dataesr/scientific-paragraphs-categorization huggingface dataset:
Patient image data-sets were cropped to the VOI and the iterative template creation was repeated, using WAIR T1 3 as first registration reference, and the cropped WAIR volumes as active image data-sets. The method was iterated four times (Fig. 4B ), resulting in four WAIR-led non-linear templates (Fig. 4A ) per modality denoted T1 WAIR 0→3 and WAIR WAIR 0→3 and their associated labels, Labels WAIR 0→3. All the processing was run on a 32-core AMD 2990WX 3. 80 GHz workstation equipped with 128 GB of RAM running Debian Linux (debian. org). All the tools and software were setup in a docker environment based on the NeuroDocker project (https://github. com/ kaczmarj/neurodocker). Most of the normalization processing time was spent in this non-linear atlas creation step. Time required to run the non-linear normalization step (Fig.
• DRAMMS (Ou et al. , 2011) uses texture information with Gabor filters applied on moving and reference images in different orientations and with central frequencies to measure the similarity between images. The set of Gabor filters used in fine during the registration is optimized in order to use only the most appropriate set of filters, enabling multi-modality registration. Mutual-Saliency is used to weight the volume based on the similarity metric. This allows to relax the optimization in areas where differences are more important e. g. lesions, tumor, missing tissue, cysts. For each of the tools under test, the default settings were evaluated. For ANTS, the setting used in the MultiModalityTemplateContruction2  (Avants et al. , 2010b)  script were considered as the default settings as none are set by default in the registration program.
The results are presented again as violin plots with the same bandwidth settings as previously. Lastly, in order to provide a more visual and qualitative representation of the normalization results, the cumulative definition of structures is overlaid to the anatomical template images. The cumulative definition of several anatomical structures are however difficult to visualize at the same time, because of the number of structures in this dataset, and the overlap of the heatmaps in their peripheral parts. To make this visualization possible, the cumulative definitions are resolved into binary labels using a relative threshold set to 50% of the max intensity in each heatmap. This is equivalent to majority voting across patients on the inclusion of a voxel in a label class.
Qualitative evaluation For visual comparison purposes, a coronal slice from the original WAIR dataset from one PD patient is presented in Fig. 7 , together with slices of each of the best templates at the location of the largest crosssection of the STN for ANTS, FNIRT and DRAMMS. The three template images (Fig. 7 , ANTS, FNIRT, DRAMMS) present much smoother contrast than the individual patient's image. Compared to ANTS, both FNIRT and DRAMMS are smoother and present less contrast dynamic, resulting in increased difficulty to identify intensity differences. The superior quality of the template created by ANTS compared to the two others was confirmed by the neurosurgeon after inspection of each volume data-set. The cumulative definition of STN is different across templates, ANTS and FNIRT both have maximum cumulative value of 11, while DRAMMS has 10.
Group template creation Several researcher have proposed group templates created from different input data and approaches. Nowinski et al. (2005)  normalized MRI from 168 patients who underwent bilateral STN implantation based on Nowinski's Fast Talairach transformation method. The result was a probabilistic functional atlas of the STN. However, the use of linear transformations as opposed to non-linear transformations is described as a limitation by the authors of the study. Shattuck et al. (2008)  presented the creation of an anatomical template based on T1 images from 40 patients, comparing AIR, FNIRT and SPM5. The study was focused on cortical structures labeled by two different experts and not on the deep brain. The approach only consisted in a single registration of each patient with each registration tool under test, to a different reference for each tool.
For the structures indiscernible on any modality, the histological atlas from  Chakravarty et al. (2006) , after conversion to a pseudo-MRI image, was warped to the previous template, providing a definition of the missing structures. Very recently,  Ewert et al. (2019)  compared ANTS, FNIRT, SPM12 and the "linear threestep" method implemented in Lead-DBS  (Horn et al. , 2019). Their comparison was conducted for the task of registering patients to an existing template. The manual segmentation by two different experts of two structures (STN and GPi) per hemisphere served to measure registration accuracy. In contrast to the studies described above, our work describes the creation of a group specific template based on multi-modality nonlinear registration using coherent data from a single center and expert  (Lemaire et al.
