dataesr/scientific-paragraphs-categorization huggingface dataset:
| Typology of land cover changes We selected five clusters of land cover changes for kmeans clustering. These five clusters explained 45% of the variance of the dataset (Figure  5 ). The first cluster included 14 farms (4% of the studied farms) in which the proportion of temporary grassland area increased (+21% of total farm areas), compensated by a decrease in winter wheat, maize, rapeseed, permanent grassland and barley area proportions (-8%, -4%, -4%, -4% and -3%, respectively). The overall proportion of grassland area (temporary and permanent) in this cluster increased (+17%). The second cluster included most of the studied farms (247, 67% of the farms) in which almost no land cover changes occurred. The third cluster included 48 farms (13% of the F I G U R E 3 Distribution of the difference in crop area proportions per farm associated with a biogas plant after and before biogas plant start-up at the France scale and per control surrounding area.
| Interest in national databases to study land cover changes associated with biogas plants A recent report of the French Senate pointed out the lack of information about land cover changes caused by the development of anaerobic digestion in France  (Salmon, 2021). We therefore combined land cover data (LPIS) with data about the location of biogas plants to provide a comprehensive picture of these land cover changes at the national scale. Interest in the use of LPIS data to track land cover changes over time has been highlighted in various contexts by several authors  (Levavasseur et al. , 2016; Zimmermann et al. , 2016). Lüker-Jans et al. (  2017 ) used LPIS to track land cover changes caused by the development of anaerobic digestion in Germany at the municipality scale. However, in comparison to their study, we identified the farm areas associated with biogas plants.
facilement disponibles lorsqu'ils sont valorisés en agriculture. L'introduction d'un méthaniseur dans une exploitation agricole peut induire des changements d'assolement, du fait de la production de matières premières pour la méthanisation et/ou de la modification de son système de production. L'objectif de cette étude était donc de caractériser et de quantifier les changements d'assolement dans les exploitations agricoles associées à des méthaniseurs en France. Nous avons combiné deux bases de données nationales spatialisées: le registre parcellaire graphique (assolement annuel du territoire français à l'échelle de la parcelle avec identifiant d'exploitation par parcelle) et la base de données SINOE (localisation du méthaniseur et année de mise en service). Nous avons montré qu'en moyenne, les changements étaient limités avec une augmentation des surfaces en maïs (+3.
DATA AVAILABILITY STATEMENT The data that support the findings of this study are openly available: (i) in the SINOE database for biogas plant location: https://eci-sig. ademe. fr/adws/app/bb11c e07-5cc9-11eb-a8fe-7dd6c 4f9bb 1d/index. html; (ii) on the Open platform for French public data for the LPIS data (LPIS version without farm identifier, the version with farm identifier has restricted access for privacy concerns): https://www. data. gouv. fr/fr/datas ets/regis tre-parce llair e-graph ique-rpg-conto urs-des-parce lles-et-ilots -cultu raux-et-leur-group e-de-cultu res-major itair e/; (iii) on the Agreste website for "régions agricoles" (RA) limits: https://agres te. agric ulture. gouv. fr/agres te-web/metho don/Z. 1/!searc hurl/liste TypeM ethod on/.
Data acquisition and data reduction Observations of the ATOMIUM sources were performed with VLT/SPHERE-ZIMPOL between 2019 April 7 and A96, page 2 of 22  (b)  18. 68 2700 (6) 1. 9 × 10 -5 (6) Notes. The stars listed in the second part of the table were not observed with ZIMPOL due to their faint magnitude in the R band. ( a) The R magnitudes were obtained from the USNO-B catalog  (Monet et al. 2003 ). ( b) These angular diameters were derived from the bolometric luminosity, effective temperature and distance  (Decin et al. 2020 ). ( c) These angular diameters were derived using the magnitude-color relation of  van Belle et al. (1999). ( d) Mass-loss rates from references (  5 )-(  10 ) and (  12 ) are derived from the 1D modeling of their CO rotational line emission. For VX Sgr, in (  17 ), the authors model the thermal emission from dust from the optical to the far-infrared, and assume a gas-to-dust ratio of 200 from the litterature.
