dataesr/scientific-paragraphs-categorization huggingface dataset:
They found that inhalation exposure was negligible compared with dermal exposure, and that hands were the most exposed body part. Using exposure data from the Agricultural Handler Exposure Database (AHED) and the Pesticide Handler Exposure Database (PHED),  Pouzou et al. (2018)  created probabilistic estimates of exposures. Even if the dermal route of exposure contributes to most of the total doses most of the time, in some of their simulated cases, the amount of pesticides absorbed via dermal route was exceeded by the amount of pesticides absorbed via inhalation  (Pouzou et al. , 2018). While empirical statistical models consider a linear relationship between the amount of pesticide applied and the amount of exposure, the probabilistic estimations highlighted the variability of the relative contribution to the dose between dermal and inhalation exposure.
There are very few models able to produce probabilistic estimates of human health risk and exposure to plant protection products, since traditional models/databases used to generate summary statistics for occupational risk assessments of pesticide handlers in official calculations  (Van den Berg et al. , 2016). The BROWSE simulation also showed that exposure by ingestion was low (only 2%) and only detected for operators; it was not observed in the resident populations. Dermal exposure on the hands may result in exposure by ingestion through hand-to-mouth contact, especially for farmers (operators) that are routinely exposed to pesticides  (Butler Ellis et al. , 2017b). However, it should be noted that the BROWSE model is a more conservative model than other risk assessment models, and the predicted amounts of pesticides absorbed might be overestimated.
Funding information The authors received financial support from the Directorate General of Scientific Research and Technological Development (DGRSDT-MESRS_Algiers) within the framework of the PRIMA Foundation, call 2019-Section 2-BIODIVERSIFY "Boost ecosystem services through high Biodiversity-based Mediterranean Farming systems"-project. This work was partially funded by the PRIMA Foundation, call 2019-Section 1-GA n. X "Research-based participatory approaches for adopting Conservation Agriculture in the Mediterranean Area-CAMA". This paper has been supported by the RUDN University Strategic Academic Leadership Program (Rebouh Yacer and Latati Mourad: Soil analysis). CRediT authorship contribution statement B. Zemmouri: data collection, field experiment management, data analysis and writing original manuscript.
Wetland Soils Denitrification Model The WSDM was developed specifically for natural wetland ecosystems where complete denitrification occurs  (Adame et al. , 2019; Guilhen et al. , 2020; Martínez-Espinosa et al. , 2021). The WSDM aims to model denitrification processes in wetland soils, influenced by three main physical parameters; soil moisture, temperature and nitrate availability. The WSDM model simplifies the denitrification process by forcing available daily spatialized data to enable large scale application. A software was developed to harmonize the different databases and to carry out the simulations. The model is developed in Python programming language, using standard scientific computation and visualisation libraries with an open-source license. The software is developed in a functional approach with separate modules for pre-processing, simulation and post-processing.
Model implementation WSDM needs two kinds of spatialized data. On one hand, static datasets define the wetland typology and extension, and the soil characteristics (i. e. texture, bulk density, organic carbon content). On the other hand, dynamic datasets (soil moisture and soil temperature) will lead the nitrification and denitrification processes. Before running the physical-model, a pre-processing phase is carried out. The user then has to define the Region of Interest (ROI), giving two latitude and two longitude points indicating ROI limits, followed by the starting and end date of the simulation period. Once these criteria are established, simulation is launched. Spatial harmonization of databases is the first step to have n-dimensional data on a regular grid. Soil characteristics database -WISE30sec  (Batjes, 2015)  was the reference grid, with a spatial resolution of 30 arcsec (1 km 2 ), as well as Global Lakes and Wetlands Database (GLWD) (1 km 2 )  (Lehner and Doll, 2004).
Brightness soil temperature and root zone soil moisture data  (Al Bitar et Mahmoodi, 2020; Al Bitar et al. , 2017)  have an original grid of 25 km 2 , therefore a linear interpolation, using (https://scipy. org/) scientific python library is computed in every simulation, before running the physical model. The workflow summary WSDM processes can be seen in Fig. 1. The physical model has different functions that are explained in the next subsections. The limitation of the approach is detailed in the discussion section.
