dataesr/scientific-paragraphs-categorization huggingface dataset:
4. 0). SIRIUS is a collection of different separate tools that together provide molecular formula annotation  (ZODIAC, [332] ), prediction of compound classes without the need of databases  (CANOPUS, [372] ), and library search (PubChem) based on a molecular structure fingerprint predicted by fragmentation trees which are calculated from the MS2 spectrum  (CSI:FingerID, [298, 333] ). KPIC2 offers a feature extraction with parameters (i. e. , mass tolerance) based on the clustering of ions without the need to set them oneself, removing the arbitrary setting of parameters by the researcher. This feature extraction algorithm is integrated in patRoon. KPIC2's pattern recognition, based on Partial least squares discriminant analysis (PLS-DA) and RandomForest can be used as a tool for prioritisation of features  [373].
OpenMS provides a massive range of more than 185 different tools for different steps of a processing workflow  [320]. Although originally developed for proteomics and metabolomics, it is also commonly used in environmental analysis settings via the algorithms used in patRoon for data pretreatment, peak picking, componentisation and adduct annotation. Digital Sample Freezing Platform (DSFP) The DSFP was developed by NORMAN as a collaborative effort for retrospective screening of data uploaded to the platform [374], using  NORMAN SusDat [375]  for annotation. Data sets can be uploaded, archived, processed and compared to publicly available data already in the database. This helps find spatial or temporal trends for chemicals of concern  [374]. GNPS The Global Natural Product Social molecular networking platform (GNPS) was developed as a "big data" tool for MS2 data curation and analysis  [318, 376].
MZmine currently in its third version, MZmine is a modular toolbox offering a range of different options for raw data import, peak list methods (alignment, filtering, etc. ), statistical analysis and visualisation  [215, 216]. Custom databases and online databases such as PubChem, KEGG and HMDB can be used for candidate searching. Parts of MZmine are also integrated in other third-party software, such as SIRIUS and GNPS. InSpectra Newly released, InSpectra offers a completely automated cloud-based processing, including a comprehensive workflow based on a range of previously published algorithms for feature detection  [221], deconvolution [378] , prioritisation  [379] , annotation using the US EPA CompTox database  [274]  and eventually comparison to data that others have processed before to find temporal and spatial trends of chemicals of emerging concern.
The need to preserve all recorded mass spectral information led to the digital archiving of environmental HRMS data  [423]. The archiving offers a possibility of retrospective suspect screening of contaminants of concern for various research and policy purposes. To achieve this goal, a common repository for HRMS chromatograms of environmental samples, accompanied by a suite of compound structure-elucidation software tools, has been developed by the NORMAN network  [374]. The NORMAN Digital Sample Freezing Platform (DSFP) is part of the NORMAN Database System  [424] , where the substance database is built on contributions made via the NORMAN-SLE  [269]. It is designed to store HRMS data and meta-data (sampling site, matrix and sample description, instrumental setup etc. ), which are required for interpretation of the results.
Detecting exposure-relevant chemicals based on modelling approaches (emission, fate) in combination with chemical databases Identifying causative chemicals for adverse effects observed in ecosystems by ecological monitoring Detecting TPs or (disinfection) byproducts reported in literature or predicted by QSAR Identifying new persistent and bioaccumulative compounds from multimedia and biota monitoring Spatial monitoring along, e. g. , river stretches Spatial monitoring along, e. g. , river stretches Trend monitoring over time at one site Trend monitoring over time at one site Retrospective screening of emerging suspects in stored raw data to determine extent of previous contamination.
I. EDICT ARCHITECTURE With the recent advances in Artificial Intelligence (AI) and Machine Learning (ML) techniques to provide IoT-based solutions, there is a growing need for datasets that capture the performance of data flows in smart environments. EDICT  [1]  is a simulation tool for generating performance metrics datasets of IoT enviroments that aims to facilitate IoT system tuning tasks. This paper demonstrates how EDICT can be used for such purposes. Fig. 1  shows the high-level architecture of EDICT. We briefly describe next the main components of this architecture. Dataset.
