dataesr/scientific-paragraphs-categorization huggingface dataset:
2B ) and that SDs occur at a disproportionately higher frequency at the subtelomere (Fig. 2C ). Key differences in methodology between our work and that of  Wang et al. (2017)  include the use of a manually curated repeat library for the removal of repeat-element derived hits (an issue identi ed in their analysis,  (23)  and our more stringent threshold applied to SD length (20 kbp vs 10 kbp). Despite our increased ability to remove spurious hits, improvements to the genome assembly  (24)  and our novel approach allow us to demonstrate a 10-fold increase in SDs contribution to the S. mansoni genome. However, this may still be an underestimate; some SDs may fall within the incomplete subtelomeres of the assembly (4 chromosome ends of the 16 do not have telomeric repeats,  (24)  and there may be genuine SDs < 20 kbp, which would be omitted from our dataset due to our length ltering.
6 ). Whilst these tandem duplications further demonstrate the evolutionary bene t of gene expansion, they do not represent NAHR and fall outside the scope of this work (Cercarial Proteases and Mucins, Fig. 3A ). The largest example of gene family expansion is a cluster of 49 genes, of which 36 were subtelomeric. Although some of these genes were annotated as "CP-391B-like egg proteins" and "CP-391S-like egg proteins", the descriptions available from WormBase ParaSite database (Release 17, (48) did not have a consensus product de nition (Supplementary File 12) and were inconsistent with our protein domain analysis. We, therefore, initially referred to this cluster as "hypothetical proteins". Thirty-ve proteins in the cluster have a 5' signal peptide, 28 of which also have a 3' membrane-bound domain (Fig.
4A ), and via conserved residue analysis of the cluster, we predicted a plexin-repeat domain (PF01437. 28, Fig. 4C ) with a characteristic "CxWC" motif (Fig. 4B )  (93, 94). We renamed this cluster as "plexin domain-containing protein" based on this evidence. We could not identify any protein domains associated with the remaining part of the protein core despite conservation at many residues (Fig. 4B ) and a well-resolved core structure upstream of the plexin domain (Fig. 4C, 4D ). Little is known about PSI domains, particularly their direct biochemical function  (95) ; however, they are often subunits of larger, functional domains, particularly extracellular receptors  (93) , in which they are thought to play at least a structural role in transmission (96). It is likely that the remaining unannotated protein core represents a larger domain, within which the PSI domain is a subunit.
Similar protein domain architecture is found in the platyhelminth-speci c integrins, which have a 5' signal peptide, a 3' membrane-bound domain and core integrin domains, of which PSI is a subunit  (97). Because subtelomeric genes are often taxa-speci c owing to the rapidly changing interface at which they are found, using databases mainly informed by model organisms limits our capacity to identify parasite-speci c protein domains (68, 98, 99). Similar proteins are, therefore, unlikely to be present in orthogonal datasets from which inferences regarding structure can be drawn. This is likely to change in the near future due to the increasing number of publicly available genomes. Expression analysis of the plexin cluster indicates stage speci city and tissue speci city. We found that plexin domain proteins are primarily expressed in the intra-molluscan stages of the parasite, that is, within the intermediate host (Fig.
Model up-scaling We used STEP-GENDEC-N2O to simulate daily herbaceous vegetation mass, CO2 and N2O emissions from soil in West Sahelian SPSs. We developed a framework to run the model at a regional scale, using the parameterizations developed in the above-cited studies. Simulations were performed at the West Sahelian band scale (Fig. 1 ) divided into 18271 grid cells of 0. 1° x 0. 1°, from 2012 to 2022. Input variables were extracted from different datasets available at the global or regional scale as described below (Table  1 ). For the soil dataset which is provided at finer resolution (<0. 1° x 0. 1°), pixel values for each centroid of the 18271 simulation grid cells were extracted. Simulations were performed using a 6-year spin-up to reach carbon and nitrogen pools and stability as in  Agbohessou et al.
Climate data The climate data required for the simulation were derived from two different datasets (GPM_3IMERGDF and AgERA5). Precipitation (mm) data were taken from the IMERG (Integrated Multi-satellitE Retrievals for GPM) dataset, GPM_3IMERGDF  (Huffman et al. , 2019). GPM_3IMERGDF or GPM IMERG Final Precipitation L3 1 day 0. 1 degree x 0. 1 degree V06, is derived from the half-hourly GPM_3IMERGHH dataset (GPM IMERG Final Precipitation L3 Half Hourly 0. 1 145 degree x 0. 1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), 2022) The spatial distribution of the GPM_3IMERGDF average precipitation over the last decade (2012-2022) exhibits significant gradients, with precipitation reaching as low as 0 mm at the northern border, exceeding 500 mm at the south-eastern border, and exceeding 1000 mm at the south-western borders (Fig.
