dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). 3   In the present paper, we address this issue by mobilizing regional data from several Latin American and African countries. We measure how daily mobility to work has changed after the implementation of lockdown policies depending on the subnational level of poverty. We conjecture that in the poorest regions, work mobility is higher than in other regions due to a lower degree of compliance to lockdown measures or, more generally, less opportunity for voluntary containment. Note that several independent studies suggest a similar approach by looking at regional differences in wealth or economic activity. Yet, they all focus on specific countries. For instance,  Durizzo et al. (2021)  use a recent survey conducted in two poor neighborhoods of Ghana and South Africa. They find that the main factor that impedes cooperation with containment policies is poverty and the lack of infrastructure.
Mobility We use daily human mobility data from Google COVID-19 mobility reports, which aggregate anonymized data sets from users' mobile device Location History. These reports record percent changes in the number of visits or length of stay at various locations compared to a reference period of January 3 -February 6, 2020. There are six location categories: (i) retail and recreation, (ii) grocery and pharmacy, (iii) parks, (iv) transit stations, (v) workplaces, and (vi) residential areas. We focus on a subset of the Google mobility data covering subnational regions of nine countries in Africa (Egypt, Kenya, Nigeria, South Africa) and Latin America (Argentina, Brazil, Colombia, Mexico, Peru) for a period from February 16 to April 26, 2020.
Poverty We combine mobility data with poverty statistics at the level of subnational regions. Poverty is measured as headcount ratios (the share of people living below national or international poverty lines in a region). For graphical analysis, we use discretized versions of regional headcount ratio: binary and terciles. A binary poverty measure takes the value one if the poverty headcount ratio is above the national average of regional poverty rates, and zero otherwise. Tercile measures countries and regions. For further detail, see Google LLC (2020). 6 We transform the mobility data, for a matter of convenience, from the percent changes into an index on a 0-100 scale, where the reference level of mobility is equal to 100. For example, work-related mobility with the value of 85 for the governorate of Cairo on March 20 corresponds to a 15 percent decrease in mobility for this type of activity and this place compared to the reference period.
are a set of dummy variables defining levels of regional poverty as low (below 25th percentile of regional poverty rate within a country), medium (between 25th-75th percentiles), and high (above 75th percentile). For estimations, we use both regional poverty rate directly, as a continuous measure of poverty, and its discretized versions. We rely on official poverty statistics and, when missing (i. e. for Nigeria and South Africa), on our calculations using recent available household surveys. Datasets and methodological choices are explained in much detail in Table  A. 1 in the appendix. 7 Note that our results are not very dependent on these methodological choices, especially the choice of the poverty line: our difference-in-difference (DID) approach essentially compares regional time variation in poverty (controlling for regional fixed effects), rather than differences in poverty levels across regions.
Africa Source: author's calculations based on Google mobility data (mobility for workplace) and poverty data from national statistics offices and authors' estimations using household surveys. Local polynomial fit with 95% CI of daily mobility across regions, weighted by (1/# of regions in the corresponding country). Poverty is measured as the share of people in region living below national/international poverty lines. % of poor is defined as low if region's poverty rate is below 25th percentile of regional poverty rates within country, medium if between 25th and 75th percentile, and high if above 75th percentile.
Workplace vs Transit stations Source: author's calculations based on Google mobility data and poverty data from national statistics offices and authors' estimations using household surveys. Local polynomial fit with 95% CI of daily mobility across regions, weighted by (1/# of regions in the corresponding country). Poverty is measured as the share of people in the region living below national/international poverty lines. % of poor is defined as low (high) if region's poverty rate is below (above) country's average poverty rate. In the first graph of Figure  2 , we see that mobility fluctuates around 100 in late February and early March, that is, around the same level as in the reference period. All poverty groups show similar mobility patterns during this early stage, both in trends and levels.
In Columbia and Peru, smartphone ownership rate was 53% and 36% respectively, according to our calculations based on the 2018 Latinobarometer. Although still lower than in rich countries, digital and mobile connectivity has rapidly expanded in middle and low income nations over the last years. 17 Moreover, the latest statistics for mobile penetration rates in these countries show that cellular subscriptions have reached an average of 115 per 100 people (see Table  A. 3 in the appendix). Overall, these statistics reassure us that Google mobility data does not represent a marginal share of the populations covered in our analysis. Furthermore, it is likely that we underestimate mobility differences across regions. Indeed, GLH information may capture the mobility of the least poor within poor regions, i.