B. Queueing Network Composition We rely on Open Queueing Networks to create a generic QoS Model that represents application-layer interactions in IoT environments. EDICT takes as input the NGSI-LD representation of a smart environment and parses it to extract the information needed to instantiate the generic QoS Model and compose the corresponding Queueing Network that evaluates Edge interactions in the provided environment. This is possible through API calls to the open-source Java Modelling Tools (JMT)  1  queueing simulator. EDICT composes and simulates the JMT queueing network to generate a dataset containing performance metrics of data flows in the IoT-enhanced environment. A more detailed description about the queueing network composition process is provided in  [3].
C. Performance Metrics Dataset Generation After simulating the composed JMT queueing network, EDICT extracts the simulation results from the JMT jsimg files and creates a performance metrics dataset as a csv file. The generated dataset includes metrics related to response time, throughput, and data drop rate for each data flow in the simulated smart environment. In addition, the file contains the configuration parameters of the data exchange system. These parameters include the priorities and data drop rate set for each application category, as well as the available network resources and the network allocation policy used. These features facilitate the use of the dataset for automated system tuning approaches  [4]. We demonstrate how the dataset can be readily used for QoS prediction in  [1].
II. USING EDICT In this section, we demonstrate how EDICT can be used by IoT systems designers to generate a performance metrics dataset of Edge interactions in smart environments. For the sake of example, we consider an IoT system consisting of 30 devices that capture 30 different observations, with 16 applications to receive the observations generated by the IoT devices. The deployed applications belong to four application categories with different QoS requirements: analytics (AN), real-time (RT), transactional (TS), and video streaming (VS). The EDICT code can be downloaded at https://github. com/ SAMSGBLab/edict.
Moreover, the configuration parameters include setting drop rates for application categories, and defining the capacity (in number of messages) of the data exchange system. Designers can then define some simulation settings: the simulation duration, an alias to be used for saving the simulations results, and a global message size to be used when running the simulation. Once the configuration parameters are set, EDICT is ready to generate the performance metrics dataset. Through calls to the JMT library, EDICT composes and simulates the JMT queueing network (Fig. 4 ). Then, EDICT generates a dataset as a csv file consisting of performance metrics of the simulated system. IoT designers can also test different configuration parameters for the same system to evaluate the performance of their systems under different situations (e.
g. , using different network resource allocation policies, different drop rates). This enables creating a richer dataset, where each row contains the metrics for a flow matching a subscription under different configurations parameters. As depicted in Table  I , metrics are generated for each flow matching a subscription, and when applicable, for the whole system. For example, EDICT stores end-to-end latency per flow and the utilization of the data exchange system under all configurations specified. In Table  I , each row contains the metrics for a flow matching a subscription under different configurations parameters. For example, if the developer chooses to simulate an IoT system using the default network allocation policy as well as the maxmin policy, a subscription flow would have two entries in the dataset that represent its metrics under these two policies.
We added Nd isotopes ( 143 Nd and 144 Nd) as optional passive tracers into the ocean component of FAMOUS, using a version of the model with the Met Office Surface Exchange Scheme (MOSES) version 1  (Cox et al. , 1999: FAMOUS-MOSES1). It was a pragmatic choice to avoid the more recent FAMOUS-MOSES2. 2 configuration  (Essery and Clark, 2003; Valdes et al. , 2017; Williams et al. , 2013; Essery et al. , 2001)  because evaluation of our new Nd scheme would be hindered by the collapsed Atlantic Ocean convection and strong deep Pacific meridional overturning circulation (MOC) produced by FAMOUS-MOSES2. 2 under pre-industrial boundary conditions (see  Dentith et al. , 2019). Nonetheless, the Nd isotope code implementation presented here is directly transferable to other versions of the UK Met Office Unified Model (UM) version 4.