The complete characteristics of ZIMPOL's filters are available on ESO's dedicated website 2. Each target observation was followed by the observation of a reference spatially unresolved star (usually a K giant), used as proxy of the point spread function (PSF). We chose stars with R magnitudes close to the ATOMIUM sources in order to have a similar setup for the AO system. The data were reduced through the SPHERE DataCenter  (Delorme et al. 2017; Galicher et al. 2018). Custom Python routines were applied to derive the polarimetric observables (Stokes I, polarized flux, degree of linear polarization (DoLP), and polarization angle; see  Kervella et al. 2015  for more details). However,  Engler et al. (2017)  state that the polarized flux (and the DoLP) are affected by a systematic bias when the signal-to-noise ratio (S/N) is weak.
(2021). W m -2 µm -1 arcsec -2. A96, page of 22 features are visible. We note that U Del's PSF calibrator, namely HD 195835, is actually a binary system, which was suggested, after our observations were executed, by its high Gaia Early Data Release 3 (EDR3) renormalized unit weight error (RUWE) of 13. 7 from  Kervella et al. (2022). We note that it is currently listed as an interferometric calibrator in  Bourgés et al. (2014) ;  Cruzalèbes et al. (2019). The angular separation between the two centroids in the CntHα filter is 74. 2 mas. With a parallax of 2. 66 ± 0. 23 mas (Gaia Collaboration 2023), this translates into a separation of 27. 8 ± 2. 4 au. From now on, and in subsequent ATOMIUM-ZIMPOL papers, we will use R Hya's PSF reference (HD 121758, observed three days earlier with the same filter) instead if we need to deconvolve the intensity images.
Data analysis To characterize the observed extension of the ATOMIUM sources and their PSF references, we fitted the intensity images with a circular 2D Gaussian. The resulting derived full widths at half maximum (FWHMs) are presented in Table  2. Their comparability to the 20 mas theoretical angular resolution of an 8. 2 m telescope in the R band is a good proxy of the quality of the AO correction (i. e. , the Strehl ratio). In most cases (except U Del, GY Aql, and SV Aqr), the size of the PSF calibrator is smaller than the scientific source, and in the range 25-30 mas. In the case of U Del (for which we fitted a two component PSF on HD 195835) and SV Aqr, the PSF reference is slightly larger than the main source, suggesting a slight evolution of Earth's atmospheric conditions (hence the AO correction).
Polarization signature modeling using RADMC3D In order to evaluate the effect of the dust properties and distribution on the DoLP measured by ZIMPOL, we ran 3D dust radiative transfer simulations. We used the publicly available code RADMC3D 3   (Dullemond 2012). The DoLP in the vicinity of the ATOMIUM sources shows very diverse values (between a few percent and ∼40%) and morphologies. It is beyond the scope of the present study to attempt to reproduce in detail the DoLP for each individual star. Instead, we aim to characterize the effects of the various circumstellar parameters on the polarized signal. We computed the simplest model possible: a spherical dust clump placed around a typical AGB star from the ATOMIUM sample. Using this common model, we interpret the observations of the ATOMIUM sample as a whole in Sect.
Effect of the PSF convolution on the polarization For these preliminary tests we used a clump of 4 au in radius, with a constant dust density of 10 -17 g cm -3 (a value in the range derived for clumps offset cool evolved stars; e. g. ,  Cannon et al. 2021; Montargès et al. 2021 ). The clump was located at 10 au from the star center, exactly to the east in the plane of the sky through the center of the star. For this setup, the dust consisted only of olivine, with grain sizes ranging between 0. 01 and 0. 1 µm. RADMC3D produces images in the three Stokes parameters (I, Q and U). To allow a meaningful comparison with observational data, these images needed to be convolved with the PSF. The accompanying Python module radmc3dPy provides tools to perform the PSF convolution by a 2D Gaussian or an Airy pattern corresponding to a circular aperture with a central obscuration (caused by the secondary mirror).
Additionally, the ZIMPOL observations were obtained close to the stars, where we expect dust nucleation to start. For this smallest grain sizes on the order of 0. 01 µm considered in this study, we note that finite size effects like large surface-tovolume ratios and atomic segregation can impact the structure of the grain. As a consequence these small size grain properties, including their optical constants, could be different from those in the Jena database. Overall, the wavelength dependence of the DoLP appears related to the grain size, at a given dust composition. With a broader wavelength baseline (e. g. , extending to the infrared), it would be possible to discriminate this dust parameter. Regarding dust composition, our observations do not allow us do draw decisive conclusions: when taking the other parameters studied in the previous paragraphs into account, it appears that olivine and corundum, or forsterite and enstatite, produce rather similar signals.
