dataesr/scientific-paragraphs-categorization huggingface dataset:
Velocity picking is done after each major processing step in order to refine the final velocity model and build a coherent pre-stack Kirchhoff time-migrated section (PSTM). OBS WAS data processing included clock drift corrections, instrument localization using direct water wave arrivals to correct for the drift from their deployment position and eventually band-pass Butterworth filtering to improve travel time arrival identification and picking. We used the iterative two-dimensional forward ray-tracing procedure followed by the damped least-squares travel time inversion of the RAYINVR software  (Zelt and Smith, 1992). Our modeling proceeded following a top-to-bottom strategy of arrival times fitting of both wide-angle reflected and refracted phases identified on OBS data. Starting from an initial velocity model including the bathymetry, model interfaces were inserted when a velocity change in apparent refracted velocity and a coincident high-amplitude reflective phase were observed (i.
5 km s -1. The deeper crustal structure is poorly constrained (see MZ3OBS11 in Fig. 7 ). Though refracted phases cannot be identified, some higher-amplitude phases can be distinguished at specific offset ranges. Those can be explained as resulting from reflected waves from a layered medium similar to the one modeled for the LM. We can further interpret some far offset phases (e. g. , MZ3OBS10 in Fig. 9 ) as reflection from the Moho if considering a depth of about 35 km for this interface as suggested by the modeling of the crossing MZ2 and MZ6 profiles  (Schnürle et al. , 2018). Nonetheless, the poor constraints, we can reasonably consider the deep crustal structure beneath the NNV and ALR as similar to the western part of the LM up to the basement high. Our dataset reflects a thick crust with strong internal reflectivity, a low-velocity gradient and high bottom velocities for both domains (7.
10a ). Indeed, these regions of the model are less sampled by rays (Fig. 10b ), i. e. , usually from a single direction, and data quality is either poorer or degrades naturally with increasing offsets. Because the model parameterization has been adapted to these limitations, MZ3 shows overall limited smearing (± 3, Fig. 10c ) and very good resolution (above 0. 9, Fig. 12d ). The quality of the modeling decreases at depth and towards model extremities. Resolution values remain, however, higher than 0. 5, which are still considered acceptable. Greater smearing occurs essentially in the lower crust where only few refracted rays travel. Those have been difficult to identify on OBS records because many phases converge around to the triplication point with the PmP and Pn (see Sect. 3. 3).
Uncertainties estimation Velocity and depth uncertainties of MZ3 final velocity model were estimated using the VMONTECARLO code  (Loureiro et al. , 2016). VMONTECARLO explores the model space by generating random models. It evaluates the ability of each model to fit the observed dataset and translate it to estimates of uncertainties given some quality thresholds. Due to computational costs, the explored model space was reduced by limiting the number of parameters and fixing some bounds. For MZ3 we chose to maintain fixed sedimentary and volcano-sedimentary layers, as well as basement depth nodes, because those benefit from refined MCS constraints. This results in a total number of variable parameters of 148 as shown in Fig. B1. We also allowed a maximum fluctuation of ± 0. 50 km s -1 on velocity nodes while the maximum depth node variations were set to bands of 1, 2 and 3 km for the upper crustal, the 3 mid-crustal and the Moho interfaces, respectively.
MZ3 PSDM A pre-stack depth migration and a residual moveout analysis were performed in order to convert MCS data from time to depth and verify the accuracy of the wide-angle velocity model. The Kirchhoff pre-stack depth migration procedure is described in detail by  Schnürle et al. (2018) , and the resulting section for MZ3 profile is shown in Fig. C1a. As for the pre-stack time migration (Fig. 3 ), the full sedimentary cover is well imaged up to the top of the SV1 layer. Below, the section is mostly transparent with only intermittent and tiny events within the SV1 layer. In the common-image gathers (CIGs: Fig. C1b ), the top of Sv1 coincides with a strong low-wavelength reflector, followed by poorly coherent and under-migrated events. The semblance panels (Fig. C1c ) are similarly well focused in the upper sediment and at the top of the SV1 interfaces, whereas it is blurred at greater depth and beneath the ALR.
