dataesr/scientific-paragraphs-categorization huggingface dataset:
Elemental localization in Pancheria reticulata The elemental maps obtained from the synchrotronbased XFM analysis were visually interpreted for localization of elemental distribution and prevailing relative concentrations estimated based on comparisons of the quantitative concentration scale and anatomical features in the maps. The data from the leaf cross-sections show that Mn had particularly high concentrations (30-40 g kg -1 ) in the large cells of the adaxial hypodermis and in lower concentrations in the abaxial epidermal cells, apoplastic space of the collenchyma, and more diffusely occuring in the mesophyll (Figs. 6  and 7 ). The localizations of K and Mn are very similar, yet with a distinct difference in the midrib xylem where there is hardly any prevailing Mn, whilst K concentrations are high (~10 g kg -1 ) (Fig.
b. , vascular bundles; w. , wood  of a genus where hyperaccumulation of multiple elements is observed. Pancheria diverged from its sister group, Cunonia, 26. 9 million years ago  (Pillon et al. 2021a) , with a very similar level of morphological and species diversity in New Caledonia, an equivalent preference for ultramafic substrates, but which has much lower affinities for metals. Data obtained from analyses of leaves by ICP-AES and by herbarium XRF on Pancheria reticulata confirmed that this species has extremely high Mn concentration in its leaves (19. 5-57. 7 g kg -1 , n = 20). To our knowledge this is the highest Mn concentration in leaves ever found in a hyperaccumulator species so far, with Virotia neurophylla (Proteaceae) from New Caledonia spanning a similar concentration range (15.
Acknowledgements This research was undertaken at  P06  at  DESY , a member of the  Helmholtz Association (HGF). V. Gei  was the recipient of an  Australia Awards PhD Scholarship  from the  Australian Federal Government. The authors thank the  Province Sud de Nouvelle-Calédonie  for permission to collect the plant material samples ( N° 4508-2018/ARR/DENV ). The authors thank  Vanessa Hequet  for collecting P. multijuga at Mont Humboldt.
New Phytol 133: 217-224. https:// doi. org/ 10. 1111/j. 1469-8137. 1996. tb018 88. x  Reeves RD ,  Baker AJM ,  Borhidi A, Berazaín  R (1999) Nickel hyperaccumulation in the serpentine flora of Cuba. Ann Bot 83: 29-38. https:// doi. org/ 10. 1006/ anbo. 1998. 0786  Reeves RD ,  Baker AJM ,  Jaffré T ,  Erskine PD , Echevarria G, Ent A (2017) A global database for plants that.
Correlations between the qua tive instability factors were then evaluated to determine which ones intervene the mo determining the ground instability classes. Finally, an additional variable "type of i bility" was introduced to correlate with instability factors in order to observe the pos links between them and thus post-validate the analysis. PCA is an extremely powerful tool for compressing and synthesizing informa which is very useful when there is a large amount of quantitative data to process interpret  [21]. It allows for correlations between quantitative variables to be visua and interpreted, even if they are dichotomous, in order to limit the number of varia and only keep those that are important in the analysis. In our study, the objective in u PCA was to represent the quantitative instability factors and to visualize their correla in order to classify the factors according to their degree of importance.
Correlations between the qualitative instability factors were then evaluated to determine which ones intervene the most in determining the ground instability classes. Finally, an additional variable "type of instability" was introduced to correlate with instability factors in order to observe the possible links between them and thus post-validate the analysis. PCA is an extremely powerful tool for compressing and synthesizing information, which is very useful when there is a large amount of quantitative data to process and interpret  [21]. It allows for correlations between quantitative variables to be visualized and interpreted, even if they are dichotomous, in order to limit the number of variables and only keep those that are important in the analysis. In our study, the objective in using PCA was to represent the quantitative instability factors and to visualize their correlations in order to classify the factors according to their degree of importance.
Study Site and Input Data Each selected site was analyzed in-depth to identify the potential predisposing factors that could have participated in the triggering of ground movement. Data were collected from different sources, including field trips, thematic maps, state databases, modeling and data reprocessing in GIS software, etc. Table  1  presents the variables used in MCA and Table  2  presents the variables used in PCA. Appendix A provides the datasets used in our statistical analyses: Table  A1  presents the MCA input data, Table  A2  presents the PCA input data for the landslide portion, and Table  A3  presents the PCA input data for the rockfall portion. The same principle was applied to the other tested parameters: geometric, hydrological, anthropic aspects, etc. The sites were chosen to be representative of the different ground instability types existing in the study area.
