dataesr/scientific-paragraphs-categorization huggingface dataset:
Mixture risk assessments While the presented projects have their major focus on either exposure or hazard assessment, all of them explore strategies to integrate both to finally address risks from exposure to chemical mixtures. EuroMix aims at integrating exposure and hazard modelling into an integrated probabilistic risk modelling approach for mixtures, which will become available as a web-based toolbox. EuroMix will include a very conservative risk assessment as a lower tier for data poor situations based on conservative assumptions on CAG membership, aiming to set test priorities. Additionally a higher tier risk assessment will be implanted using more realistic information on CAG membership and on the chemicals' potencies as well as full data on consumption and of chemicals in food. These data are currently well-organised in Europe for consumption by EFSA and the EU Member States, for pesticides and various food contaminants for which monitoring is requested by regulation.
In the course of the HBM4EU project new HBM mixture data will be compiled for the European population. Based on experience gained from the initial analysis of already existing HBM mixture data, risk assessment and health impact assessments will be performed on these new mixture data. Based on the analysis of existing HBM mixture data, alternative approaches to generate aggregated indicators based on toxicity information will be compared. To this end, a reference table will be developed which curates available health-based guidance values, such as the German Human Biomonitoring Values and Biomonitoring Equivalents from different sources, including both workplace and general population values. These will serve as the basis from which a mixture indicator (such as a Hazard Index) may be calculated.
Identification of priority mixtures With the large number of chemicals in use, there is an intractably large number of possible combined exposures to multiple chemicals. Therefore, concepts for prioritising chemical combinations of concern are needed, in order to effectively act on them. The three non-food Scientific Committees of the European Commission SCHER/SCENIHR/ SCCS (2012) have suggested criteria for identifying priority mixtures based on combinations of exposure and hazard criteria, such as significant environmental exposures close to health based guidance values, multi-constituent products and commercial mixtures containing active substances or substances of concern, likelihood and frequency of exposure and their persistence, etc. The EuroMix toolbox includes a module for identifying the mixtures of concern based on exposure and hazard considerations.
Based on grouping of HBM mixture data on toxic potency for common MoA and AOPs, clustering in specific risk groups will be performed, within and across the different countries. Moreover, determinants of these clusters of risk groups will be further assessed. SOLUTIONS is working on a proposal for an advanced methodological framework for identifying priority pollutants and priority mixtures of chemicals in European freshwaters. The proposal aims to tackle major shortcomings of current prioritisation procedures under the EU WFD, which is mainly focused on single chemicals. The advanced framework is proposed to integrate all available lines of evidence (LOE) on significant mixture risks. This includes evidence from (i) ecological monitoring (field observations on so-called BQEs), (ii) effect-based monitoring (in vitro or in vivo testing in the lab or onsite), (iii) chemical monitoring in combination with component-based mixture assessment approaches, and (iv) integrated modelling of co-exposure and resulting mixture risks.
Acknowledgements WB, JvG, MF, AK have received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement No  603437  (SOLUTIONS). AB, PED, JR have received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement GA No 634880 (EDC-MixRisk); JvK under GA No 633172 (EuroMix); MKG, RB, EL under GA No  733032  ( HBM4EU ); BvdW under GA No  681002  (EUToxRisk). JR acknowledges further funding received from the  Swedish Research Council ; EL and JvK acknowledge further funding received from the  Strategic Programme RIVM.
Model input and setting Model performance was tested against observed data from Guandaushi and four FLUXNET sites (Hainich, Carlow, Brasschaat, and Turkey Point 89). The FLUXNET database provides on-site meteorological data for each site that could be used as forcing for simulations in JULES. However, we had to use the global WATCH dataset  (Weedon et al. , 2010)  as forcing for the Guandaushi site where no on-site data were available. Forcing data were checked for any missing information and they were gap filled by linear interpolation. The meteorological forcing is provided at the measurement site level (no explicit spatial resolution) and includes the downward shortwave and longwave radiation at the surface (W m -2 ), rainfall (kg m -2 s -1 ), snowfall (kg m -2 s -1 ), wind speed (m s -1 ), atmospheric temperature (K), atmospheric specific humidity (kg kg -1 ), and air pressure at the surface (Pa) at a 30 min time step  (Best et al.
Acknowledgements. The research leading to these results received funding from the  European Union 's  Horizon 2020 research and innovation programme  under  Marie Sklodowska-Curie  grant agreement no. 643052  ( C-CASCADES  project). We want to thank  Altaf Arain ,  Tim Moore , and  Gerd Glexiner  for providing the DOC measurements. Ronny Lauerwald  received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 703813  for the  Marie Sklodowska-Curie European Individual Fellowship "C-Leak". Jing Tang  is financed by a  Marie Sklodowska-Curie Action Individual Fellowship  (MABVOC:  707187 ) and supported by the  Danish National Research Foundation  ( CENPERM DNRF100 ). Marta Camino-Serrano  acknowledges funding from the  European Research Council Synergy  grant  ERC-2013-SyG-610028 IMBALANCE-P.