The most prominent basement high, located at 310-340 km model distance, is clearly an isolated basement structure controlled on each side by steep faults. To its SW, the fault zone appears wider at the basement surface, suggesting a deep fault rising as a flower system (Fig. 3 ). Internally, the acoustic basement shows a chaotic and incoherent signal in some places (60-120, 300-420 km, Fig. 3 ) but clear basement-parallel or dipping reflectors of variable amplitude in others  (-60 to -20; 120-260 km). This mixed nature already suggests that the top of the acoustic basement is not the roof of the crystalline basement, at least up to the basement high at 330 km where it is more homogeneous. Its internal structure is, however, only partially resolved by the MCS. WAS data further constrain a continuous, 3 to 4 km thick unit made of two layers (SV1 and SV2 layers in green in Fig.
3b ). They are, respectively, 1-1. 5 and 1-2. 5 km thick with velocities ranges of 4-5 and 5-6 km s -1 on average, though slightly lower values are observed beneath the ALR (Fig. 13 ). From its seismic facies and velocities we therefore argue for the presence of a deep volcano-sedimentary unit that is continuous from the NNV towards the LM. This unit possibly ends at the basement high at 330 km model distance. Beyond 340 km model distance, our velocity model only constrains a single layer (a prolongation of SV1 for modeling purpose), which is 1. 5 km thick with intermediate velocities of 5. 0-5. 5 km s -1. From these velocities and its homogeneous, high-amplitude but chaotic facies, we instead suggest the presence of extruded volcanism only at the NE extremity of MZ3. Commercial MCS data and companion PAMELA profiles complement and clarify the nature and extent of the deep volcano-sedimentary unit (SV1 and SV2).
Rift to drift evolution and vertical movements In the alternative East Gondwana "fit" proposed by  Thompson et al. (2019) , the continental domain to the southwest of the Astrid Ridge on the Antarctica plate is the conjugate of the African corner made by the MCP and the Beira High (Fig. 17a-b ). Further south, the Grunneghona craton is facing the NNV. The exact seaward limit of the Antarctica continental crust is inferred at the location of a strong positive free-air gravity anomaly  (Mueller and Jokat, 2019; Scheinert et al. , 2016)  since transitional crust is observed further offshore in seismic data  (Jokat et al. , 2004). At this stage, the Antarctica plate overlaps the corridor, which develops progressively during rifting (Fig. 17b ) and before oceanic spreading in the MB starts from Chron M25 (155 Ma) onwards (Fig.
Acknowledgements. We thank the captain, crew, and  MCS  technical team of the R/V Pourquoi Pas?. We also thank the OBS technical team who maintain and constantly improve our OBS pool, as well as the land stations deployment team. Angélique Leprêtre  and  Fanny Verrier 's respective post-doc studies and contracts were co-funded by TOTAL and Ifremer as part of the  PAMELA (Passive Margin Exploration Laboratories)  scientific project. We thank  WesternGeco ,  INP  and  Total  for giving us permission to make and publish line drawings of the seismic data. The GMT (Wessel and Smith, 1998),  Seismic Unix  (Cohen and Stockwell, 2019;Stockwell, 1999) and Geocluster (CGG-Veritas) software packages were used extensively in this study. We are grateful to editors and reviewers for handling this work and their recommendations that improved this manuscript.
Data availability. Seismic datasets of the PAMELA-MOZ3  (Moulin and Aslanian, 2016)  and PAMELA-MOZ5  (Moulin and Evain, 2016)  cruises are archived and referenced in Ifremer SISMER database and can be requested at https://doi. org/10. 17600/16009500 (last access: 10 August 2021) and https://doi. org/10. 17600/16001600 (last access: 10 August 2021). The MZ3 final velocity model is provided in RAYINVR format on SEANOE at https://doi. org/10. 17882/80287 (last access: 10 August 2021,  Evain et al. , 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/se-12-1865-2021-supplement.
This hypothesis was further strengthened by the recent discovery of endogenous viral elements in the genomes of Ornithodoros moubata and Ornithodoros porcinus ticks. These elements, representing almost 10% of the ASFV genome, probably integrated around 1. 46 million years ago allowing for a glimpse into viral evolution and tick-virus coevolution  (Forth et al. , 2020b). Nucleocytoviricota. The neighbour joining tree was constructed from  MAFFT 7. 388  aligned ASFV EP1242L amino acid sequences using the Geneious tree builder in Geneious employing the Jukes-Cantor model and the UPGMA tree build method and 1000 bootstraps. Taxon names include, where available, ASFV-designation and INSDC accession number and P72 genotype. The scale bar represents number of substitutions per site. https://www. wageningenacademic.