See Table  S2  in the Supplement for initial boundary conditions and Figs. S1-S4 for the Atlantic meridional stream function and mixed-layer depth for the four experiments. These simulations were integrated for a further 5000 years to ensure adequate spin-up of the physical circulation. They were then assessed for their ability to simulate global modern observations of salinity and temperature (compared to the NOAA World Ocean Atlas salinity and temperature database;  Locarnini et al. , 2019; Zweng et al. , 2019)  as an objective and quantitative basis for selecting the best-performing parameter configuration to be our control (Fig. 1 ). From this analysis, XPDAA returned the lowest rootmean-square error (RMSE) (and hence best performance using this metric; Fig. 1 ) for simulating both salinity (RMSE of 0.
FAMOUS does contain an optional ocean biogeochemistry module (Hadley Centre Ocean Carbon Cycle; HadOCC), which includes simplified nutrient, phytoplankton, zooplankton, and detritus (NPZD) classes  (Palmer and Totterdell, 2001)  and could instead be used as the basis for predicting vertical particle fluxes in the ocean (which was the approach adopted by  Arsouze et al. , 2009; Gu et al. , 2019; and Rempfer et al. , 2011). We favoured satellite-derived estimates in order to improve the accuracy of particle-associated cycling of Nd and reduce biases inherent to the intermediate-complexity biogeochemistry model  (Dentith et al. , 2020; Palmer and Totterdell, 2001). This approach also optimises the computational efficiency of our scheme. In our scheme, biogenic particle fields (POC, CaCO 3 , and opal) are prescribed using gridded, global satellite-derived particle export productivity from  Dunne et al.
e. at the surface and high latitudes. We also report root-mean-square error (RMSE) for each simulation as an additional indicator of model skill and to allow for additional comparability with other studies: RMSE = 1 N N k=1 (obs k -sim k ) 2 N. ( 18 ) The observational data used in this assessment are from the seawater REE compilation used by  Osborne et al. (2017 Osborne et al. ( , 2015)) , augmented with more recent measurements including data in the GEOTRACES Intermediate Data Product 2021 (GEOTRACES Intermediate Data Product Group, 2021) from GEOTRACES cruises (GA02, GA08, GP12, GN02, GN03 and GIPY05). Combined, our observational database represents a total of 6048 [Nd] d and 3278 ε Nd measurements, making it the largest compilation of seawater Nd data used to date to validate the performance of an Ndisotope-enabled model.
Generally, simulated  [Nd]  d distributions in EXPT_RS4 match observational data well (Fig. 10 ). The lowest concentrations occur in the surface layers, and deep-water [Nd] d increases along the global circulation pathway and with increasing age of water masses  (Bertram and Elderfield, 1993; van de Flierdt et al. , 2016; Tachikawa et al. , 2017). Thus, we may infer that the model scheme in FAMOUS does have the broad capability of representing the physical and biogeochemical processes governing global marine [Nd] d distributions. However, the scheme does tend to overestimate the global vertical [Nd] d gradient with depth (see Fig. 10  and Fig. S9  in the Supplement for major ocean basin averaged depth profiles), a feature reported in previous similar model schemes (e. g. Arsouze et al. , 2009; Gu et al.
, 2015; Rahlf et al. , 2021) , which implies that the magnitude of the sediment Nd sourced to seawater near river mouths could be too weak. Specific to our scheme, the vertical convection of water from the physical ocean model in FAMOUS may also be https://doi. org/10. 5194/gmd-16-1231-2023 Geosci. Model Dev. , 16, 1231-1264, 2023 Nonetheless, consistent with compilations of water column measurements  (Tachikawa et al. , 2017; van de Flierdt et al. , 2016) , overall global distributions of simulated ε Nd are broadly most non-radiogenic in the North Atlantic and more radiogenic in the North Pacific (Fig. 10 ), with intermediate values in the Southern and Indian oceans. The most non-radiogenic ε Nd occurs in the surface layers of the Hudson Bay and Labrador Sea regions, and they closely match measured data (ε Nd = -18).
e. "mod") files and should be applied to the original FAMOUS model code, which is protected under UK Crown Copyright and can be obtained from the National Centre for Atmospheric Science (NCAS) Computational Modelling Services (CMS; https://cms. ncas. ac. uk/; last access: 12 February 2013 ), with specific FAMOUS documentation available at https://cms. ncas. ac. uk/miscellaneous/um-famous/ (NCAS, 2023). All files and corresponding information that needs to be applied to configure each individual simulation presented here are available from the same DOI (https://doi. org/10. 5518/1136); note that to complete the setup of these simulations, line 4909 in each simulation's tracer. f file needs updating with the corresponding RS_TUNE value as listed in Table  3. A complete version of the modified code for the EXPT_RS4 simulation using FAMOUS-MOSES1, including Nd isotope implementation, is archived at the Research Data Leeds Repository and linked from the DOI above.