Implications for the Spread of COVID-19 in Africa and Latin America Finally, we attempt to provide suggestive evidence on how poverty translates into a higher spread of COVID-19 through increased work mobility. Note that the following calculations are purely indicative. Hereafter, we use daily mobility data and the cumulative number of reported COVID-19 cases for the period from March 20-April 26, 2020. We first establish how the upcoming growth rate of COVID-19 responds to the instantaneous mobility index, reflecting the time and spatial variation in behavioral responses to lockdown policies. For each day, we compare the current cumulative number of reported COVID-19 cases to that of 2 weeks ahead, 18 and divide the corresponding growth rate by 14 to obtain an average daily growth rate of upcoming COVID-19 cases.
Africa Source: author's calculations based on Google mobility data (mobility for workplace) and poverty data from national statistics offices and authors' estimations using household surveys. Local polynomial fit with 95% CI of daily mobility across regions, weighted by (1/# of regions in the corresponding country). Extreme poverty is measured as the share of people in region living below national/international extreme poverty lines. % of extremely poor is defined as low if region's poverty rate is below 25th percentile of regional poverty rates within country, medium if between 25th and 75th percentile, and high if above 75th percentile.
26 # accesses per 100 inhabitants Ministry of Information Technologies and Communications 3rd quarter 2019 Egypt 95. 59 # accesses per 100 inhabitants Ministry of Communications and Information Technology February 2020 Kenya 114. 8 # SIM per 100 inhabitants Communications Authority of Kenya December 2019 Mexico 95. 7 # service lines per 100 inhabitants Federal Telecommunications Institute 3rd quarter 2019 Nigeria 98. 9 # active telephone connections per 100 inhabitants Nigerian Communications Commission February 2020 Peru 127. 6 # mobile phone lines per 100 inhabitants National Institute of Statistics and Informatics September 2018 South Africa 159. 93 # cellular phone subscriptions per 100 inhabitants ITU World Telecommunication/ICT Indicators database 2018 Note:.
For that purpose, an aluminum core of 392. 5 cm 3 is used to collect samples at the TDR installation depths. Three samples are collected per day and per field during 5 d chosen with different soil moisture conditions in order to cover a wide range of values (0. 08 to 0. 33 m 3 m -3 ). A linear regression is established between the volumetric water content and the square root of the TDR time response (named τ , in seconds) as follows: SSM = a TDR × √ τ + b TDR. (1) The calibrated values using data of both fields are a TDR = 0. 275 m 3 m -3 s -0. 5 and b TDR = -1. 154 m 3 m -3. Figure 3  illustrates the calibration results with all the samples displayed. The statistical metrics are the correlation coefficient R = 0. 97, root mean square error RMSE = 0. 018 m 3 m -3 and no bias. When considering both fields separately, the results for (F1, F2) are R = (0.
Funding: This work has received funding from the  European Institute of Innovation and Technology (EIT Raw Materials) , a body of the  European Union , under the  Horizon 2020 EU framework programme for Research and Innovation  (project agreement no. 19145-DIGISER ++ ). The official website of the DIGISER ++ project is https://digiser. tech/,accessed on 20 October 2021. The  French Agency for Research (ANR)  is also acknowledged for partial funding of the GENEPI platform within the framework of the investments of future (Ref:  ANR-11-EQPX-0019 ).
Sones and soil were put back in the pit, respecting the layer order. The volumetric water content measured by each sensor was converted to millimetres after accounting for the proportion of stones in the corresponding soil layer. Given the high soil heterogeneity, the limited number of probes may not be sufficient to yield representative soil moisture. Additionally, these measurements do not cover the full depth explored by the roots. Actually, two thirds of the water holding capacity being contained deeper under the bedrock. We thus used additional simulations of soil moisture dynamic under control and exclusion treatment by using the forest stand water balance model Biljou©  (Granier et al. , 1999). This model computes the different component of the water balance at a daily time scale using a set of meteorological data (wind speed, relative humidity, precipitation, radiation and temperature) as well as soil and stand characteristics (number of layer and their soil bulk density, maximum extractable water and wilting point by layer, LAI).