A2 ). Additionally, there is a significant increasing trend in annual mean precipitation amounts from 2010 to 2021, along with interannual variability (Fig. 3c ). Temperature (°C), solar radiation (MJ m -2 ), vapour pressure (hPa) and wind speed (m s -1 ), were extracted from the AgERA5 dataset  (Boogaard et al. , 2020)  using the R package "ag5Tools" (ag5Tools: Toolbox for Downloading and Extracting Copernicus AgERA5 Data, 2022). AgERA5 dataset provides daily surface meteorological data matching the input needs of STEP-GENDEC-N2O. The dataset is actually based on the ECMWF (European Centre for Medium-Range Weather Forecasts) re-analysis ERA5-Land dataset (Muñoz Sabater, 2019). ERA5-Land is an enhanced global dataset for the land component of the fifth-generation reanalysis produced by the ECMWF.
Soil data Soil pH and soil texture (i. e. clay, silt, and sand content) were obtained from the iSDA (Innovative Solutions for Decision Agriculture Ltd. ) soil dataset  (Hengl et al. , 2021). The iSDAsoil dataset contains soil property predictions at 30 m pixel size using machine learning coupled with remote sensing data and a training set of over 100,000 analysed soil samples all over Africa  (Hengl et al. , 2021; Miller et al. , 2021). Dry soil albedo, soil moisture (mm) and soil temperature ( • C) at the beginning of the simulation were extracted from the ECMWF re-Analysis ERA5_Land  (Muñoz Sabater, 2019). Exploration of the extracted soil datasets showed that the soils in the Sahel region are typically sandy, with high levels of sand and low levels of clay (Fig. A3a  and A3b ). This results in well-drained soils but low in nutrients.
Animal load data Information about livestock population and animal load distribution were obtained from the total livestock number for the reference year 2010 provided by the Gridded Livestock of the World version 3 (GLW3)  (Gilbert et al. , 2018)  dataset. GLW3 provides global population densities of cattle, buffaloes, horses, sheep and goats in each land pixel at a spatial resolution of 0. 083333 decimal degrees (approximately 10 km at the equator). The relative spatial distribution of livestock over the simulation period was assumed to be the same as the one indicated by the GLW3 database for the year 2010. To our knowledge no measurement data are available on the temporal variation of livestock across the Sahel. We used the annual values of the GLW3 database to distribute the animal load on a monthly basis, taking into account the temporal variation of the livestock population from one month to the next throughout the year.
We assumed an increase of the livestock up to 100% (in reference to the GLW3 database) in the pixels during the rainy season, and a gradual decrease down to 20% as we approach the middle of the dry season. Analysis of the GLW3 dataset revealed that livestock is heterogeneously distributed across the Sahel and the animal load is dominated by bovines, ovines, caprines and some equines  (Gilbert et al. , 2018). High livestock densities were observed in north-western Senegal, southern Mauritania, central Mali, northern Burkina-Faso, southern Niger, northern Nigeria and southwestern Chad (Fig. A3f ).
Initial biomass data The model calibration input parameters related to herbaceous vegetation such as Initial mass (Bg0) and Initial Specific Leaf Area (SLAg0) at germination date were computed using data from the biomass dataset provided by "Action Contre la Faim (ACF) Surveillance West Africa"  (Bernard and Fillol, 2021, 2020; Lambert et al. , 2019). ACF biomass data were produced from 10-day images of Dry Mass Production (DMP) from SPOT-VEGETATION 4&5, PROBA-V and SENTINEL-3 satellites  (Lambert et al. , 2019). The retrieval algorithm of the DMP product is described as follows  (Monteith, 1972; Swinnen et al. , 2022)  Eq. (  1 ): DMP = R • fAPAR • ℇ LUEc • ℇ c • ℇ T • ℇ CO2 • CUE (1) DMP is the 10-day Dry Matter Production (kgDM/ha/day), R is the 10-day total shortwave incoming radiation (GJT/ha/day), fAPAR is the PAR-fraction absorbed by green vegetation (JAP/JP), ℇ LUEc is the light use efficiency at optimum (kgDM/GJAP), ℇ c is the fraction of PAR in total shortwave (JP/JT), ℇ T is the normalized temperature effect, ℇ CO2 is the normalized CO2 fertilization effect and CUE is the carbon use efficiency.
