamic SLA modeling accelerates grasses regrowth (higher LAI during growing season in Fig. 3 ), but the effect on LAI remains small (difference of 2. 35 % for annual mean LAI).
, 2008). Improved GPP simulation by ORCHIDEE-GM comes from more accurate prediction of plant growth under management. However, the main component of TER is soil respiration. It is highly sensitive to soil organic matter amount, which is initialized by the same soil C module in ORCHIDEE rather than by field observations in this study. Although ORCHIDEE-GM performs better on both timescales, systematically better at cut sites than at grazed sites, the improvement at grazed sites are more noticeable on the seasonal-annual than on the intermonthly timescales. This illustrates the fact that cut and grazing practices have different influences in the temporal variation of NEE, and that grazing has more impact on seasonal-annual than on intermonthly timescales. The large amplitude on intermonthly timescale (Fig.
Management However, it should be noted that our estimation of NBP is biased by the fact that the model initialization of soil C pools is not realistic. Only long-term simulation with precise management history or initialization based on precise soil organic C (e. g. , the European Soil Database;  Panagos et al. , 2012)  can avoid this uncertainty when running the model on a grid for future applications. Nevertheless, a more complete picture of NBP provided by ORCHIDEE-GM enables us to separate the role of grassland management from those of other factors (e. g. , CO 2 , climate and land use) in the attribution of the grassland C sequestration  (Soussana et al. , 2010).
Conclusions This paper is an attempt to realistically represent the impact of management on the C fluxes of European grasslands in a DGVM. We developed a new model, ORCHIDEE-GM, integrating a management module from a grassland specific model (PaSim) and evaluated its results at 11 European sites. Generally, ORCHIDEE-GM is better able to reproduce intraannual variation of LAI, AGB, and CO 2 fluxes (GPP, TER, and NEE) induced by cut or grazing practices. Model-data discrepancies can be attributed to lack of data to initialize soil C pools, to CO 2 fluxes observation uncertainties and to some site-specific parameter values. The optimization of N related parameters (SLA, Vc max , and J max ) in ORCHIDEE-GM reduces the model-data misfit at sites where it was performed. However, we should state that this study is not designed for fully bridging the gap in C fluxes simulations but more for understanding the contributing effect of improved management practices to C fluxes simulations in temperate grassland.
INTRODUCTION The "Spoken Web Search" task of MediaEval 2011 involves searching for audio content, within audio content, using an audio content query. By design, the data consisted of only 700 utterances in Telephony quality from four Indian languages (English, Gujarati, Hindi, Telugu), without language labels. The task therefore required researchers to build a language-independent audio search system so that, given a query, it should be able to find the appropriate audio file(s) and the (approximate) location of query term within the audio file(s). Performing language identification, followed by standard speech-to-text is not appropriate, because recognizers are typically not available in these languages. Evaluation was performed using standard NIST metrics for spoken term detection  [1]. For comparison, participants could also search using the lexical form of the query, but dictionary entries for the search terms were not provided, and we are not reporting results here.
DESCRIPTION OF TASK AND DATA Participants were provided with a dataset that has been kindly made available by the Spoken Web team at IBM Research India  [2]. The audio content is spontaneous speech that has been recorded using commonly available land-line and mobile phone equipment in a live setting by low-literate users. While most of the audio content is related to farming practices, there are other domains as well. Data was collected from the following domains: (1) Sugarcane information by farmers (North India, Hindi language, 3000 users), (2) Village portal by villagers (South India, Telugu language, 6500 users), (3) Farming knowledge portal (West India, Gujarati language, 175 users), (4) Mixed content, e. g. job portal, event agenda (South and North India, English language, 80 users).