First we note that for U Del, V PsA, and SV Aqr the MIR excess is negative. This is unphysical and could arise from erroneous estimates of their angular diameter or their effective temperature, or from variability of their photometry. Most stars are in a single group, whatever their MIR excess is: they show a mean DoLP of 0. 01-0. 04. This indicates that whatever their warm dust content is, the observed dust in polarization (i. e. , in the plane of the sky) in the inner and intermediate circumstellar environments is similar (and relatively low in density, producing an optical depth close to unity) among these sources. This may imply that the ZIMPOL data are catching newly formed dust. In this inner region, dust likely forms only in favorable (density, temperature, pressure) areas. This could explain the clumpy shapes that are observed in most ZIMPOL DoLP maps.
Comparison with ALMA data Through molecular emission lines, ALMA is sensitive to the gas content of the circumstellar environment. In the continuum, the maps are dominated by the central star but are also sensitive to warm dust close to the star. Except for π 1 Gru  (Homan et al. 2020) , there is no detection of spatially resolved dust features in the ATOMIUM ALMA continuum maps. Here we compare the DoLP detections with ZIMPOL, with the rotational lines observed with ALMA. We chose to focus our discussion on the CO = 0, J = 2-1 and SiO = 0, J = 5-4 lines. Owing to its low dipole moment  (Chołuj & Bartkowiak 2016) , CO is mostly insensitive to any radiation field. Da Silva Santos et al. (  2019 ) state that infrared pumping of ground or vibrationally excited states should be minimal in the inner regions of AGB envelopes -therefore the high gas densities close to the star ensure that CO is mainly excited through collisions.
We do not discuss the data on the three RSGs (AH Sco and KW Sgr because their HIPPARCOS parallax is negative, and VX Sgr because its Gaia EDR3 parallax is most certainly biased). We note that for the AGB stars the S/N of the proper motion anomaly is larger than 3 only for R Hya, π 1 Gru, R Aql, and GY Aql. π 1 Gru's companion is detected by  Homan et al. (2020) , which allows us to identify the coma shape of the DoLP as a dust tail formed in the wake of the companion's motion. For GY Aql, we saw a correlation between the ALMA maps and the DoLP, which suggests that the dust we detect is at the head of spiral arms, implying that there might be structures that are created by an as-yet undetected companion. For R Hya and R Aql, no feature can be conclusively associated with the presence of a potential companion either in the ZIMPOL observations or in the ALMA data.
2009). They are more likely to dominate over the others. 3. The dependence of the polarization on wavelength is a probe of the size distribution of the grains, with small, intermediate-sized, and large grains respectively showing a negative, flat, and positive slope in the wavelength interval studied here (from 645 to 817 nm at best). In the part of the sample for which multiwavelength data are available, the grains seem either small (0. 01-0. 1 µm) or possibly intermediate sized (0. 01-1 µm), but likely not large (0. 1-1 µm). However, in order to place firmer constraints on the size range, a wider wavelength baseline for comparison would be useful, for example toward the infrared with the InfraRed Dual-band Imager and Spectrograph (IRDIS) subunit of SPHERE. We compared the spatial structure of the dust, which gives rise to the polarized radiation observed with SPHERE-ZIMPOL, to the spatial structure of the gas by observing the CO = 0, J = 2-1 and SiO = 0, J = 5-4 lines with ALMA in the ATOMIUM project.
) are not consistent with such an outflow. The dust properties were taken from the Jena Database of Optical Constants for Cosmic Dust 4. The following five dust species -commonly found in the environment of cool evolved stars -were input individually in separate simulations with the RADMC3D code: glassy MgFeSiO 4 (olivine, Jaeger et al. 1994; Dorschner et al. 1995), glassy Ca 2 Al 2 SiO 7 (melilite, Mutschke et al. 1998), Al 2 O 3 (corundum, Zeidler et al. 2013), amorphous MgSiO 3 (enstatite, Jäger et al. 2003), or amorphous Mg 2 SiO 4 (forsterite,.