10. 1029/2017WR022437 Water Resources Research and management cannot be evaluated. Less developed regions tend to have less dense in situ measurement networks that do not provide adequate coverage of even the most essential variables such as precipitation, and this can be a barrier to successful water management. Figure  1  shows the distribution of precipitation and streamflow gauges in the LAC region that are currently available from global data centers that accumulate subsets of national databases, and highlights regions with very few gauges, particularly for streamflow. Some of these gauges are reporting in real time via national or global teleconnection networks, most are not; and many only have data up to several years to decades ago, making it difficult to accurately quantify water resources and extreme event risk.
In particular, there has been a dramatic decline in networks since the 1980s for various reasons; RS data can overcome many of these issues including transcending transboundary disputes on data sharing  (Biancamaria et al. , 2011; Voss et al. , 2013). At the same time, there are trade-offs to using RS data that we highlight here and discuss in detail in subsequent sections. Some variables cannot be retrieved from satellites as yet, and modelbased sources of data are sometimes the only source of information, in some regions and at large scales (e. g. , wind speed), or can complement in situ and RS sources of data. In section 5 we discuss how models are useful for providing consistent and continuous data records and are the only way of providing physically based hydrological forecasts.
Many of these challenges are related to the design of sensors and their orbital characteristics, which in many cases were not designed with water variables and water management in mind, or are indirect measurements that require often substantial processing and retrieval models to derive the required variable. The reliance on a retrieval model has implications for how these types of data are used as observations, more so when other modeled   Water Resources Research data (e. g. , reanalysis climate data) are required to support the retrievals. A review of the uncertainties associated to RS estimates of hydrologic variables and their applications can be found in  Demaria and Serrat-Capdevila (2016). Satellite remote sensing of relevant variables for water management and hydrological hazard monitoring have evolved considerably from the few early dedicated missions that were focused on snow extent and land cover, to the current routine monitoring of nearly all components of the water balance and vegetation health.
The core satellite was launched in February, 2014, which carries the GPM Microwave Imager that captures precipitation intensities and horizontal patterns and the Dualfrequency Precipitation Radar that provides insights into the three-dimensional structure of precipitating particles. The GPM extends the capabilities of the TRMM sensors to measure light rain and quantify microphysical properties of precipitation. It therefore has obvious applications for drought monitoring globally, plus flood risk and water availability. Other products such as CHIRPS  (Funk et al. , 2015)  and MSWEP  (Beck et al. , 2016)  combine available satellite products with gauge data. MSWEP optimally merges gauge observations (GHCN-D, GSOD, and others), remote sensing data (CMORPH, GridSat, GSMaP, and TMPA 3B42RT), and model outputs (ERA-Interim, JRA-55, and NCEP-CFSR).
Blending of information across sensors of different types appears to be the most promising approach  (Frei et al. , 2012) , but such systems have yet to be developed to provide the necessary resolution (perhaps ~100 m in mountainous regions) and temporal repeat (at least weekly). Microwave radar can solve some issues related to resolution and SAR approaches have been proposed that also address issues of sensing wet snow, such as ESA's CoreH2O (ESA, 2012c) and NASA's Snow and Cold Lands Processes, but these have not been selected or have yet to be selected, respectively. Again, merging of information with models may be the best way forward to provide high space and time resolution data on SWE and snow depth, and several experimental and operational systems exist (e. g. , NLDAS and SNODAS).
, 2016). These early retrievals will be built upon with the recent launch of the NASA Orbiting Carbon Observatory-2 spectrometer and the upcoming Sentinel-5 Precursor mission that hosts the TROPOspheric Monitoring Instrument. These two sensors improve upon the resolution of Greenhouse gases Observing SATellite to 3 and 8 km, respectively, making retrievals relevant to decision-making. The upcoming (2022) ESA FLuorescence EXplorer is the first dedicated Solar-Induced Fluorescence mission. The Sentinel-2 mission provides unprecedented information from which vegetation variables can be derived. The 13 spectral bands from visible and NIR to SWIR at a range of resolutions from 10 to 60 m are suitable for monitoring vegetation health. Sentinel-2 is providing data on several vegetation indices such as LAI, leaf chlorophyll content and leaf water content, and importantly, it is the first sensor to include bands in the 10.