The 1 km 2 resolution biomass raster product showing biomass production in the Sahel in kg ha -1 yr -1 was downloaded for the study period. We extracted the biomass value for each centroid of the simulation grid cells and performed a normalization by linearly scaling the dataset to a range between 0 and 2. 5 g m -2 (the min and max values of Bg0 in the STEP model) to get the spatial distribution of the initial biomass (Bg0) at germination date. To obtain the spatial distribution of the initial Specific Leaf Area (SLAg0) at germination date, we normalized ACF biomass dataset to a range between 0 and 280 cm 2 g -1 (min and max values of SLAg0 given in  Jarlan et al. (2008) ). Here is the normalization formula used to linearly scale biomass values to Bg0 and SLAg0 ranges Eq. (  2 ): X norm = a + (x-min(x))•(b-a) max(𝑥)-min (𝑥) (2) With X norm representing the value of Bg0 or SLAg0, a and 𝑏 being the smallest and the largest value that Bg0 or SLAg0 can take, respectively, and 𝑥 being the biomass values in the ACF dataset.
Accounting for SPSs distribution in model outputs Global Land Cover -SHARE (GLC -SHARE) dataset  (Latham et al. , 2014)  provides information about the spatial distribution of a set of eleven major land cover classes (i. e. , artificial surfaces, cropland, grassland, tree covered areas, shrubs covered areas, herbaceous vegetation, aquatic or regularly flooded, mangroves, sparse vegetation, bare soil, snow and glaciers, and water bodies) for the year 2013 and at 1-km 2 pixel resolution. First, we assumed that land cover change intensity was negligible in the Sahel during the last decade (the study period). Second, a new land cover class called silvopastoral areas was created and represents the sum of pixels of the classes: shrubs covered areas and grassland (Fig. 1 ). The proportions of silvopastoral areas pixels within the 0.
1° × 0. 1° simulation grid cells (pixel resolution ≈ 123. 21 km 2 ) were calculated using the GLC -SHARE dataset to obtain the spatial distribution of silvopastoral systems in the Sahel (Fig. A1 ). To estimate the annual budget of soil CO2 and N2O emissions, the model outputs were weighted by the proportion of silvopastoral area within each simulation grid cell (Fig. 1  and Fig. A1 ), therefore considering all SPSs, even those which %SPS < 80%, across the simulation domain.
In the first step, a bootstrap sample of observations (equal to the number of trees) is randomly drawn from the dataset, with replacement. Approximately one third of the total observations are left out and used as "out-of-bag" (OOB) data to evaluate the model's performance and prevent the need for a separate validation dataset  (Efron and Tibshirani, 1986; Philibert et al. , 2013). This provides a resampling procedure that generates multiple versions of the training dataset, which helps to mitigate overfitting and improves the accuracy of the model. In the second step, a random subset of predictor variables is selected at each node of the decision tree  (Ghattas, 2000; Philibert et al. , 2013; Prasad et al. , 2006). The number of variables selected (mtry) was set to the integer part of the square root of the total number of variables  (Breiman, 2001; Liaw and Wiener, 2002; Philibert et al.
, 2013). This approach involves considering a subset of variables at each node of the decision tree and selecting the best variable that maximizes the information gain. This randomization technique reduces the correlation among the trees and makes the model more robust and accurate. In the final step, multiple decision trees are grown from the bootstrapped dataset and the random subsets of features. The trees are grown using recursive binary partitioning of the data, with the best split determined by optimizing a quality criterion such as information gain by the Gini impurity index  (Breiman et al. , 1984). The final prediction is made by aggregating the predictions of all trees in the forest by averaging the outputs. The process is repeated multiple times until a stable estimate of model performance is obtained.
60 ± 2. 38 gC m -2 d -1 N: 5. 89 ± 0. 46 gN m -2 d -1 ), and soil water content (4. 87 ± 0. 19% yr -1 ). Our simulation results do not allow to explore possible interactions between climate warming and annual soil CO2 and N2O emissions, as the average annual air temperature (averaged over the study domain) did not vary much over the simulation period (28. 37 ± 0. 25 °C). Regional-scale observations show a temperature increase ranging from 1 to 2°C between 1950 and 2010  (Guichard et al. , 2020). Therefore, over a 10-year period, this corresponds to a maximum increase of approximately 0. 33°C, which is less than 0. 5°C. This order of magnitude is comparable to the one computed for air temperature from the climate dataset used, and it is too small to be detected by temperature-versus-time regression.