Nitrification will be active if soil moisture is low, if soil moisture is high, no nitrates will be created and no denitrification will be recorded, until that condition changes. Following the workflow, day i+1 soil moisture will define whether nitrification or denitrification occurs. This loop continues run until the end date of the defined simulation period is reached. modified and tested at larger scale, to calculate the dynamic potential denitrification of flooded areas using satellite data  (Guilhen et al. , 2020) , and subsequently for daily denitrification quantification on floodplain areas in different watersheds under tropical, temperate and arctic conditions  (Fabre et al. , 2020). These advances were key evidence to adopt this denitrification model into the WSDM as follows, Dp = 0.
Model performance with respect to field data The eight soil samples analysed are heterogeneous with soil properties ranging from low organic carbon content (mineral soils) to rich organic soils. Even though the study area is small compared with the Amazon watershed dimension, the freshwater marshes and flooded forests, (the main freshwater wetland typologies of this basin), were sampled in each of the three main tributaries (i. e. Negro, Solimões and Madeira rivers). This study is not intended to better understand denitrification in the Amazon, but to use Amazon soils, as examples of freshwater wetlands (flooded forests and freshwater marshes) under conditions of low anthropogenic impact, with a bimodal hydrological cycle. These samples were key to calibrate the WSDM and were intended to model complete denitrification rates on a large scale considering wetland heterogeneity (i.
Both models have an empirical base that operate at watershed scale and demand copious input datasets, that may not be available for all tropical wetlands. WSDM, is complementary to both models, taking into account different wetland ecosystems based on the vegetation structure, but with a simplified approach that is useful on a large scale. The results are given in terms of total denitrification, aiming to contribute with a particular vision of natural wetlands and their positive role in the global nitrogen cycle. This natural wetland function via complete denitrification is often overlooked. The total denitrification accounts for non-harmful emissions (N 2 ), which implies the subtraction of nitrate flux into aquatic ecosystems (i. e. rivers, lakes, coastal areas).
Acknowledgements The research presented in this paper is funded by  CONACYT  (The Mexican  National Council for Science and Technology)  in the frame of a  PhD research grant (Scholar/Scholarship reference :  625261/471711 2017-2021 ) at  Université Toulouse III -Paul Sabatier, Toulouse, France. We acknowledge the financial support from the  TOSCA-SOLE  project from  French Space agency CNES, France. We acknowledge the financial support for research provided by the  Agence Nationale de la Recherche (ANR)  through an  Investissement d'Avenir grant  ( Labex CEBA ,. We thank the reviewers for their comments and contributions that improved the manuscript, to  Elisa Domínguez Hüttinger  for her mathematical writing consultancy and to  Lisa Ernoul  for proofreading English.
The International LOFAR Telescope is an interferometer with stations spread across Europe. With baselines of up to ∼2000 km, LOFAR has the unique capability of achieving sub-arcsecond resolution at frequencies below 200 MHz. However, it is technically and logistically challenging to process LOFAR data at this resolution. To date only a handful of publications have exploited this capability. Here we present a calibration strategy that builds on previous high-resolution work with LOFAR. It is implemented in a pipeline using mostly dedicated LOFAR software tools and the same processing framework as the LOFAR Two-metre Sky Survey (LoTSS). We give an overview of the calibration strategy and discuss the special challenges inherent to enacting high-resolution imaging with LOFAR, and describe the pipeline, which is publicly available, in detail.
Despite these challenges, we have made a substantial amount of progress over the last few years. The Long Baseline Calibrator Survey (LBCS;  Jackson et al. 2022 Jackson et al. , 2016 ) is now complete and provides a database of calibrator sources over the northern sky. We have developed a calibration strategy and built a pipeline to carry out this strategy, which forms the basis of high-resolution imaging with LOFAR. Additionally, initial tests have shown that we are able to image targets within LOFAR's wide field of view at sub-arcsecond resolution. This is the first in a series of papers describing highresolution imaging with LOFAR. In this paper, we will cover the overall calibration strategy that will prepare the data for high-resolution imaging. Paper II is a companion paper, which covers the complete Long Baseline Calibrator Survey.