Taking advantage of the high-resolution (0. 05°) PISCO merged data set, Peru generates the Standardized Precipitation Index for drought monitoring which is made available through the National Drought Observatory (SENAMHI, 2018a). For flood risk management, the GPM-IMERG data set is currently used by SENAMHI to identify potential imminent flood hazards. A similar effort was developed in Chile, using the PERSIANN rainfall product. Using the extensive database of more than 700 historical rain gauge stations (Figure  2a ), a two-step procedure has been developed to calibrate PERSIANN in real time using a bias correction  (Yang et al. , 2016) , in combination with a merging approach  (Yang et al. , 2017). This significantly improved the quality of the remote sensing product, and it has been therefore made available within the National Agroclimatic Observatory (Chile National Agroclimatic Observatory, 2018) for rainfall monitoring.
3. 8. 2. Integrated Crop, Drought, and Flood Monitoring Vegetation indices are also actively used in a decision-making framework. Uruguay has developed the Instituto Nacional de Investigacion Agropecuaria (2018) monitoring system to identify the drought hazards in its agricultural sector, which uses the NDVI anomalies as one of its main indicators. A similar system has been implemented in Brazil, called AGRITIEMPO (2018), which uses a combination of station data and TRMM and MODIS remote sensing data. Mexico has implemented the national drought management plan PRONACOSE  (Korenfeld-Federman et al. , 2014) , which includes the establishment of a national drought monitor. This has been built around the CHIRPS and NDVI data sets, providing continuous updates of drought conditions in the country.
The JPSS JPSS is an upcoming polar-orbiting operational environmental satellite system jointly run by NOAA and NASA. Satellites will provide global coverage twice per day, and includes a range of sensors including the Visible Infrared Imaging Radiometer Suite (VIIRS). The VIIRS multispectral instrument was first launched in 2011 aboard the NASA/NOAA Suomi National Polar-orbiting Partnership satellite with the intention of bridging between MODIS and the operational Joint Polar Satellite System (JPSS). VIIRS will be one of the instruments on the JPSS. Its large swath width (3,060 km) provides near-global coverage in 1 day. The sensor measures in 22 bands in the VIR spectra. With a spatial resolution between 375 and 750 m depending on the band wavelength. VIIRS builds on AVHRR and MODIS to provide daily time series of multispectral data with applications in energy and water balance, vegetation dynamics, land cover land use change, and the cryosphere.
, 2017). However, this often represents a disjoint picture from the application context, which may require data over large river basins, for multiple variables, in real time, and so on. This is further complicated by the fact that remotely sensed variables have unique spatial and temporal characteristics that make it difficult to use multiple variables in concert without some kind of downscaling or integration. For example, estimating the water budget across a river basin from remote sensing could require integrating coarse scale estimates of total water storage from GRACE (~500 km, 30 day), precipitation from TMPA (25 km, 3-hourly), ET from MODIS (1 km, once daily) and streamflow (single river reach, 15 day). However, the limited assessment to date of the consistency among remotely sensed variables at scales relevant to WRM shows a range of errors across variables and nonclosure of the water budget when combined (e.
This is echoed by others (e. g. ,  Schaeffer et al. , 2013)  that indicate that cost, accuracy, continuity, and agency approval for using RS were the key concerns for water managers when using RS-based water information. Cost issues may be related to previous experience with RS imagery; for example, Landsat data were previously charged for by U. S. Geological Survey. In terms of accuracy, quantitative error estimates are required for uptake, and users are most interested in site-specific evaluations that are not always available. Evaluations need to take into account in situ measurement errors and scale mismatches between in situ and RS data. At the same time, users also need to identify their accuracy requirements to RS providers. Concerns about continuity are often cited because of worries that products will not be available in the future, and therefore efforts are needed by RS agencies to ensure that continuity is provided including standardizing retrieval methods, data formats and access, and providing intersatellite calibration.
Continuity also relates to challenges in transitioning to using different data sources that may conflict with existing WRM methods and practices that are based on traditional, in situ measurements. Successful implementation of locally tailored applications often suffers from several bottlenecks in local enduser agencies: lack of sufficient human and technical capacity, lack of a stable environment to ensure continuity, maintenance and updating, lack of funding, poor internet connection, and others. Lack of capacity and human resources is identified as a key constraint to the uptake of RS information (World Bank Group, 2018). There is a need for training in methodologies, software, and databases, and backing from agency management is vital to provide the education, training and support. As RS information becomes more complex, especially for the uptake of more recent research products that do not have a long legacy of provision to a wide variety of users, training in specific software and methodologies is required, and this should be focused on WRM applications rather than generic RS techniques.
g. , Apache's Hadoop), or software platforms that abstract into a single software application (e. g. , Google Earth Engine). The scalability and on-demand nature of these resources, coupled with relatively low cost, are key benefits. In particular, the abstraction of the hardware and operating/file system is key to enabling users to access the vast quantities of remote sensing information. These platforms are increasingly being leveraged for analyzing satellite imagery and more recently for WRM applications, such as the Climate Engine that uses Google Earth Engine for drought monitoring and other natural resources management applications  (Huntington et al. , 2017). Despite the provision of reliable, and scalable access to distributed computer power, the overhead of training users should not be underestimated, especially for applications that are not straightforward parallel processing of subsets of satellite imagery.