Uncertainties and limitations The lack of comprehensive dataset on the annual spatial distribution and growth dynamics of the livestock population in the Sahel between 2012 and 2022 remains a significant source of uncertainty in the CO2 and N2O emissions reported in this study. Actually, information on the spatial distribution and population of livestock was only available for the 2010 year  (Gilbert et al. , 2018). Only the spatial and the seasonal variability of the grazing pressure was taken into account in our simulation. We assumed that the annual distribution and growth dynamics of livestock in Sahelian SPSs did not change significantly between 2010 and 2022, although it might have been affected by the interannual variability of herbaceous mass. Given the significant impact of livestock on CO2 and N2O emissions in these ecosystems  (Agbohessou et al.
, 2003a, b)  and databases related to soil C and N content  (Hengl et al. , 2021)  in the Sahel region, accurately assessing the temporal variability of these elements in Sahelian SPSs soils remains challenging. Finally, we assumed that there were no natural or anthropogenic disturbances such as wildfires in Sahelian SPSs during our simulation period. Uncertainties related to disturbances like wildfire are actually difficult to estimate as there are varying perspectives and conflicting findings in the literature regarding the impact of burning on N2O emissions  (Karhu et al. , 2015; Takakai et al. , 2006).
, 2017; Brümmer et al. , 2009; Delon et al. , 2017). Our results extended these local estimates to a broader spatiotemporal scale, showing that, overall, Sahelian SPSs soil emits less CO2 and N2O than tropical areas and croplands, on a global scale. Furthermore, by mapping emissions we provided crucial insights into the localization of soil CO2 and N2O emission hotspots, thereby offering indirect assessments of soil health in the Sahel region. This information can be a valuable asset for land managers who can leverage it to devise and implement effective strategies aimed at minimizing emissions and fostering carbon sequestration. To further refine estimates of soil CO2 and N2O emissions in Sahelian SPSs, efforts to collect comprehensive datasets on livestock spatial distribution and temporal dynamics, tree densities and fire are needed.
Funding. This work was supported by the " Carbon sequestration and greenhouse gas emissions in (agro) silvopastoral ecosystems in the Sahelian CILSS states" (CaSSECS) project  ( FOOD/2019/410-169 ), which received funding from the  European Union  under the " Development of Smart Innovation through Research in Agriculture " (DeSIRA) Initiative. Additionally, this research received financial support from the " Intergrated Nitrogen Studies in Africa " ( INSA ) project, which is supported by the  European Union 's  Horizon 2020 Research and Innovation programme  under the  Marie Skłodowska-Curie Actions (RISE MSCA)  Grant Agreement n°  871944. Mention. This document has been produced with the assistance of the  European Union. Its content is the sole responsibility of the authors and can in no way be taken to reflect the position of the  European Union.
Code availability. The 2D STEP-GENDEC-N2O model is available on Zenodo at https://doi. org/10. 5281/zenodo. 7866671 Author contribution. YA, CD, MG, EM, and OR conceived and designed the study. EM, CD, MG, and YA developed the 2D STEP-GENDEC-N2O model code and YA performed the simulations. YA prepared the manuscript with contributions from all co-authors.
is used in our study. The target variables of the RF are the spatial distribution of the simulated soil CO2 and soil N2O emissions, while the predictors are the spatial distribution of a range of environmental and biological factors susceptible to impact the simulated soil CO2 and N2O emissions. These factors include: soil sand content, soil clay content, soil water content, soil temperature, soil pH, soil C content, soil N content; air temperature, albedo, annual precipitation; herbaceous mass and animal load. The dataset used was composed of 18271 individual observations (outputs of each simulation grid cell), and we conducted the RF with the default parameters proposed by the randomForest package.
of the datasets used for input variables and land cover/use Dataset Input variable (unit) Spatial resolution Temporal resolution Ref URL iSDAsoil Soil pH (-) and soil 30 m 01-01-2012 (Hengl et al. , https://developers. google. com/earth- texture (clay, silt, and (taken as 2021) engine/datasets/tags/isda?hl=en sand content (%)) constant) ERA5-Land Initial soil water content 0. 1° x 0. 1° 01-01-2012 (Muñoz Sabater, https://cds. climate. copernicus. eu/cds (mm) and initial soil (taken as 2019) app#!/dataset/reanalysis-era5- temperature (•C) constant) land?tab=form GPM_3IMERGDF Precipitation (mm) 0. 1° x 0. 1° 01-01-2012 to (Huffman et al. , https://disc. gsfc. nasa. gov/datasets/GP 31-12-2021 2019) M_3IMERGDF_06/summary?keywo (daily) rds=%22IMERG%20final%22 AgERA5 Temperature (°C), Solar 0. 1° x 0.