2022). It is useful to correct all instrumental effects before attempting to solve for DDEs. Thus we would like to independently correct for the delays introduced by the clock offsets. This can be done using an algorithm developed by M. Mevius and implemented in the LOFAR Software SOlutions TOol (LoSoTo; de  Gasperin et al. 2019). The algorithm uses the fact that the clock delays are non-dispersive, dφ/dν = constant, while the ionospheric delays are dispersive, dφ/dν ∝ ν -2 , to separate the effects in solution space. This separation is accomplished by simultaneously fitting for these two different behaviours in phase solutions. PREFACTOR performs this on the standard flux density calibrator, where the signal-to-noise is high. The clock offsets can then be transferred to the target data. Clock offsets for the international stations are typically ∼100-250 nanoseconds.
We found that all of these methods produced very similar results, with only small quantitative differences arising from differences in the data preparation and/or algorithms used to find the delays. However, although the ideal situation would be to solve for clock (here we mean the clock drift as the offset is already corrected) and TEC values separately, the clock/TEC separation does not work in all cases on in-field calibrators. The pipeline therefore currently only solves for the dominant effect, which is the dispersive delay (TEC; option iv above). Using the dedicated LOFAR software tool to do this has the advantage that we do not have to process data through software that is not optimised for LOFAR data. By this, we refer both to the fact that LOFAR observations produce large data volumes, which AIPS and CASA are not necessarily equipped to handle efficiently, and the fact that conversion between data formats and use in other software runs the risk of meta-data being missing or not properly handled.
LOFAR-VLBI pipeline In this section, we describe each step of the pipeline in detail. In this paper, we use the V3. 0. 0 release of the LOFAR-VLBI pipeline, which was run using a Singularity image. Singularity is a software container, which allows users to access a virtualised operating system that has all necessary software and dependencies in it. Information on software requirements, including how to access the specific singularity image we use 8  , can be found in the documentation online  9. Appendix A provides basic pipeline profiling. Fig. 5. Comparison of the ST001 primary beam with a single core station primary beam. The beam profiles were created by imaging with natural weighting using all the core stations (ST001) and only the two-substations in a single core station (CS001). Only 10 iterations of WSCLEAN were run to provide an estimate of the PSF in both cases.
g. 3C sources) that may also be suitable calibrators. It is necessary to query both databases as the LBCS catalogue does not contain flux density information, although this can be estimated based on the signal-to-noise to provide an approximate flux density measurement. For areas of the sky that are not yet fully covered by LoTSS, the user can generate their own catalogue and point the pipeline to a local file, specified by the lotss_skymodel parameter in the pipeline. If a local file is specified and exists, the pipeline will use this instead of querying the LoTSS database. For this paper, there was incomplete coverage of the field in LoTSS DR1 and we generated our own catalogue from the upcoming LoTSS Data Release 2  (Shimwell et al. 2022). The pipeline cross-matches the LBCS and LoTSS (or usergenerated) catalogues and writes out several key results.
It will not always be possible to do this as fainter sources may not have enough signal-to-noise to produce good solutions. In this case, solutions from the nearest LBCS calibrator can be found and applied to the science target before imaging. This is not currently a step in the pipeline and will have to be completed as a post-pipeline step (see Sect. 5). The residual delays are applied to the DOI and then selfcalibration is performed using DIFMAP  (Shepherd 1997) , called from a python wrapper. DIFMAP is a purpose-built program built specifically for self-calibrating VLBI data, which was originally named after the 'difference mapping' technique it uses. During a standard run of the CLEAN algorithm, a model of the source(s) is built up by subtracting components iteratively from the residual map.