Control candidate simulations for new FAMOUS reference are as follows: -XPDAA control simulation (0-5000 years), -XPDAB control candidate simulation (0-5000 years), -XPDAC control candidate simulation (0-5000 years), -XPDEA control candidate simulation (0-5000 years). Reversible scavenging efficiency  ([Nd]   Total Nd source from sediment (f sed ) sensitivity simulations are as follows: -XPDAL f sed = 1. 5 Gg yr -1 (0-9000 years), -XPDAM f sed = 3. 0 Gg yr -1 (0-9000 years), -XPDAH f sed = 4. 5 Gg yr -1 (0-9000 years), -XPDAN f sed = 6. 0 Gg yr -1 (0-9000 years). Data availability. The data are available via the Research Data Leeds Repository  (Robinson et al. , 2022:  https://doi. org/10. 5518/1136. ) Supplement. The Supplement related to this article is to be associated with a DOI and is currently attached as a.
Acknowledgements. Suzanne Robinson  was funded by the  Natural Environment Research Council (NERC)   SPHERES Doctoral Training Partnership  (grant no. NE/ L002574/1 ). Lauren J. Gregoire  was funded by  UKRI Future Leaders Fellowship "SMBGen"  (grant no. MR/S016961/1 ). Julia Tindall  was supported through the  Centre for Environmental Modelling and Computation (CEMAC) at the University of Leeds. Frerk Pöppelmeier  was supported by the  European Union 's  Horizon 2020  programme (grant no. 101023443 ). Tina van de Flierdt acknowledges funding from  NERC  (grant no. NE/P019080/1 ). Our heartfelt thanks go to  Jamie D. Wilson  for valuable discussions when implementing the reversible scavenging scheme and to  Andy Ridgwell  for helpful discussions during the design of code developments. We would like to thank all reviewers for taking the time and effort necessary to review the manuscript.
We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript. Financial support. This research has been supported by the  Natural Environment Research Council  (grant no. NE/L002574/1 ). Review statement. This paper was edited by  Andrew Yool  and reviewed by  Benoit Pasquier ,  Ed Hathorne ,  Catherine Jeandel , and  Torben Stichel.
Objectives This goal of this research is to develop an open-source, statistically validated assessment of deforestation and degradation (DD) and the associated direct drivers at regional scale covering six countries of Central Africa over the 2015-2020 study period. The identification of distinct forest classes, types of change and the presence of direct drivers is visually interpreted by a team of regional experts over a stratified random sample of points. The relative contribution of drivers and their combinations are assessed by forest types, fragmentation classes and their associated biomass. The methodology is globally applicable, replicable, and open access to support decision making activities such as land use planning and prioritization as well as for validating other satellite sources of data for monitoring forest ecosystems.
Regional land cover We developed a regional land cover map to define 12 types of forest cover, and 7 types of non-forest (Table  1 ), synthesized from vegetation classifications from each of the six countries and harmonising them using the universal Land Cover Classification System (LCCS) framework  (Di Gregorio, 2005). Landsat satellite image composites for the baseline year, along with auxiliary information on elevation  (Farr and Kobrick, 2000) , the percent tree cover dataset, and water sources  (Pekel et al. , 2016) , were classified. A supervised training algorithm was executed in SEPAL using the random forest machine learning approach, calibrated with 2190 training points provided by partners and derived from visual interpretation of 5 m resolution image mosaics for 2015-2020 provided by Planet, through a program financed by the Norwegian Government (NICFI), along with other high-resolution images using Collect Earth Online, a tool provided by the Open Foris Initiative of the FAO  (Saah et al.