For example, antibodybased assays like western blotting can be used to describe the expression of viral proteins in the course of an infection, if immunological reagents are available. However, the number of samples that can be analysed may be limited by the throughput of the assay format and quantitative analysis may be difficult, especially if different strains of viruses are compared. Therefore, MSbased proteomic workflows seem straightforward to analyse proteomes of viruses with large coding capacities like ASFV, especially if highly diverse genes like the members of the ASFV multi gene family (MGF) are present. Fortunately, the proteome of the domestic pig is available and the sequences of a number of ASFV strains are provided in public repositories. Recently, ASFV sequence data have been combined and made accessible in the ASFVdb database (http:// asfvdb.
In the majority of the eastern European countries, this small-scale pig production system prevails. Based on the data published by EUROSTAT in 2014, small-scale pig producers are mostly found in the 13 Member States that joined the EU since 2004 (EUROSTAT, 2014). However, these data refer to 2013, before the introduction of the ASFV in Eastern European countries, where since 2014 numerous outbreaks have been reported in the backyard sector, which resulted in a substantial reduction in the number of this type of farm. The higher density of pigs in small-scale confined production systems leads to a higher risk of pathogen circulation among herds. In backyard farms, pigs are generally purchased from several sources, such as other small pig producers, local markets or abroad, where the basic principles of biosecurity such as segregation and cleaning and disinfection are often poorly implemented.
Density data Strong variations in population densities are recorded within the European continent, even inside similar regions, e. g. in the Iberian Peninsula a range from less than three to over 30 wild boars per km 2 is registered. Food availability, such as crops or supplementary feeding, are the main factors affecting wild boar productivity and consequent local population densities. Estimates of the size and density of wild boar populations are key factors to monitor and understand the epidemiology of wild boar diseases, such as ASF. The organisation and collection of hunting statistics and their analysis is essential not only for hunting management but also for developing wildlife policies. Hunting data are available and potentially comparable across Europe for use in predictive spatial models of wild boar density.
Acknowledgements This publication is based on work from ' Understanding and combating African swine fever in Europe (ASF-STOP COST action 15116 )' supported by  COST (European Cooperation in Science and Technology ). LI received funding from the  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie Action  (Grant Agreement no. 656697 ). Additionally, we are grateful to all ASF-STOP participants for the productive, engaged and friendly exchange of ideas and information (Figure 1. 3).
Acknowledgements This publication is based on work from ' Understanding and combating African swine fever in Europe (ASF-STOP COST action 15116 )' supported by  COST (European Cooperation in Science and Technology). The author would like to thank  Ana-Luisa Reis  for critical reading of the manuscript. The author's work on ASFV is supported by the  Department for Environment, Food and Rural Affairs and the Biotechnology and Biological Sciences Research Council  grants  BBS/ E/I/00002120 ,  BBS/E/I/00007030 ,  BBS/E/I/00007031 ,  BBS/E/I/00007037  and  BBS/E/I/00007039. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  773701. Acknowledgements This publication is based on work from ' Understanding and combating African swine fever in Europe (ASF-STOP COST action 15116 )' supported by  COST (European Cooperation in Science and Technology).
Acknowledgements This publication is based on work from ' Understanding and combating African swine fever in Europe (ASF-STOP COST action 15116 )' supported by  COST (European Cooperation in Science and Technology). Acknowledgements This publication is based on work from ' Understanding and combating African swine fever in Europe (ASF-STOP COST action 15116 )' supported by  COST (European Cooperation in Science and Technology). The authors kindly acknowledge José Cortinas Abrahantes for Figure 9. 2 and  Linda Ernholm  for Figure 9. 3. Acknowledgements This publication is based on work from ' Understanding and combating African swine fever in Europe (ASF-STOP COST action 15116 )' supported by  COST (European Cooperation in Science and Technology). Acknowledgements This publication is based on work from ' Understanding and combating African swine fever in Europe (ASF-STOP COST action 15116 )' supported by  COST (European Cooperation in Science and Technology).
