dataesr/scientific-paragraphs-categorization huggingface dataset:
Behavioral observations Trained observers perform daily behavioral observations on all individually-recognized mandrills using either ad libitum data or 5-min focal sampling. During each focal observation, we also record scans on one to three occasions to measure spatial associations between the focal animal and all groupmates that are located less than five meters away (termed "scan"). Since 2012, we obtained 9,305 scans from 48 infants and their 30 mothers during their offspring's first year of life (mean ± SD: 124. 1 ± 178. 3 scans per individual). We restricted our analyses on association rates to infant-infant (169 ± 77 scans per pair), mother-infant (174 ± 80) and mother-mother (174 ± 91) dyads with at least 5 scans (with at least one scan for either partner) and when both individuals were alive and infants aged ≤1 yr.
Matrilineal identity and genetic relatedness Maternity was known for all 80 studied infants thanks to daily monitoring. Matrilineal identity was determined for all of them, including those 48 infants and associated 30 mothers used in the proximity analyses, thanks to detailed data on births and pedigree information available since the beginning of the project  17  and at CIRMF  18. In particular, individuals belong to the same matriline when they share a common mother, maternal grand-mother, great-grand mother and so-on with the exception of those females born at CIRMF that belonged to the same matriline but were released on two different occasions. In these cases, major social disruptions involved changes in female's dominance hierarchy, with females released in 2006 being automatically lower in rank than all other females released in 2002, whatever the initial rank they achieved.
In these cases, females released in 2006 formed a new matriline, independent from their matrilineal origin at CIRMF. Genetic relatedness was determined using pedigree data obtained from paternity analyses performed on most adult mandrills and on a subset of immature individuals (see for details  17  ). We determined paternity for 15 of the 80 portrayed infants. For the proximity analyses, we restricted our data set to those infant dyads (and associated mother-infant and mother-mother dyads) for whom the mothers' pedigree was known to the previous generation (maternal grand-father and grand-mother known) in order to obtain good estimate of mother-mother relatedness. Relatedness across the 285 different pairs of mothers vary from 0. 01 to 0. 66 (mean ± SD: 0. 13 ± 0. 12).
Statistical analyses All data were analyzed using R v. 3. 6. 1. using the package spaMM. We first fitted a General Linear Mixed Model (LMM) to study variation in the averaged facial distance among pairs of 80 infants aged ≤1 yr as a function of the averaged distance in ages across all pairs of pictures collected on each studied pair of infants, their sex (class variable with three modalities: "ff", "fm", "mm"), whether or not they share a common mother (class variable with two modalities: "same mother", "different mother"), and whether or not they either share a common father or were conceived during the tenure of the same alpha male (class variable with two modalities: "same father/alpha", "different father/alpha"). We excluded those infants (and the corresponding pairs) conceived during a month when the alpha position was disputed and unclear, resulting in 2,616 pairs of infants represented by 16,923 pairs of pictures (mean ± SD: 6.
Acknowledgments We are grateful to past and present field assistants of the Mandrillus Project who collect daily behavioral data on the study population, to the  Wildlife Reserves of Singapore  and the  Zoo of Granby  and to the  Primatological Centre at CIRMF (Gabon)  for providing pictures of their mandrills, to the SODEPAL-COMILOG society (ERAMET group) for their long-term logistical support and contribution to the Mandrillus Project. This study was funded by several grants that allowed long-term collection of data:  Deutsche Forschungsgemeinschaft  ( DFG ,  KA 1082-20-1 ) to PMK and  MJEC ,  SEEG Lékédi (INEE-CNRS)  to MJEC, and  Agence Nationale de la Recherche  to MJEC ( ANR.
But since changes at the cliff top may not be representative of changes over the entire cliff face  (Young et al. , 2009) , comparisons between cliff face 3D Point Clouds (PCs) are also used. For this second approach, methods such as terrestrial laser scanning (TLS), airborne laser scanning (ALS), mobile laser scanning, unmanned aerial vehicle photogrammetry (UAVP), or terrestrial photogrammetry (TP) are used to monitor the cliff face  (Young et al. , 2010; James and Robson, 2012; Michoud et al. , 2014; Letortu et al. , 2018). Surveys are taken over time and PCs are compared using tools such as Cloud to Cloud (C2C)  (Girardeau-Montaut et al. , 2005)  in CloudCompare software  (Girardeau-Montaut, 2016). Manual analysis of the differences is then performed to determine areas of erosion on the cliff face and accumulation at the cliff foot.