Visual interpretation Validation data points were selected for visual interpretation to identify forest type, presence of change, type of change (deforestation, degradation), date of change and presence of one or more direct drivers. A stratified random sampling scheme was developed to select spatially balanced samples proportional to the size of the map classes of forest type and change  (Pagliarella et al. , 2018). At least 150 random points per change class were selected, with more points for larger map classes (N = 11,078), along with a random sample of stable points from all land cover classes (N = 1,192). Visual interpretation of all points was performed using the Open Foris tool Collect Earth Online, using available the high-resolution optical image mosaics from Planet. Samples were uploaded to Collect Earth Online for visual interpretation by a group of 60 experts from the project technical committee developed guidelines and agreed definitions and who were then trained in the use of the tool.
Trends in forest disturbance (2015-2020) In comparison with existing global datasets on forest disturbances, we observe similar trends in the 2015-2020 time period for all Congo Basin countries. Data from Global Forest Change (GFC;  Hansen et al. , 2013)  and Tropical Moist Forests (TMF;  Dalimier et al. , 2022; Vancutsem et al. , 2021)  report higher rates of forest disturbance after 2015, which then decrease from 2017 until 2019. Estimated burned forest area has also been observed to be generally decreasing since 2001  (Andela et al. , 2017). The FAO Forest Resources Assessment Remote Sensing survey also observed an overall slowing of deforestation in 2010-2018 in comparison to 2000-2010, which diverges from the Global Forest Resources Assessment (2020) which identifies an increase in rates of loss between 1990 and 2020  (FAO, 2001)  which demonstrates how different methodologies may not reach consensus, and that assessments should be tailored to regional or national scales.
The effect of toroidal rotation on both turbulent and neoclassical transport of tungsten (W) in tokamaks is investigated using the flux-driven, global, nonlinear 5D gyrokinetic code GYSELA. Nonlinear simulations are carried out with different levels of momentum injection that drive W into the supersonic regime, while the toroidal velocity of the main ions remains in the subsonic regime. The numerical simulations demonstrate that toroidal rotation induces centrifugal forces that cause W to accumulate in the outboard region, generating an in-out poloidal asymmetry. This asymmetry enhances neoclassical inward convection, which can lead to central accumulation of W in cases of strong plasma rotation. The core accumulation of W is mainly driven by inward neoclassical convection. However, as momentum injection continues, roto-diffusion, proportional to the radial gradient of the toroidal velocity, becomes significant and generates outward turbulent flux in the case of ion temperature gradient (ITG) turbulence.
The impact of toroidal rotation on both turbulent and neoclassical W transport was investigated in Ref. [21]. The main approach used in the study was to compute turbulent and neoclassical transport separately. Turbulent transport was calculated using the gyrokinetic model in the toroidally co-moving frame, whereas neoclassical transport was evaluated using the drift-kinetic code NEO, but without considering frictional effects at the lowest order  [17, 25]. The aim of this paper, as an extension of the previous work on impurity transport from light to heavy impurities  [26] , is to explore the effect of toroidal rotation on turbulent and neoclassical W transport using the full-F gyrokinetic code GYSELA  [27]. By employing the multi-species collisional operator implemented in GYSELA  [28] , we investigate the selfconsistent treatment of both neoclassical and turbulent transport channels.
The interplay between these channels can have a significant impact on impurity transport and its accumulation in the core of tokamaks  [29, 30]. To this end, we examine the impact of sonic (for the impurities) toroidal rotation on turbulent and neoclassical transport in the presence of poloidal asymmetry enhanced by centrifugal forces. The remainder of the paper is organized as follows. Firstly, in Section II, we introduce the GYSELA code and the source terms for studying impurity transport in a rotating plasma. Section III discusses the results of the nonlinear GYSELA simulations, with a focus on the poloidal asymmetry generated by the external momentum injection. In Section IV, the effect of sonic rotation on both turbulent and neoclassical W transport is explored, highlighting the inward neoclassical convection due to the poloidal asymmetry and the outward rotodiffusion due to the steepening of the velocity gradient.