Registration tool selection In the present work, we compared the performance of ANTS, FSL's FNIRT and DRAMMS for the task of creating group-specific templates of the deep brain. The study from  Klein et al. (2009)  describes ANTS as the best performer. Based on this conclusion many studies used ANTS and for that same reason, we included it in the comparison. The second tool selected was FSL's FNIRT considering the popularity of the software package FSL, and lastly, the DRAMMS tool was added as fewer information about its performance could be found. The registration tools were evaluated in their default settings as well as those from  Klein et al. (2009)  and  Ewert et al. (2019)  when applicable. The well-established Statistical Parametric Mapping toolbox (SPM,  (Ashburner and Friston, 1999) ) registration, used in several other studies (e.
g. (Ewert et al. , 2019; Klein et al. , 2009) ) was however not used in this work. SPM registration using the "DARTEL" and "Normalize" functions takes a significantly different approach and requires the application of the "NewSegment" function first, which creates a segmentation of the different tissues based on a Tissue Probability Map (TPM). The default TPM shipped with the toolbox only defines the white matter, gray matter and CSF areas, which makes registration of the subthalamic area ambitious. Lorio et al. (2016)  and  Ewert et al. (2017)  proposed different refined TPM focused on the subthalamic and basal ganglia areas, however defining only a small subset of the anatomical structures available in our dataset. Moreover, the use of this method requires prior information of the subthalamic/basal ganglia area in the form of this TPM, which makes it difficult to compare with the tools used in the present study in a fair way.
Conclusion A workflow for multi-modality iterative anatomical template construction was introduced and the performance of three non-linear registration tools (ANTS, FSL's FNIRT, and DRAMMS) was evaluated for that purpose using MRI of the human deep brain. The quality of the registration was evaluated using labels of the anatomical structures from the thalamus and sub-thalamus produced by single-expert manual segmentation. ANTS using the "high variance" settings from  Ewert et al. (2019)  produced the results of the best quality. Future works will determine the number of patients necessary to achieve a reliable template that represents a group accurately. Apart from creating a representative anatomical template, other patient data can now be transferred in the normalized template space in the same way as the labels of the structures.
Pesticide transfer models need to know when each active ingredient (AI) is applied and the quantity applied. However, unlike the other data needed for implementing models (climate, soil characteristics or land use), there is no database available on phytosanitary practices that can directly feed the models  [6] [7] [8]. In addition, there are few attempts to synthesise these practices at the catchment scale. This deficiency led to the development of new methods of acquiring and processing data  [6, 7]. In the PIREN-Seine programme, studies were conducted to better define crop rotations and landscape diversity in the Seine catchment area. Agricultural practices were investigated, and the first models simulated nitrate contamination behaviour in groundwater  [9]  and surface water  [10]. New developments have been carried out to integrate pesticide behaviour, taking into account farmers' phytosanitary practices.
Study Sites The pesticide fate model was designed to gather input databases such as soil characteristics, climatic data, pesticide properties and phytosanitary practices. Given that the database is not available for the latter, an estimation of annual quantities of each AI, the application schedule and spatial distribution of application on the catchment must be at least documented. The effort needed for inquiries on pesticide applications is directly related to the size of the watershed. Therefore, the Orgeval site was considered for a long-term study, while the Vesle site was chosen for a spatial approach.