Plant area index Plant area index was regularly monitored using hemispherical photographs taken every one to two months. For the present study, we used only data taken during summer, at the peak of foliage growth. Photos were taken at eight locations in each plot, roughly following a regular 5 × 5 m grid (i. e. the distance between photos could vary between 7 and 10 m depending on the presence of stumps that precluded the installation of the camera). From 2008 to 2015, we used a Canon 5D camera equipped with a sigma 8 mm EX DG fisheye lens, and from 2015 to 2018 a Nikon D3200 camera with a sigma 4. 5 mm EX DC HSM fisheye lens. The camera was positioned skywards and oriented so that the north would be at the top of the picture. The camera was mounted 2 m above the ground on a self-levelling platform (manufactured at INRAE-URFM, Avignon, France), to avoid any influence of the gutters or of understorey vegetation.
Unusually tall understorey plants proved nonetheless to be a problem in some locations, and these were excluded from the analysis. The analysed data come from 6 locations in the rainfall exclusion plot and 12 locations in the control plots. Photos were taken when the sun was low (mostly at sunset) or, very rarely, when the sky was uniformly overcast. They were taken in RAW format, and later converted with the software DCRAW with no gamma correction. The images were processed and analysed using a set of macros written for the ImageJ software  (Schneider et al. , 2012) : extraction of the blue channel, automatic contrast adjustment, manual double thresholding, and calculation of the gap fraction and of the plant area index (PAI). Thresholding was carried out as described by  Leblanc et al. (2005)  and the PAI was calculated using Miller's formula, as approximated by  Welles and Norman (1991).
For each ERT measurement, nail probes were connected via electrodes to the resistivity system. The system automatically determined the appropriate electric voltages to apply between all MPs, measured the intensity and deduced the mean resistivity between the nails. Then, all data (geometry and ERT) were sent to the TreeTronic expert software Q74 (Argus Electronic Gmbh) on a laptop for a 2D reconstruction step. The R script provided by  Benson et al. (2019)  under the supplementary material was then used to build electrical resistivity tomographs (Fig. S2-S3 ) with a colour scale ranging from blue (low resistivity) to red (high resistivity) and calculate sapwood depth and area, considering that the heartwood-sapwood boundary is characterized by a threshold resistivity value. This value is identified by the algorithm as the steepest change in resistivity with depth in radials profiles  (Guyot et al.
Based on  Cohen et al. (2008) , sap velocities decreases with sapwood depth and is highest inner the first 2 cm of sapwood for both species, where probes are located. This gives confidence that granier sensors can detect shifts in sap velocity due to environmental factors. The upper probe was heated constantly using a 0. 12 A current. Reference measurements of the sapwood temperature were taken from the lower probe. The difference in temperature between the probes (∆T) was recorded at 30 min intervals. Disfunctionning sensors were replaced and the affected data were excluded. Data were also lost because of power failure (animal predation, battery failure). Anomalies in ∆T fluctuations were visually identified by overlaying the measurements with potential evapotranspiration (PET) data (calculated using the Penman-Monteith equation).
After 3D-reconstruction, the final spatial resolution of the 3D images was 2. 5 µm. One transversal 2D slice was extracted from the middle of the volume using VGStudio Max© software (Volume Graphics, Heidelberg, Germany). The slice was then analysed using the software ImageJ  (Schneider et al. , 2012) , to estimate the surface area of embolized conduits. To estimate the embolism ratio for P. halepensis, we developed a process consisting in separating areas with embolized tracheids from areas with water-filled tracheids, which are easy to identify based on colour thresholds. The embolism ratio was then calculated as the proportion of the total cross-sectional area containing embolized tracheids  (Choat et al. , 2015; Torres-Ruiz et al. , 2016). This procedure assumes that all tracheids have similar diameters and thus similar hydraulic conductivities.