Outlook Satellite remote sensing is now able to provide near-real-time retrievals of nearly all components of the terrestrial water cycle, albeit with many challenges including those related to accuracy, consistency, continuity, and utility. Although much work is needed to enable and improve approaches for retrieving Water Resources Research groundwater, water quality, surface water levels, and river flows, most of these retrievals are also global in coverage, and at the spatial, temporal, and spectral resolutions to resolve hydrological processes and their interaction with human activities. They are therefore well poised to provide information for WRM for operational and tactical decision-making. In particular, satellites can provide information in regions where in situ data are scarce, unreliable, or unavailable as a real-time information source.
The developer then proceeds to debug the application locally, with an entire copy of the program state. In this paper, we show the benefits of out-of-place debugging in terms of reduced latency and scoping of side effects, in different debugging scenarios. We also present our implementation of IDRA, a prototype of out-of-place debugger in Pharo Smalltalk. We show that the presence of our debugger does not impact application performance during normal execution. We also show that our prototype debugger performs better than a traditional remote debugger in some scenarios and can improve the debugging experience.
Debugger Monitor The monitor implements the debugger API and is in charge of communicating with the debugger process. Its main roles are to supervise and control the application execution. When the application hits a breakpoint or raises an exception (1), the debugger monitor suspends the program execution. Then it extracts the state of the execution and the application's state, and creates a debugging session  (2). The debugging session is then serialized and transferred to the Debugger Manager (3).
IDRA: a Prototype Implementation of out-of-place Debugging We implemented IDRA  [23] , a concrete instantiation of out-of-place debugging for the Pharo Smalltalk  [32]  programming language. We choose Pharo as our experimentation platform because it features an extensible environment to quickly prototype our debugging architecture. More concretely, the Pharo platform reifies the stack of execution as Pharo objects, and provides a debugger written in Pharo itself which is accessible and can be extended. We strongly believe that the out-of-place debugging architecture can be implemented for other programming platforms that (1) provide reflective capabilities that reify the execution stack and provide access to a debugging interface, or (2) allow to introduce virtual machine modifications (when reflective capabilities are limited).
the IDRA Changes Handler, an instance of both Changes Handler and Updater The IDRA Manager allows debugging different sessions coming from different connected IDRA Monitor(s). The communication between the different instances happens either via message passing on TCP sockets, where the connection needs to be kept alive, or via REST HTTP calls through the Zinc HTTP library  [37]. All data serialization happens through the object serialization library Fuel  [8] , which is able to serialize standard user-created objects as well as meta-objects such as the call-stack. The IDRA Changes Handler is also deployed on both application and debugger processes, and is capable to exchange code changes between two (or more) instances. Communication between the two IDRA Changes Handlers also happens by means of the two aforementioned networking technologies.
Handling of Exceptions and Code Changes Inspired by the Smalltalk tradition, IDRA models both breakpoints and unhandled exception as exceptions which suspend (halt in Smalltalk terminology) the program execution. In order to potentially handle, on the application side, concurrent exceptions and, on the debugger side, debugging sessions from different monitors, both IDRA Manager and Monitor handle exceptions asynchronously. To this end, IDRA Manager and IDRA Monitor share the same basic mechanism: they feature a queue, called sessions queue, where debugging sessions are stored. The IDRA Monitor queue actually stores the debugging session created upon an exception which is then later sent to the IDRA Manager. On the other hand, the IDRA Manager pops debugging sessions from its queue to open a debugger to the user.
Handling Remote Resources We now detail how we implemented the two strategies for handling remote resources described in section 3. 3. 2: object substitution during serialization (when we are transferring an already referenced remote resource) and code instrumentation (when a remote resource needs to be opened remotely). We implemented in our prototype a proof-of-concept to handle remote files. Our current implementation does support read streams but not yet write streams, nor other external resources such as socket streams or sensors; this is ongoing work. Object substitution During the serialization of a debugging session, while navigating through the stack, we substitute all the instances of FileStream with a corresponding RemoteFileStream object, which will contain information on how to set up a proxy.
Code instrumentation We implemented code instrumentation using meta-links  [6] , specialized meta-objects that control the execution of AST nodes. During execution, a metalink can execute its code before, instead or after the execution of its annotated AST node. This facility gives fine-grained instrumentation at the sub-method level. We use metalinks to transparently replace all accesses to external resource classes by accesses to a corresponding Remote* proxy class. In our concrete implementation for files, we replace all accesses to the File class by calls to RemoteFile. On the first access, such RemoteFile will retrieve from the IDRA Manager the necessary information to setup a proxy with the remote application. Then during debugging, the RemoteFile proxy class manages the access to the remote file.