Data Acquisition To study long-term contamination by pesticides, we focused on atrazine. In the Seine River and some aquifers of the basin, atrazine and its by-product DEA are currently detected in rivers and groundwater even after its ban in 2003. Atrazine is a herbicide widely used on maize as well as vineyards. It was also used on urban areas for weed control on roads and railways. In France, pesticides are monitored in rivers and aquifers for the European framework directive. However, such data is generally partial (monthly, half-yearly, annual or less), and further investigations were conducted. In the Orgeval basin, within the PIREN-Seine programme, a flow-controlled refrigerated sampler has been installed since 2008 at the Avenelles station (see Fig. 1 ). Since then, samples have been collected once a week and then gathered monthly for pesticide analyses.
A survey of the crop management techniques of the main farms was also carried out  [12]. Pesticides were then extracted with an offline solid-phase extraction (SPE) technique with the Oasis hydrophilic lipophilic balance ® (HLB, Waters) cartridge and analysed by liquid chromatography with tandem mass spectrometry (LC/MS/MS)  [13, 14]. Detection limits are 1 ng L À1 for both atrazine and DEA. In the Vesle basin, atrazine and DEA concentrations were extracted for 39 piezometers from the ADES database  [15]. Additional specific sampling campaigns were carried out monthly from November 2003 to February 2005 on 11 piezometers by the Agence de l'Eau Seine-Normandie (Direction Territoriale des Vallées d'Oise, DTVO). This covered a larger geographic area and included more specific hydrogeological contexts.
) and his/her personal choices (sowing date, crop variety, tillage practices, etc. )  [16] [17] [18]. Then, for the same crop, pesticide use may differ from one farm to another or even from one field to another. This diversity could explain 40-60% of pesticide use  [16]. Moreover, pesticide use evolves rapidly over time, depending on the regulations (banned or new AI), climate conditions (which cause diseases and pests to vary) and rotation crops  [17]. The main source of information on crop rotation in the two study areas is the French Agricultural Census, which has been available at the municipal level since 1970. It is updated only every 10 years, but annual data were estimated by interpolation using annual data available at the regional scale between these dates  [19] [20] [21]. With regard to pesticide use, there are few generic databases that can be used over a long time period and on large study sites.
The E-Phy database  [22]  lists the plant protection products authorised in France, as well as their composition, homologation dates and actual registered uses and doses. However, these data cannot be directly used because farmers can choose to use one AI or another. The national sales made by distributors' database (BNV-d), which includes pesticide sales, are another source. This database could be used to spatialise pesticide use  [23, 24] , but data have been available only since 2008. To better define model inputs, a "hybridization approach" using national resources and local data was developed according to two methods  [6]. Since the Orgeval catchment is only about 100 km 2 , all agricultural practices were monitored over a period as long as possible for the whole watershed. The Vesle catchment is too large, and therefore only herbicide use has been investigated within a 30-year period on targeted practices (weeding vineyards and corn) for which the use of these AIs was approved.
The Orgeval Case For the Orgeval basin, other data were available to refine the spatial accuracy of the annual rotation. First, rotation tables were provided directly by farmers, describing their crop succession on their plots (771 plots accounting for 45% of the landscape area, dating from 1970 to 2005). Moreover, data from the French Land Parcel Identification System (LPIS) geographic database [25] has been available since 2006. Here, we chose to collect records of their past practices directly from farmers located in the study area. These documents, called "lowland logbooks" or "field logs", help to get closer to farmers' "real" practices and to reconstruct the dynamics of phytosanitary practices over 20 years for a representative sample  [26]. As an example, the chart in Fig. 2  shows that atrazine was long the main AI applied on maize until its ban in 2003.
However, the chart still shows some applications beyond this date, showing the authenticity and low bias of the data recorded. High-resolution data could capture spatial variability but at the cost of intensive computing  [27, 28]. Therefore, we assumed a type of simplification: to keep confidential and anonymous data and also to simplify modelling input; these data were averaged by crop and by year for the whole basin in the final database. This APOCA (Agricultural Practices of the Orgeval Catchment Area) database was used to quantify the AIs applied per crop per year since 1990. It also includes a large number of indicators to monitor changes in phytosanitary pressures  [26]. Comparison of these indicators shows that the phytosanitary pressure is not decreasing overall even if applied quantities per hectare are lower.