The explanatory variables were the treatment, the diameter at breast height (DBH) at the beginning of the rainfall exclusion period (i. e. 2009), and the interaction between these variables. The primary growth data were analysed by comparing annual values for each branch type. The first two years of data were excluded as they contain most of the age-related signal (Fig. A2 ). Finally, we tested for a time effect on the evolution of PAI under both treatments, and on the difference in PAI between treatments, using a simple linear model with time as the explanatory factor. All statistical analyses were performed with the software R (3. 5. 2, R Development Core Team 2018).
Exclusion effects on growth Our data support that the rainfall exclusion negatively affected the different estimates of growth we considered, in accordance with the high sensitivity of cell growth to drought  (Guillemot et al. , 2015; Lempereur et al. , 2015). For P. halepensis, lower needle lengths were recorded from the very first year of the experiment onwards. On average, needles were shorter in the exclusion plot by 15. 0% in 2009, and by 15. 7% over the entire period. This direct effect on needle length is consistent with the results of several studies (e. g. Adams et al. , 2015; Myers, 1988)  that have shown that needle growth depends strongly on the water potential, notably for P. halepensis  (Borghetti et al. , 1998). The impact of rainfall  exclusion on shoot length, and to a lesser extent, on the number of lateral ramifications, was delayed by at least one year (Fig.
For Q. ilex, the dataset was more limited and a drought effect was only observed in the number of ramifications of top branches, which slightly decreased in trees subjected to the exclusion treatment (Fig. A4 ). This decrease is in agreement with previous studies on this species  (Limousin et al. , 2012; Martin-StPaul et al. , 2013; Ogaya et al. , 2006). Measurements of the PAI were in line with the primary growth data, indicating a progressive reduction in the overall stand foliage cover in the exclusion plot. Indeed, while the PAI in the exclusion plot was higher before the start of the experiment, this difference decreased as the experiment went on, indicating a progressive reduction in leaf area under rainfall exclusion. The results obtained for basal area increment were not fully consistent with those on primary growth, as basal area growth was only significantly reduced under rainfall exclusion in Q.
Acknowledgements The data analysis and writing for this study were supported by the  French Environment and Energy Management Agency (ADEME)  in the form of a PhD scholarship. During the long study period, experiments were funded by the  French National Research Institute for Agriculture, Food and Environment (INRAE) , the  Agence Nationale pour la Recherche  ( ANR  project  Drought+  no  ANR-06-VULN-003-04 ;  ANR  project  HydrauLeaks  no  ANR-18-CE20-0005 ), the long term  French observatory networks SOERE F-ORE-T and Analysis and Experimentation on Ecosystems  ( AnaEE  no  ANR-11-INBS-0001 ). The authors thank  Damien Gounelle ,  Arnaud Jouineau ,  Jean-Michel Lopez ,  Mathieu Audouard , and  Roland Esteve , from  INRAE , for their contributions to the field work. The authors also thank the  INRAE PHENOBOIS centre (Clermont-Ferrand, France ) and  Pierre Conchon  for help with the X-ray microtomography analysis.
This pattern might be simply the result of a difference between extractability of Ca, Mg and Na, with the first two being highly soluble in AR, while Na has low extractability. This would indicate that the pattern is not related to the type of source parent materials. (yellow circles) according to  Parker (1967)  and  Rudnick and Gao (2003) , respectively. Calcium, Mg and Na Ap data from (a) XRF and (b) aqua regia.
5%) are significant, whereas basalt and felsic volcanic rocks (0. 5% each) play a subordinate role. The Mg distribution is further depicted for chalk (carbonates) and granite, according to the geological parent material subgroups constructed for processing the Ap soil results from the GEMAS data set  (Birke et al. , 2014b). Figure  12a   (1967). This reflects the capability of AR digestion to extract Al from the clay fraction in soil. Secondly, the Al concentrations lie well above the 1:1 line and mainly reflect low Al aqua regia extractability in soil (ca. 20%), with the XRF data showing substantially higher values than AR results. This may have implications for the weathering patterns in carbonate-rich soil where Mg is the mobile and Al the residual part, resulting in Mg depletion and Al enrichment in agricultural soil.