Action Contre la Faim Surveillance West Africa Proxy of herbaceous 1 km 2019-2021 (Lambert et al. , https://data. humdata. org/dataset/acf_ mass at germination (Kg (taken as 2019; Bernard biomass_west-africa_raster ha -1 ) constant) and Fillol, 2020, 2021) Tree area density Proxy of trees' foliar 100 m 2023 (taken (Tucker et al. , https://daac. ornl. gov/cgi- biomass as constant) 2023) bin/dsviewer. pl?ds_id=2117.
Economists rarely agree on issues, but on carbon taxes, they do. The agree so much that 3354 economists, including many Nobel Prize winners, recently signed an open letter supporting the introduction of a carbon tax. 2  But what is more puzzling, is that fossil fuel companies seem to agree as well. Building up a new dataset, I find agreement rates as high as 78% when looking at the top 50 largest oil and gas companies who have expressed their opinion on the question. I ask why fossil fuel companies are so active promoting the introduction of carbon taxes? I offer a survey of the largest fossil fuel companies and analyse their take on carbon taxes in their different communications. The paper first looks at a conceptual trilemma model of fossil fuel companies and carbon taxes. The model informs that there are only three possible options.
14 Using these data, a carbon tax would increase the coal price of 17-86$ per MWh and only 10-50$ per MWh for gas. It becomes easy to see that if consumers have to choose between paying 86 or 50$ for a MWh on top of the price of energy generation, they will turn to gas (oil is used less for energy generation). A larger carbon tax would therefore increase the demand for gas and reduce the demand for coal, all things being equal. If this is 12 IEA, Coal 2021, Analaysis and forecast to 2024, IEA report, available here https://www. iea. org/reports/coal-2021 (see table on p. 99 for forecast). 13  Data from Our World in Data, available here https://ourworldindata. org/grapher/carbon-dioxideemissions-factor 14 Chevron Climate Change Resilience Report, p. 32, available here https://www. chevron. com/-/media/chevron/sustainability/documents/climate-change-resiliencereport-7-21.
Removing uncertainty Uncertainty is bad for business and can reduce investments (Bloom, Bond, and Van Reenen   2007; Baker, Bloom, and Davis 2016). Currently, oil and gas companies do not know if, when or where a carbon tax could be implemented globally. Knowing this would help plan investment with less risk. This uncertainty also has potential effect on financial stability of the economy (Semieniuk et al. 2021). Santos and Oil Search, two companies in our dataset, perfectly reflect this issue of uncertainty in a large merger document available online: "[…] market participants and commentators have a wide range of views regarding both future supply of and demand for oil, reflecting uncertainty regarding future technological and regulatory developments and the impact of global initiatives to reduce carbon emissions.
These results motivated us to track activation dynamics in Centralia to understand the contributions of active members to the community disturbance response. Here, we present a 7-year longitudinal study of the "whole" bacterial communities and the subset of active members  (27, 28)  annually sampled from our original fire-impact gradient of fire-affected, recovered, and reference sites in Centralia (Fig. 1A , Table  S1 ). Whole bacterial communities were assessed using 16S rRNA gene amplicons, which include contributions of active, dormant, and recently deceased cells. The active subset of the whole community was determined by filtering the dataset to include only the taxa that met a conservative, populationlevel activity criterion based on their 16S rRNA to 16S rRNA gene ratios. Then, we applied several specific methods to quantify microbiome recovery, particularly time-lag and trajectory analysis  (29, 30) , where resilience is indicated by the slope of a time-lag regression and by the strength of directionality in trajectory analysis (e.
Sequence processing Raw, demultiplexed sequencing reads are available through the NCBI short read archive (BioProject PRJNA973689). Individually for each sequencing run, raw reads were first trimmed using cutadapt (v1. 18) (59) to remove remaining primer sequences. Paired-end reads were then merged using PEAR (v0. 9. 6) (60) with a maximum length of 600 bp and a minimum overlap of 20 bp. Paired reads with a maximum expected error greater than one and/or containing unknown bases were removed using usearch (v10. 0. 240_i86linux64)  (61). Filtered sequences were aligned to the SILVA SEED reference alignment (v132), and poorly aligned sequences were removed along with those containing homopolymers with a length of 8 or greater using mothur (v1. 40. 4)  (62). At this point, quality-filtered sequences from all sequencing runs, including DNA and cDNA libraries, were pooled into a master dataset.