This approach made it possible to better simulate long-term desorption from bound residues. A detailed description of the module is given in  [41]. Five categories of parameters have to be described to implement the model: climate data, soil parameters, cultural practices (sowing and harvest dates, crop management), crop parameters (main crop rotations) and pesticide parameters. For the soil parameters, the field capacity, wilting point and bulk density are derived from the national database related to soil description and the characteristics of the French soil  [46]. The organic carbon profile is related to field data and laboratory measurements  [47]. Agricultural practices were extracted from the APOCA database. Atrazine inputs were calculated using phytosanitary practices provided by farmers (applied quantities on maize, application date) according to percentages of maize over the 1990-2009 period.
Pesticide characteristics are mostly extracted from international databases such as  [48]  or previous studies including field measurements with pedoclimatic conditions similar to the Orgeval catchment  [49, 50]. Moreover, the transformation of atrazine to its metabolite DEA is estimated with the first-order kinetics and maximum occurrence fraction estimated at 0. 21  [48]. Several simplifications and assumptions were made in this study. The soil characteristics were assumed to be homogeneous at the catchment scale. Given that more than 80% of the area is artificially drained, we assumed that the residence time of water is equal to or less than 1 year. Consequently, the simulation and observed data were compared on an annual scale. Moreover, since we focused on atrazine behaviour, maize was the only crop considered.
It therefore requires three kinds of input: (1) climate data from the SAFRAN atmospheric analysis system  [55, 56] , (  2 ) soil data at the 1:1000000 scale  [57]  for which parameters are estimated through the use of pedo-transfer functions  [58] [59] [60] [61]  and (3) crop rotation and management  [29, 62, 63]  for which pesticide use was computed with crop rotation in the ARSEINE database  [21]  over a  45-year period (1970-2015)  in Fig. 6. MODCOU  [64, 65]  is a regional spatially distributed model which describes both surface and underground water flows at a daily time step. Mainly conditioned by land use, climate and parent soil material, surface water balance calculations are based on a conceptual reservoir-based approach  [66]. Infiltration leaks through a vadose zone were implemented using a set of reservoirs introducing a delay between the infiltration and the aquifer recharge  [67, 68].
Pesticides at the Seine River Basin Scale At this stage of the modelling procedure, the results obtained on the Vesle basin show that it is possible to simulate the transfer of atrazine in a soil-groundwater-river system. However, considering the Seine River basin, various residence times in the vadose zone and aquifers make modelling more complex, and the mosaic of cropping systems must be associated with the diversity of soil types and the thickness of the unsaturated zone  [21, 78]. In the first step, a prospective approach was implemented to understand pesticide contamination of groundwater in the Seine basin, again using the ADES database  [15] , in which pesticide contamination has been documented since 1997. As DEA is the most widely detected pesticide residue in water, it was possible to use this national monitoring database to understand the temporal evolution of the contamination.
The mean concentrations of two different periods were compared: the first one from 1997 to 2003 corresponding to the use of atrazine (Fig. 8 , left) and the second one from 2006 to 2014 (Fig. 8 , right). Data between 2003 and 2005 were considered as a transition period when atrazine was still used by farmers (finishing stocks). DEA was analysed on 1,436 piezometers, but many wells presented values below the limit of quantification. A value reported as below the limit of quantification was assigned a value of one-half of the limit of quantification. As this limit decreases over time from 0. 1 to 0. 005 μg L À1 , then the mean values can decrease over time due to the limit of quantification even if DEA was never detected. To better represent the evolution of DEA contamination, only mean values over 0.