Study Site and Input Data Each selected site was analyzed in-depth to identify the potential predisposing factors that could have participated in the triggering of ground movement. Data were collected from different sources, including field trips, thematic maps, state databases, modeling and data reprocessing in GIS software, etc. Table  1  presents the variables used in MCA and Table  2  presents the variables used in PCA. Appendix A provides the datasets used in our statistical analyses: Table  A1  presents the MCA input data, Table  A2  presents the PCA input data for the landslide portion, and Table  A3  presents the PCA input data for the rockfall portion. The processed data are both qualitative and quantitative. Some MCA variables are found in the PCA as quantitative variables, whereas others are specific to PCA.
In PCA, we decided to independently process landslide and rockfall as the mechanics of the movements is different. Thus, the same variable may not have the same influence weight in both cases. In both PCAs, elevation was considered as an additional variable because it disturbs the other variables. In the landslide PCA dataset, we decided to add some geomechanical parameters: cohesion, C and friction angle, Φ. Considering where landslide occurred, cohesion and friction angle are calculated on each site by a retro-analysis at limit equilibrium. Thus, the set of parameters (C and Φ) that gives a factor of safety equal to one is calculated for each given geometrical description. Numerical modeling of each landslide was performed with FLAC/slope [23]. As it was not possible to carry out precise soil studies on each site, we had to access part of our geotechnical parameters from existing literature.
In the landslide PCA dataset, we decided to add some geomechanical parameters: cohesion, C and friction angle, Φ. Considering where landslide occurred, cohesion and friction angle are calculated on each site by a retro-analysis at limit equilibrium. Thus, the set of parameters (C and Φ) that gives a factor of safety equal to one is calculated for each given geometrical description. Numerical modeling of each landslide was performed with FLAC/slope [23]. As it was not possible to carry out precise soil studies on each site, we had to access part of our geotechnical parameters from existing literature. Thus, C and Φ are linked together. Thus, they are additional variables in the analysis. The interest of their presence is to examine their correlations with the other active variables. In the PCA rockfall, cohesion is considered to be equal to zero as the rocks failed; thus, no cohesion value is included in the analysis.
Focus on the Additional Variable "Type" In MCA, it is common for a variable to play a specific role in the sense that it is the one that we are trying to explain it from the others  [28] ; this is exactly the case of the additional variable "Type" in the dataset. This specific role eventually appears only at the level of interpretation. When this is the case, it means that MCA has worked well. In other words, the variables explaining the phenomenon (here the "Type" variable) have been taken into account and have been highlighted by the analysis. Here, we need to know the factors that control the type of ground instability. In the modalities graph, the type of instability is located at the barycenter of the modalities it contains. The confidence ellipses surrounding the barycenters contain the modalities that are related to the instability types.
Interpretation of the Additional Qualitative Variable "Landslide Depth" In the study area, surface slides are the most abundant and they are more problematic than deep slides. Through PCA, we looked at the modalities of the factors that may control the depth of the slides. According to the individuals' graphs (Figure  10a, b ), the two modalities (shallow and deep) have coordinates close to the center of the graph on the first four dimensions; this gives the first indication that the depth variable does not depend on any variable in the dataset. The only observation that can be drawn from the use of this additional quantitative variable is that we find only superficial slides in the peridotite, we find only deep slides in the sandstone, and we find both in the shales and micaschist.
MCA The key to the evaluation of causative factors is their contribution rate in the dataset. In principle, factors with a very low contribution value have little or no influence on the ground instabilities studied. The three factors "Spring", "Watercourse", and "Dip/slope" are the least influential. This conclusion is related to the variation of the modalities of the three variables with the fixed modalities of the other variables. In several study sites, we notice the repetitive aspect of certain modalities such that there is a mutual influence on one another. It is understood that in order to trigger field instability in some particular sites, it is necessary to repeat modalities. In these sites, the similarity between modalities does not concern the modalities of the three least influential variables.
For example, we find that the two modalities of "Spring" variable, i. e. the presence or not of a water spring, has no effect on the modalities of the other variables. An evaluation of the most influential factors requires a more in-depth analysis. The principle is based on multiple correlation analysis, the purpose of which is to rank the variables according to the number of links that each variable has with the others. The variable having the highest number of correlations with the other variables is the most dominant in the dataset, and the variable with the least correlations is the least dominant. The analysis is realized by observing the variables whose contribution rate is more or less similar along the same factorial axis. The variables "Lithology", "Elevation", and "Fracture persistence" are the most dominant according to both axes 1 and 2; each of them has a correlation with eight other variables.
Landslides All active variables present in the dataset are potentially predisposing factors for landslides. Here, we seek to identify the predominant factor. By analyzing the 19 study sites through the table of site contributions to the first four dimensions, we notice that nine sites are not related to any specific variable and the others are related to at least one variable in the dataset. The fact that nine sites are not linked to any specific variable can be explained by the same influence weight of the different variables. As for sites 12, 13, and 15, we can note that they are strongly related to density and fracturing. Landslides in sites 23, 24, 25, and 26 are strongly related to the presence of water springs, which are the predominant predisposing factors. The last three remaining sites (sites 9, 16, and 20), which correspond to road slope collapse-type landslides, are strongly related to their zero NDVI and are negatively related to slope height.