This continues until the user stops the algorithm or a pre-set threshold has reached. Modifications to the model that is built are not allowed during the process; the user must start again if they find that CLEAN has, for example, included too many imaging artefacts in the model. Difference mapping, on the other hand, allows the model and/or the visibilities to be modified during the CLEAN process, by updating the residual map. This is calculated using the 2D Fast Fourier Transform (FFT) of the difference between the model and observed visibilities. Thus artefacts can be removed from the model as CLEAN progresses, whereas the standard implementations of CLEAN must re-start the entire process. The DIFMAP software is very compact and efficient, and typically more than an order of magnitude faster than other available tools.
The disadvantages are that it is relatively rigid, and concentrates on doing only a few things really well; it does not write out corrections (only corrected data); and it self-calibrates amplitudes and phases but not delays. However, the pipeline corrects for delays by solving for and applying dTEC before passing the data to DIFMAP, and we found that the self-calibration out-performs our previous version using DPPP and WSCLEAN  (Offringa et al. 2014; Offringa & Smirnov 2017). The DIFMAP solutions are written out using a modified version of the CORPLT task, which is included in the singularity image. The python wrapper around the DIFMAP self-calibration converts the measurement set to uvfits format using ms2uvfits with writesyscal=F. Any completely flagged channels of data are identified, as these will cause DIFMAP to crash if they are processed, and a list of good channels is stored.
The python wrapper writes a DIFMAP script and then calls it to select the data (Stokes I) and perform the self-calibration. The following steps are performed. First, a point source model is used for an initial self-calibration. Then, three rounds of phase self-calibration are accomplished with different u-v weighting schemes. The clean/self-calibration loops proceed until the peak residual drops below a set fraction of the rms noise. After this, an overall amplitude scaling is performed followed by amplitude self-calibration with steadily decreasing solution intervals, and intervening phase self-calibration if the signal-to-noise of the output drops. The final clean map is generated and saved to a FITS file and an ASCII table of phase and amplitude corrections are written. We note that this self-calibration scheme is only likely to work for strong sources, and we implement extra post-pipeline steps described later in Sect.
We combine these together by taking the average of antennas that participated in the observation. We selected a cutoff of 4 by plotting the calibrator selection statistic described in Sect. 4. 1 against the combined FT Goodness, and found that below 4 this statistic began to rise steeply, while above 4 the statistic was fairly flat, indicating good candidate calibrators. There are six calibrators that fit these criteria in P205+55. This strategy would also be appropriate for the simpler scenario of Case 1. Following self-calibration on the LBCS calibrators, we will finally image several directions in the field as test science targets. To do this, we use tools that already exist in the pipeline (e. g. software and scripts called by the pipeline) and WSCLEAN for imaging.
The noise achieved is 99 µJy bm -1 with a beam size of 0. 29 × 0. 21. We also show ILTJ134255. 151+541432. 752 overlaid on optical data in Fig. 14. The optical data consist of g, r, z bands from the Legacy Surveys, and is smoothed with a 2D Gaussian kernel (width = 0. 8 ) to bring out the detection of the faint optical host, which is clearly identified in the WISE 3. 5 µm band. This optically faint galaxy has a photo-z of 1. 2 in the Legacy Surveys Data Release 8 database. There are two possibilities for the radio core in this image, which could be confirmed by aligning the source with higher-frequency images. We searched for higher-frequency interferometric radio images, but although this source is detected in FIRST the resolution is not high enough to distinguish a core. To further demonstrate the pipeline we image in a number of different directions.
As these steps were based on the particular sources and their locations relative to each other in the field, they have not yet been included in the pipeline. Although we have tested it on HBA data, the strategy is similar 12 https://lofar-vlbi. readthedocs. io/en/latest/ enough to that used by  Morabito et al. (2016)  that it should in principle work for LBA data as well. The final images reach ∼90 µJy bm -1 noise with an average restoring beam of 0. 3 × 0. 2. We presented the images of the delay calibrator, secondary LBCS calibrators, and a few sources in the field. For the delay calibrator, we demonstrated how the astrometry and flux density scale can be checked and, if necessary, corrected, using LoTSS as a reference. We found good agreement in the flux density scale, with only ∼5% difference from a line of equal total flux density.