Acknowledgements The  GEMAS  project is a cooperative project of the EuroGeoSurveys Geochemistry Expert Group with a number of outside organisations (e. g. , Alterra, The Netherlands;  Norwegian Forest and Landscape Institute; Research Group Swiss Soil Monitoring Network, Swiss Research Station Agroscope Reckenholz-Tänikon, several Ministries of the Environment  and  University Departments of Geosciences, Chemistry and Mathematics  in a number of European countries and New Zealand; ARCHE Consulting in Belgium;  CSIRO Land and Water in Adelaide, Australia). The analytical work was co-financed by the following industry organisations: Eurometaux, European Borates Association, European Copper Institute, European Precious  Metals Federation, International Antimony Association, International Lead Association-Europe, International Manganese Institute, International Molybdenum Association,  International Tin Research Institute ,  International Zinc Association , The  Cobalt Development Institute, The Nickel Institute, The (REACH) Selenium and Tellurium Consortium and The (REACH) Vanadium Consortium.
Cassard, D. , Bertrand, G. , Maldan, F. , Gaàl, G. , Kaija, J. , Aatos, S. , Angel, J. M. , Arvanitidis, N. , Ballas, D. , Billa, M. , Christidis, C. , Dimitrova, D. , Eilu, P. , Filipe, A. , Gazea, E. , Inverno, C. , Kauniskangas, E. , Maki, T. , Matos, J. , Meliani, M. , Michael, C. , Mladenova, V. , Navas, J. , Niedbal, M. , Perantonis, G. , Pyra, J. , Santana, H. , Serafimovski, T. , Serrano, J. J. , Strengell, J. , Tasev, G. , Tornos, F. , Tudor, G. , 2012. ProMine pan-European mineral deposit database: a new dataset for assessing primary mineral resources in Europe. Workshop on: Mineral Resources Potential Maps: A Tool for Discovering Future Deposits. 12th-14th March 2012, Nancy, France, Proceedings, pp. 9-13.
Cassard, D. , Bertrand, G. , Billa, M. , Serrano, J. J. , Tourliere, B. , Angel, J. M. , Gaal, G. , 2015. ProMine mineral databases: new tools to assess primary and secondary mineral resources in Europe (Chapter 2). In: Weihed, P. (Ed. ), 3D, 4D and Predictive Modelling of Major Mineral Belts in Europe. Mineral Resource Reviews. Springer International Publishing, pp. 9-58. https://doi. org/10. 1007/978-3-319-17428-0_2.
Mackovych, D. , Lucivjansky, P. , 2014. Preparation of GEMAS project samples and standards. In: Reimann, C. , Birke, M. , Demetriades, A. , Filzmoser, P. , O'Connor, P. (eds. ), 2014. Chemistry of Europe's agricultural soils -Part A: Methodology and interpretation of the GEMAS dataset. Geologisches Jahrbuch (Reihe B), Schweizerbarth, Stuttgart, 37-40.
Numerical integration We implemented DynaGraM in R language (R Core  Team, 2020). We numerically solved the ODE system with the function 'ode' in the R package 'deSolve'  (Soetaert et al. , 2010)  using the Euler method and an integration time step of one day. The appropriateness of the time step was verified by comparing standard outputs with those obtained from running the model with a smaller time step. The pertinence of the numerical solution scheme was further tested by replacing 'ode' with the function 'lsoda' in the same R package, the latter being an ODE solver that automatically switches between stiff and non-stiff methods. No sensible difference in the solution was detected. However, the choice of a discrete-time integration method (Euler solver with dt = 1) can lead to negative biomass values after defoliation events.
Model variables and parameters To show the model ability to capture the response of grassland communities to management practices, we considered a fictitious grassland illustrative of European mountain pastures and hayfields. This theoretical grassland does not correspond to a real specific site, but vegetation, climate, soil, and land-use have been specified to be characteristic to those commonly observed in the Pontarlier region (French Jura Mountains, at 900 m a. s. l. elevation). In this region, permanent grasslands represent a large fraction of the agricultural area. They are extensively to intensively managed to sustain dairy farming for PDO cheese production  (Mauchamp et al. , 2014). We retrieved climatic data for Pontarlier from the E-OBS v19. 0e database  (Cornes et al. , 2018). We selected, within this climatic time series covering the period 1981 to 2018, the year 2004 to be representative of the climatic conditions in this area.