49 MHz for 29 Si signal. A zirconia rotor (Ø = 4 mm) was used. The spinning rate was 10 kHz. The 27 Al (I = 5/2) MAS NMR spectra were recorded after p/8 pulse irradiation (1. 5 μs) using a 1-MHz filter to improve the signal/noise ratio. The 29 Si (I = 1/2) MAS NMR spectra were recorded after a π/2-pulse irradiation (4 µs) using a 500 kHz filter to improve the signal/noise ratio. In each case, 400 scans were collected. The time between acquisitions was set to 5 and 10 s, respectively, to minimize saturation effects. A solution of AlCl 3 and Tetramethylsilane (TMS) were used as references for 27 Al and 29 Si. The deconvolution of NMR spectra was performed with the Winfit program (Bruker). The estimated errors for chemical shifts and relative areas are 1 ppm and 2 % respectively. The mechanical strength was measured by compressive tests using an Instron 5969 with a 50 kN load cell at a constant speed of 0.
CMIP models We analyse driver changes in wind shear and TCW out to 2100 in simulations from 38 CMIP5  (Taylor et al 2012)  and 26 CMIP6 models  (Eyring et al 2016) ; one realisation per model i. e. 'r1i1p1' members (see supplementary table 1; available online at stacks. iop. org/ERL/16/104023/mmedia) for which data were available for both variables. Only simulations forced by the RCP 8. 5 (Shared Socioeconomic Pathway 5-8. 5 for CMIP6) are analysed, consistent with CP4. Three different 30-year time slices are considered;  2030-2059 ('2040'), 2050-2079 ('2060'), and 2070-2099 ('2080'). The reference period covers 1950-1999, which was driven by historical anthropogenic and natural forcings. The models were interpolated onto a common 1. 25 • latitude × 1. 875 • longitude grid and averaged for respective time slices.
1 mm) of a range of raw CMIP5/6 models (grey box) as well as to a bias-corrected version of CMIP5 (blue box). We consider the latter to be the best available dataset regarding CMIP5-projected rainfall changes in West Africa  (Famien et al 2018). Across evaluated regions, the ensemble mean change in reconstructed extreme rainfall lies between +40% and 58% (+20%-31% for the 2040 period), which is markedly higher than for any ensemble-mean modelled CMIP rainfall in the same region. Furthermore, ∆P max 95 t+s shows a clear signal of intensification across the entire CMIP uncertainty range, linked to exclusively positive driver changes. Modelled daily extremes on the other hand include negative changes within the 10-90th percentile CMIP range for all regions, with region-dependent ensemble means between 14% and 35% for raw CMIP and 3%-48% for bias-corrected CMIP5.
Discussion and conclusions In this study, our aim was to fuse a CP model projection, which provides us with only one possible future of how precipitation extremes from West African MCSs might change, with a CMIPbased time-continuous uncertainty range. For that, we adopted a simple linear scaling based on only TCW and shear, which we show allows to reconstruct CP4-modelled rainfall intensities. The presented scaling approach follows the assumption that CMIP changes in atmospheric drivers of MCSs are plausible, while the CP model in combination with observations provides a more realistic response of extreme MCS rainfall to those drivers. We thus draw on the strengths of respective datasets. We find reconstructed changes in extreme rainfall to be exclusively positive across CMIP models, dominated by the strong projected increases in TCW with an ensemble 10-90 percentile range of +37%-75% (+55% mean) for 2070-2100 (+18%-30% with +26% mean for 2030-2060) under RCP8.
5 across our Sahel domain. Shear contributions to these figures reach at least 8% for most of the northern Sahel, although we note that this result is based on MCS sensitivities to shear in the afternoon. The shear contribution may be higher for nocturnal MCSs, when the importance of shear for MCS maintenance increases in the absence of daytime heating (e. g. Vizy and Cook 2018). The strictly positive changes are in contrast to CMIP-modelled changes in daily extremes, which we find to be less conclusive with a 10-90 percentile range of +0-60% (+30% mean) for raw CMIP5/6 rainfall and +4%-88% (+37% mean) using a bias-corrected CMIP5 dataset with some models again exhibiting negative changes. The limited previous analyses of extreme rainfall projections over West Africa to date focus on selections of RCP8.