Site 16 is also strongly related to dip. In these three sites, anthropic interventions accentuate the natural predisposing factors to instability by weakening the mechanical parameters of the soil and the degradation of stability factors such as NDVI. Here, we can say that the total absence of vegetation is an important predisposition factor in the occurrence of landslides. In the case of site 16, in addition to the NDVI factor, the "dip of the layers" factor can have played an important role in predisposition to failure. The study sites can be subdivided into four groups, described below, according to their predominant predisposing factors: 1. In this group, all predisposing factors in the active dataset have the same level of influence. In other words, removing or changing the value of any of the variables can completely change an unstable terrain into a stable terrain or conversely, increase the instability.
Lithologically, this group includes only shales and micaschists; this may indicate that the lithological nature of the terrain controls the values of the other variables modalities. It is important to note that in the case of metamorphic rocks, schistosity most likely plays an important role, even though it is not included in the dataset because it is very difficult to quantify. Nevertheless, it can represent a very important predisposition parameter by strongly increasing the joint density and fragmenting the rock.
4. This group includes the majority of landslides encountered in the vicinity of an anthropogenic infrastructure (road, runway, structure, etc. ). Man can intervene and modify certain natural parameters, in particular geomechanical parameters and the NDVI of the terrain, and thus increase the susceptibility of landslides. According to PCA, this group is controlled by NDVI, which has a negative value most of the time, thus decreasing the stability. Moreover, the negative value of NDVI becomes an indicator that the area where the landslide is located has been affected by anthropogenic interventions. Variables in the dataset can therefore be classified according to their order of influence on landslides in the study area. According to the analysis of the four groups, the lithological nature of the terrain is preponderant, even though it is represented by a single active variable, "Density".
Dispersion datasets at local scale There is a large panel of atmospheric dispersion tracer experiments for model validation at local scale  (Olesen, 1998) , both for rural and urban areas. However, most of the experiments were conducted within a few kilometers of the source. There is a lack of tracer measurement experiments studies ranging from the short to medium distances (2 km-20 km). At such scale, the 85 Kr can be a good tracer since it is an inert gas with a long half-life (τ 1/2 =10. 7 years) and its radioactive decay negligible at these distances. The main sources of the 85 Kr in the atmosphere are reprocessing plants of spent nuclear fuel, from which the 85 Kr release can be known with a reasonable accuracy. As an example, the work conducted in the Laboratory of Radioecology in Cherbourg (LRC) of IRSN, presented by  Connan et al.
(2013) , is one of the rare studies that explored the dispersion of radionuclides at distances between 5 km and 50 km. In this previous paper, continuous 85 Kr measurements at 1 minute time period were carried out at three stations and combined with well-known discharge data which were provided by the nuclear fuel reprocessing plant of Orano La Hague (later called RP), located in the North-Cotentin peninsula (north-western France). These data were then used to perform dispersion simulations, but, with only three stations, the dataset was not large enough to capture the spatial spread of released radioactive material. From these previous studies arose the need for a campaign using more observation stations, spatially representative of the area, along with a longer time-period to make the conclusions more statistically robust.
This dataset is presented for the first time in this paper, and is used to evaluate the probabilistic performance of ensemble dispersion simulations. Thus, the originality of this work can be summarized in three points: (i) the use of a unique and original dataset of continuous data of 85 Kr air concentration measurements (every 1 minute or 10 minutes) over a relatively long period, (ii) the evaluation of an ensemble of dispersion simulations using a fine-scale meteorological ensemble with in-situ observations, (iii) an innovative method developed to assess the probabilistic performance of the dispersion ensembles. The outline of the article is as follows: in Section 2, the source term, the observations and the models used in the study are described. Section 3 presents the verification of AROME-EPS against wind measurements, and then in Section 4 the ensemble dispersion simulations are presented and discussed.
IRSN's LRC laboratory regularly performs meteorological and radiological measurements in the framework of measurement campaigns. Additional meteorological and air concentration measurements are carried out by Orano for the environmental monitoring of the RP. For these reasons, the La Hague experimental site is an ideal environment for the study and validation of atmospheric dispersion simulations. Past validation studies conducted in this framework have shown that dispersion simulation results are quite sensitive to the meteorological data used as input  (Connan et al. , 2013). The North-Cotentin peninsula of La Hague is a rocky area of approximately 15 km long and 5 km wide, surrounded by the sea (Fig. 1 ). Such a complex terrain leads to spatially heterogeneous wind fields that may be difficult to accurately forecast.