11 μg L À1 were considered (Fig. 8 ). This approach allowed us to focus on contaminated wells. Mean ). Observed data were extracted from the HYDRO and ADES databases  [15, 77]  concentrations over 0. 2 μg L À1 are mostly observed in the Brie, Beauce and Champigny aquifers (see locations in Fig. 8 ) where maximum values were measured before 2003 (0. 91, 1. 8 and 1. 42 μg L À1 , respectively). Some wells also show increasing concentrations of DEA over time in the Chalk and Champigny aquifers (i. e. from 0. 14 to 0. 18 and from 0. 20 to 0. 26 μg L À1 , respectively), whereas the Cretace aquifer shows more decreasing concentrations (from 0. 21 to 0. 12 μg L À1 ). However, this cannot be directly extrapolated for the entire aquifer, because the wells did not show the same trend. For the 2006-2014 period, the mean DEA concentrations were 0.
Conclusion Depending on the size of the watershed under study, pesticide use and database monitoring differ. • At the plot scale, crop rotations and phytosanitary use and application conditions are known and can be combined for precise and adapted monitoring. • In a catchment area such as Orgeval (100 km 2 ), the agricultural landscape is more complex. The observed contamination corresponds to major pesticide use. However, homogeneity is still observed due to a similar pedoclimatic context. The response time of the soil-groundwater-river system is fast, and contamination appears quickly and abruptly at the outlet because of drains. Major pesticide inputs can be identified using farmers' logbooks. • In the Vesle basin (1,000 km 2 ), the water transfer time from groundwater to the river is longer.
The landscape is contrasted, resulting in heterogeneous crops in the basin (alfalfa, annual crops and vineyards). Spatialisation of uses will be of particular importance in this context, and we need to combine agricultural practices (e. g. pesticide treatment) and soil characteristics. In this case, the random distribution of crops considered in our modelling approach of basins may have a considerable impact on the model's outputs. Vineyards receive specific treatments and are well identified in the Corine land cover database; its pesticide applications were therefore correctly located. Modelling also required estimating inputs over at least 30 years, to include the transfer time to the outlet. This retrospective raised the question of the initialisation of the model, which takes a considerable part in the uncertainty of the simulation results.
Nevertheless, focusing on specific weeding practices in vineyards and field crops allowed us to estimate the uses of atrazine and to model its transfer. However, working at the sub-annual level would not be realistic at this time. • At the scale of the Seine basin (75,000 km 2 ), the hydrogeological context is complex, with several superimposed aquifers, different response times and a patchwork of cropping systems. Monitoring databases are those assembled by the national authority for European framework directive monitoring  [15, 81] , and they allow only scarce and discontinuous monitoring of pesticide contamination. However, considering only the residues of atrazine, which has been widely used throughout the basin and frequently detected, it is possible to show that some aquifers still have an increasing contamination of atrazine residues despite its prohibition in 2003.
In that case, other parameters must be taken into account such as trajectories of pesticide uses and landscape changes regarding agricultural activities. The new national BNV-D database indicating sales of pesticides, available since 2008 (identification of substances, annual quantities at the distributor scale), could also help characterise and spatialise pesticide inputs and these changes at the regional scale  [21]. This new database provides valuable perspectives for the characterisation of future uses. On the other hand, the characterisation of past practices, in particular those before the 1990s, remains difficult because of the absence of national databases and the disappearance of archival documents (re-structuring, destruction of old archives) and technical knowledge (retirement of advisors) on farms and advisory bodies.
Second, we assess and compare the response of neXtSIM-MEB to uncertainties in the surface wind forcing and in the sea ice cohesion. We also study the predictive skill in terms of the forecast of sea ice trajectories. The paper is organized as follows. Section 2 presents the methodology, based on the analysis of the ensembles of virtual drifter trajectories. Section 3 describes the experiment setup and how we generated the different sets of ensembles used in this study. Section 4 presents the results of the ensemble forecasts while a discussion is given in Section 5. Section 6 gives the conclusions and perspectives for the ensemble forecasting and data assimilation.