In this way, an evaluation of convection-permitting rainfall projections based on atmospheric drivers helps to communicate to users more defensible hydro-climatological information for West Africa, using our best scientific knowledge and understanding of the likely future changes in MCSs. The logical framing of this approach also lends itself to the construction of climate-change narratives (e. g. Dessai et al 2018 , Burgin et al 2020) , which have been found to be very useful in risk communication. Model Processes for African Climate (IMPALA) projects (NE/MO17176/1, NE/M017230/1). Marsham also acknowledges funding from the HyCRISTAL project (NE/M02038X/1). We thank Rachel Stratton and Simon Vosper (Met Office), who ran the CP4 simulations within IMPALA and made the data available. Instructions for access to the CP4 dataset can be found in the User Guide via https://doi.
org/10. 5281/zenodo. 4316466 and are publicly available from the Centre for Environmental Data Analysis (CEDA) archive (http://archive. ceda. ac. uk/). The bias-corrected CMIP5 dataset can be obtained by contacting A D: moise. famien@locean. ipsl. fr. MSG data are available from the EUMETSAT archive https://navigator. eumetsat. int/product/EO: EUM:DAT:MSG:HRSEVIRI, ECMWF hourly ERA5 reanalysis data are available from the Copernicus Data Store (https://doi. org/10. 24381/cds. bd0915c6) and data from the Global Precipitation Measurement mission are available from https:// doi. org/10. 5067/GPM/IMERG/3B-HH/06. We also acknowledge the World Climate Research Programme, which, through its Working Group on Coupled Modelling, coordinated and promoted CMIP. We thank the climate modeling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving the data and providing access, and the multiple funding agencies who support CMIP5 (https:/ /esgf-node.
llnl. gov/projects/cmip5/), CMIP6 and ESGF, and we thank the involved climate modeling groups for producing and making available their model output. Special thanks go to the developers of the Pangeo CMIP6 data access and cloud processing (https://pangeo-data. github. io/pangeo-cmip6-cloud/accessing_data. html), which worked like a charm, and to the providers of the python packages matplotlib/cartopy, xarray, salem, scipy, metpy, pandas, and their dependencies.
Acknowledgments The research leading to these results received funding from the  U. K. 's Natural Environment Research Council/Department for International Development (NERC/DFID) Future Climate for Africa (FCFA) program , under the  AMMA-2050  (Grant Numbers  NE/M020126/1 ,  NE/M019977/1 ,  NE/M019969/1 ,  NE/M019950/1 ,  NE/M019934/1 ,  NE/M020428/2 ,  NE/M020428/1 ) and Improving.
Material and methods Occurrence data and environmental variables. We collected worldwide occurrence records of X. crassiusculus and X. compactus from the scientific literature (Supplementary Table  S1 ), the Global Biodiversity Information Facility (GBIF) and direct observations up until the 13th of September 2019. Once removed the localities where the species was not proven to be established, we were left with respectively 781 and 323 records for X. crassiusculus and X. compactus. We then removed duplicated data, by withdrawing all but one occurrence record per pixel of the climate raster (see below), and ended with 311 occurrences for X. crassiusculus (33% in its native area) and 205 occurrences for X. compactus (30% in its native area) (Fig. 1 ). We used environmental variables available from the Worldclim database (version 2.
g. Godefroid et al. 42  for an example of such a strategy). To do so, we removed the European occurrences from the datasets of each species, and randomly sampled 80% of the remaining occurrence records to train the model. The remaining 20% of records were used to evaluate the model. To characterise the background environment, 10,000 background points were randomly generated in a wide area representative of the species' habitat. We did this for 10 replicates for each of the 126 environmental datasets used for each species. We retained a dataset when at least 5 replicates performed (1) an Area Under Curve (AUC) > 0. 8, (2) a True Skill Statistics (TSS) > 0. 6 and (3) were able to predict 100% of the European occurrences. Such combinations of model × environmental dataset were considered successful and thus used in the next modelling step  43.
Before running the second step, we searched for the optimal MaxEnt parameters to avoid overfitting 39 by fitting MaxEnt models with different rate multiplier values (from 0. 5 to 4 with increments of 0. 5) and six feature classes (1. Linear; 2. Linear + quadratic; 3. hinge; 4. linear + quadratic + hinge; 5. linear + quadratic + hinge + product; 6. linear + quadratic + hinge + product + threshold) using the R package ENMeval  44. We selected the best combination of MaxEnt parameters based on AIC (see Muscarella et al. 44  for a full explanation of this method). In a second step, we used the models and environmental datasets that were selected as explained above with all the occurrence data, thus including the European records. A model was finally kept if it scored an AUC > 0. 7. Model outputs were transformed to binary maps using the threshold that optimised the TSS statistics on the testing data  10.