This will include work to ensure that the pipeline can be integrated seamlessly with the overall efforts for LoTSS data processing, which include wide-field LOFAR-VLBI techniques. In the near future, we plan to start post-processing ∼100 pointings from LoTSS, which will help us refine and improve the pipeline, in addition to starting to build up the first sub-arcsecond radio survey of the entire northern sky. This survey will be a high-resolution (HR) extension to LoTSS, namely LoTSS-HR. Figure  16  shows a comparison of radio surveys with respect to resolution, sensitivity (projected at 144 MHz, assuming a typical synchrotron spectral index of α = -0. 7), and sky coverage. LoTSS-HR will be the only survey with resolutions comparable to wide-area optical surveys, and is expected to cover ∼50% of the entire sky (>0 • declination).
Acknowledgements. The authors would like to thank everyone who has helped develop the software and pipelines used in this paper. This work would not have been possible without tireless efforts to help update and maintain PREFACTOR and DPPP. The authors are grateful for many useful conversations with K. J. Duncan. This work made use of several different computing resources and we thank the administrators and technicians who have helped us with these resources. This work made use of the  University of Hertfordshire  high-performance computing facility (http://uhhpc. herts. ac. uk) and the  LOFAR-UK computing facility  located at the  University of Hertfordshire  and supported by  STFC  [ ST/P000096/1 ]. We would like to thank the  Christ Church Research Centre  for a grant which provided the disk space necessary to process this data.
Here N is the total number of observations: x := (x (1) ,. , x (N ) ) iid. We test this algorithm on synthetic data. With real values α = 2, λ = 5, σ = 1. 5, we generate a dataset with N = 100 observations and run the Riemann EM with random initialisation. This simulation is ran 2000 times. We observe that the Riemann EM is indeed able to increase the likelihood, despite the EM being originally intractable. On Fig. 1 , we display the average trajectory, with standard deviation, of the negative loglikelihood -ln (g(θ)) during the Riemann EM procedure. The profile is indeed decreasing. The standard deviation around the average value is fairly high, since each run involves a different dataset and a different random initialisation, hence different value of the likelihood, but the decreasing trend is the same for all of the runs.
For each of the three parameter families represented on Fig. 4 , 1000 datasets with 500 observations each are simulated, and the two EMs are ran with both the barycenter and the 2v1 initialisation. Regarding the temperature profile of tmp-EM, the only constraint is that T n -→ 1. We use an oscillating profile inspired from Allassonnière and Chevallier (2019): T n = th( n 2r ) + (T 0 -b 2 √ 2 3π ) a n/r + b sinc( 3π 4 + n r ). Where 0 < T 0 , 0 < r, 0 < b and 0 < a < 1. The oscillations in this profile are meant to achieve a two-regimes behaviour. When the temperature reaches low values, the convergence speed is momentarily increased which has the effect of "lockin-in" some of the most obviously good decisions of the algorithm. Then, the temperature is reincreased to continue the exploration on the other, more ambiguous, parameters.
The results over all simulations are aggregated in Table  1 , and confirm this observation. Table  1  presents the average and the standard deviation of the relative l 2 error on µ k of the EMs. For each category, the better result over EM and tmp-EM is highlighted in bold. The recovery of the true class averages µ k is spotlighted as it is the essential success metric for this experiment. First we focus on the effect of the different initialisations and placement of (µ 1 , µ 2 ) on the performance of the classical EM. In the first parameter family of Table  1 , µ 1 and µ 2 are still far from one another. The relative error on these two positions is around 0. 50 when the initialisation is a the neutral position at the barycenter of the dataset, and 1. 50 when the initialisation is made by placing two centroids in the right cluster (2v1 ), which is a much more adversarial initialisation.