Since November 2020, IRSN has been carrying out a continuous 85 Kr air measurement campaign in several locations chosen at different distances and directions from RP, as part of the DISKRYNOC project. This project aims to provide a comprehensive new observational dataset for model validation purposes. In this study, this dataset is used to acquire feedback on the use of meteorological ensembles to quantify the associated uncertainties. Gréville. The IRSN is the owner of the first five stations, while the last three are Orano stations. The measurements have been carried out since mid-November 2020 and are expected to extend over approximately 18 months. This extended time period should provide a significant number of observations at all measurement sites. It should compensate for periods without reprocessing activity or with a wind direction towards the sea.
using a different member of the AROME-EPS ensemble as input, along with the source term data provided by RP La Hague. This allows to generate an ensemble of dispersion simulations composed of 16 members (hereafter called the pX ensemble). Furthermore, in order to quantify the benefit of using ensembles instead of deterministic simulations, an additional pX simulation was performed using the deterministic weather forecast from AROME as input of the model. This simulation is called deterministic pX in the following.
The probabilistic consistency of the two resulting dispersion ensembles was then compared using an innovative method of temporal discretization by sliding intervals, and by calculating two probabilistic scores: ROC curves and PSS. This evaluation process was performed in two parts. First, by comparing the overall performance of the two configurations by aggregating the data from all the measurement stations. In this case the best results were obtained with Pasquill standard deviations. Secondly, by comparing the performance of the two configurations in the near fields (stations located less than 10 km from the source) and far fields (stations beyond 10 km from the source). The results showed that the Pasquill simulations were still the most consistent with observations. In all cases studied, the best decision threshold is 3 members, and the ensembles performed better than the deterministic simulations.
It thus remains unclear whether there are any generalities in diet composition, food plant partitioning, or trophic network structure among savanna herbivores. To plug this gap, we used DNA metabarcoding to assess the diets of individuals and populations of 30 species in 10 savannas, 4 of which we sampled in multiple seasons and years. We explored this unique dataset for general patterns in herbivore diet composition and diversity. We hypothesize that competition and differences in herbivore and plant functional traits give rise to food partitioning at the level of plant species, which stabilizes coexistence and structures trophic networks. This hypothesis implies support for four specific, testable predictions. 1) Large-herbivore assemblages eat many taxa, and each population eats only a subset, although the identity of that subset may vary in space and time (for grazers, browsers, and mixed feeders alike).
2  C and D). The predominant "other" families varied across sites and included Malvaceae, Acanthaceae, Rosaceae, Combretaceae, Myrtaceae, Phyllanthaceae, Rhamnaceae, Euphorbiaceae, Asteraceae, and Anacardiaceae (Datasets S1-S24). Generality of Food Plant Partitioning in Space and Time (Prediction 2). Across all sites and bouts, herbivore species' diets were compositionally distinct from those of most, if not all, other sampled species. At the assemblage level, the generality and repeatability of plant taxon partitioning is clear from nonmetric multidimensional scaling (NMDS) ordinations of dietary dissimilarity between individual fecal samples: With few exceptions, species formed discrete clusters of points, reflecting differences in the identity and RRA of food plants (Fig. 3  and SI Appendix, Fig.
05; SI Appendix, Dataset S25); these included 89 pairs where we relaxed sample size to n < 10 for one or both species (Methods), but the result was the same without those pairs (619 of 634 significant, 98%; SI Appendix, Text S2). Ecological Context and the Strength of Food Plant Partitioning (Prediction 3). While almost all pairwise differences were statistically significant, their strength varied (as indexed by the per-MANOVA r 2 , the variance in dietary dissimilarity attributable to species identity; SI Appendix, Fig. S10B ). To explain this variation, we analyzed the r 2 values using Akaike information criterion (AIC c )-based selection of 16 linear mixed-effects models with random intercepts for site (SI Appendix, Text S2); fixed effects included rainfall and the difference between each pair of species in body mass, digestive system, and grass RRA.
The top model (Akaike weight = 0. 56, marginal r 2 = 0. 28, conditional r 2 = 0. 54) included grass RRA, body mass, and rainfall; the negative coefficient of rainfall indicates that dietary differences diminish as food availability increases, consistent with our prediction (SI Appendix, Table  S4  and Fig. S11A ). Among sites, plant partitioning was weakest in Gorongosa, where the dominant herbivore species (waterbuck, reedbuck, and warthog) ate individually variable and broadly overlapping diets (Fig. 3F  and SI Appendix, Fig. S6C ). Gorongosa accounted for 13 of the 23 nonsignificant pairwise contrasts (and 11 of 15 with n ≥ 10 for both species); waterbuck, reedbuck, and warthog accounted for 11 of these. The remaining 10 nonsignificant contrasts included 1 from Laikipia, 7 from Serengeti, and 2 from Hwange, and 6 of these were among the 89 with limited power owing to inclusion of species with n < 10 samples (Dataset S25).