Acknowledgements. We would like to thank  Dr. Julien Milli  for useful discussions in handling the polarized signal from ZIMPOL. Based on observations collected at the  European Southern Observatory  under  ESO  programme  0103. D-0772 (A). We thank the anonymous referee whose kind comments helped improve the present paper. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2018. 1. 00659. L. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www.
D. G. was funded by the project grant " The Origin and Fate of Dust in Our Universe " from  Knut and Alice Wallenberg foundation. IEM  has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  863412 ). JMCP was supported by the  UK's STFC  (grant number  ST/T000287/1 ). T. J. M. gratefully acknowledges the receipt of a  Leverhulme Emeritus Fellowship. This work has made use of the SPHERE Data Centre, jointly operated by  OSUG/IPAG (Grenoble), PYTHEAS/LAM/CESAM (Marseille) ,  OCA/Lagrange (Nice) ,  Observatoire de Paris/LESIA (Paris) , and  Observatoire de Lyon. We used the SIMBAD and VIZIER databases at the  CDS, Strasbourg (France ; http://cdsweb. u-strasbg. fr/), and  NASA 's Astrophysics Data System Bibliographic Services.
Databases searched included the CGIAR publication database, Wiley Online Library, JSTOR, Elsevier, Science Direct, Sage Journals, Taylor and Francis Online, ProQuest, Springer Science and Business, ResearchGate, United States Department of Agriculture: National Agricultural Library, Semantic Scholar, and CORE. Snowballing enabled us to gather literature beyond the above-cited venues. We additionally used Google Scholar to locate open access articles. Based on the title and abstract review, articles that did not fall into the specified themes or geographic area of Ethiopia are excluded manually.
Preprocessing text Extraction of information from a large set of documents using text mining requires a set of preprocessing steps. The first operation is the conversion of pdf to text format using the pdf tools R package  (Ooms, 2020). Subsequently, the raw text is converted to smaller text units (a process called tokenization) including character, word, sentences, lines, paragraphs, and n-grams). In this study, we have segmented the raw data into words and n-grams (particularly 2-g) using tidytext  (Silge and Robinson, 2016)  package. N-grams display the words that co-occurred the most (a 2-ngram is, for example, natural + resources). In natural language processing (NLP), words that ensure sentence structure and grammar but contain little information are called stopwords (e. g. , is, of, was, the).
We used the snowball package  (Bouchet-Valat and Bouchet-Valat, 2020)  to remove stopwords, which reduces computational demand. The third and important preprocessing step is stemming, a process of reducing each word to its basic forms. For example, crop, crops, cropping, cropped will all be reduced to crop*  (Bouchet-Valat and Bouchet-Valat, 2020). We used the snowball R package for stemming words.
The idf (t, D)is estimated as: idf (t, D) = log N n t where N is the total number of articles, and n t is the number of articles with the term t in it. We used the R tidytext package to generate the tf-idf statistics for each word and document  (Robinson and Silge, 2020). We explored word and keyword associations through a network analysis, which helps to understand what words or keywords are central to the network and keep it connected.
e. gender blind, gender sensitive or gender transformative). We used multinomial regression, based on glmnet R package, because it has shown better performance in other studies  (Friedman et al. , 2021). The trained model is tested with 25%of the articles. To obtain the best combination of glmnet lesso model parameters, we used 10-fold cross validation for the training dataset. In addition to the overall model performance, we used a confusion matrix between manually-assigned categories by experts and automatically assigned categories by the predictive model to further examine the predictive capacity of the algorithm for the three gender classes.
Topic modelling We conducted topic modelling to examine the types of gender issues and topics raised in relation to land restoration in Ethiopia. To this end, topic modeling  (Nikolenko et al. , 2017)  was done on the aggregate of gender inclusive (gender sensitive and transformative) articles because there were too few articles to analyze each category. Topic modeling is an unsupervised machine learning technique that does not require a predefined list of tags and training data prepared by humans. We used topicmodels R package  (Grün and Hornik, 2011) , which uses Latent Dirichlet allocation (LDA) algorithm  (Blei et al. , 2003)  to map each article into a set of topics and a set of words for a given topic. Topic modeling tools provide a fast and feasible way of extracting latent semantic structure in documents and establishing links between the latent topics, which would otherwise be impossible or time consuming for a large dataset.
Acknowledgements This study was financially supported by the  CGIAR   Research Program on Water Land and Ecosystems ( WLE). We also acknowledge the funding from the  International Development Association (IDA) of the World Bank  to the  Accelerating Impact of CGIAR Climate Research for Africa (AICCRA ) project and the  Bill & Melinda Gates Foundation to CGIAR Excellence in agronomy (EiA) initiative. The authors acknowledge  Olga Spellman  ( Alliance of Bioversity International and CIAT Science Writing Service ) for copy editing and technical inputs into this manuscript.
Genetic Variant Analysis A sample of peripheral blood was taken with a BD Microtainer ® (Franklin Lakes, NJ, USA) blood collection system. DNA was isolated from leukocytes using the Promega Wizard ® Genomic DNA Purification Kit (Madison, WI, USA). The DNA samples were quantified with a Thermo Scientific NanoDrop™ 2000 instrument (Waltham, MA, USA) and aliquoted and stored at -20 • C until later use. The high-resolution melting (HRM) method was used to detect genetic variants of the TFAP2B and EPAS1 genes. The HRM primers were designed using the Primer Select program (DNASTAR lasergene, Maddison, WI, USA), which also ensures that the primer sequences do not form secondary structures during PCR that could increase the complexity of melting profile interpretation. The primer specificity was tested using the Primer Blast platform (NCBI)  [24].
Melting curve analysis was performed with Bio-Rad Precision Melt Analysis Software v. 1. 3. (Hercules, CA, USA). All samples with melting curves different from the average underwent capillary sequencing to determine the alteration in the sequence that yielded the difference in the melting curve (see Supplementary Materials). Moreover, three random samples with an average melting curve were capillary sequenced to ensure that the observed curve corresponded to the consensus sequence obtained from the RefSeq database (NCBI)  [25].
DNA Sequencing After purification with Thermo Fisher Scientific PCR ExoSAP-IT TM (Waltham, MA, USA), HRM products were sequenced using Applied Biosystems Big Dye Terminator v1. 1 and v3. 1 kits (Applied Biosystems, Foster City, CA, USA) and an ABI PRISM 3130 DNA Analyzer (Applied Biosystems, Foster City, CA, USA). The obtained sequences were analyzed with BioEdit v. 7. 2 software (Ibis Biosciences, Carlsbad, CA, USA) and the NCBI Nucleotide Blast platform (Blastn)  [26]. Nucleotide sequences were translated using the translate tool in the ExPASy Bioinformatics Resource Portal  [27]. Whenever a sequence variant was found, the sample was sequenced again from the opposite direction to confirm the nucleotide change.
Bioinformatic Analysis All variants found were searched in the NCBI databases dbSNP  [28]  and ClinVar  [29]  to gather information from previous reports. They were also analyzed with Mutalyzer v. 3. software  [30]  to predict whether they could produce a change in the protein sequence. The variants that were predicted to alter the protein sequence were analyzed with the PolyPhen v2. 0. 23 bioinformatic tool  [31] , which can classify those changes as probably damaging, possibly damaging, or benign. Predictions are based on multiple alignments of proteins that are closely related in function and amino acid sequence to the tested protein. This tool also considers all available data in the protein databases UniProt [32] and RCSB  [33]  PDB, such as tridimensional structures and specific protein domain information.
Statistical Analysis Allele and genotype frequencies of the studied polymorphisms were obtained via direct counting. The Hardy-Weinberg equilibrium (HWE) was calculated using the χ 2 test. The significance of the difference between groups was determined using Mantel-Haenzel chi-square analysis. All calculations were performed using SPSS version 18. 0 (SPSS, Chicago, IL, USA). Means + SDs and the frequencies of baseline characteristics were calculated. Student's t-test was performed to compare differences between continuous variables, and the categorical data were analyzed using χ 2 and Fisher exact tests. Logistic regression analysis was used to test for the association of polymorphisms with clinical variables under dominant, recessive, codominant, and additive inheritance models in the independent analysis.