Erosion rates are calculated using DEM 2. 5D analysis combined with vertical and planimetric detection thresholds, Normalized Difference Vegetation Index (NDVI), machine learning processes and manual quality control. These methods, although relevant, are not applicable on vertical or very steep cliffs. A direct 3D approach allows to study vertical cliffs and to limit the loss of information generated by the data rasterization. Nowadays deep learning is the leading framework in classification and even change identification in remote sensing data. Recently, Siamese KPConv network was proposed to highlight and categorize changes into urban 3D PCs  (de Gélis et al. , 2021). In the following study we also aim to detect and categorize changes but in cliffs. Although the field of study is very different, last decades show that as long as a training step is possible, deep learning methods can adapt to very different cases of study.
, 2014). In Varengeville-sur-Mer, the Petit Ailly cliff face (250-350 m long, 40 m high, slope from 70 • to overhang)  (Letortu et al. , 2018; Jaud et al. , 2019)  (see Figure  1 ) is monitored from 2010 every 4-5 months  (Letortu et al. , 2015 (Letortu et al. , , 2019) )  in the framework of DYNALIT French Observatory Service by terrestrial laser scanning and photogrammetry  (Letortu et al. , 2018)  to improve knowledge on retreat rates and main triggering mechanisms for rock fall activity. This large dataset highlights that rock fall activity is intense over 250-350 m, with a retreat rate of 0. 36 m/y and 4 rock falls over 1 000 m 3  (Letortu et al. , 2019).
Terrestrial Laser Scanning survey The TLS used for this study is a Riegl® VZ-400 emitting a laser pulse in the nearinfrared (1 550 nm), using the time-of-flight of laser pulse to measure the position of a point. Scan data provided by this instrument have a theoretical accuracy of 0. 005 m and a precision of 0. 003 m at a range of 100 m. The Riegl® VZ-400 is equipped with a Nikon D800 camera with a fisheye lens providing photographs that can be used to texturize the 3D point cloud. The georeferencing of the PCs is performed with the Trimble M3 total station to measure several reflective targets used as ground control points (GCPs). The advantage of the use of a total station is that it allows to measure targets closer to the base of the cliff without being affected by mask effects as a Differential Global Positioning System (DGPS) would be.
Indeed, in this case, the C2C distance computed between the two PCs is inconsistent, thus preventing us to evaluate whether it is accumulation, erosion or no change. We recall that the C2C distance has just been used to help the manual annotation task. To annotate the datasets, with respect to the distance separating the two PCs of each pair, a threshold  of 50 cm is used, and refined to 20 cm for some parts. Thereby in this study, we mainly focus on extracting movements greater than 20-50 cm, thus smaller movement like debris falls may be missed.
15 m. As far as other network hyper-parameters are concerned, we use the same configuration as in de Gélis et al. (2021): a Stochastic Gradient Descent (SGD) with momentum to minimize a point-wise negative log-likelihood loss, with a batch size of 10, a momentum of 0. 98 and an initial learning rate of 10 -2. The learning rate is scheduled to decrease exponentially. A probability dropout of 0. 5 in the last classification layers is set. Also, in order to prevent from over-fitting, we set a L2 loss regularization with a factor of 10 -6. As the dataset is largely imbalanced, the loss is weighted according to training set class distribution. In the following experiment, we decided to normalize input spheres along X and Y axis by retrieving the minimum value into the sphere of X and Y axis respectively.
Class representativity The method developed here can be applied on cliffs with various slope degrees with satisfactory results but it should be noted that the results are dependent to the annotation quality. Notice that the same protocol for the cleaning of 3D PCs and annotation should be applied for each pair of PCs. Despite encouraging results obtained by this method, supervised deep learning still requires a dataset both large and representative of each class during the training phase. Thus, the accumulation class that is under-represented might obtain better results if our dataset contained more examples. The same remarks stand for the "no data to compare" class, which is also (as mentioned in Section 4. 2) more subjective. Conversely to urban applications, cliff variability is important (especially in cavity) so information from missing data at the first PC of the pair cannot be interpolated as it is done for hidden building facade in urban environment  (de Gélis et al.
Erosion detection scale The method developed here presents an interest for the longterm monitoring of cliffs since it allows the detection of mass movements of rocks. In order to detect debris falls, more time should be spend to annotate data very accurately and adjustment of the first sub-sampling rate (dl0) is required. This rate should be chosen equal or thinner than debris size, worth noting that the minimum threshold for detecting erosion and accumulation would be the Ground Sampling Distance (GSD) of the sparser PC of the initial dataset. Thus, initial clouds resolution should be homogeneous and adequate regarding to the size of the minimal debris falls to detect. Indeed, without homogeneity between the resolutions of the different datasets, detection artifacts could appear.
and 6 corresponding to each part of the testing dataset. As we can see, the predictions provided by Siamese KPConv prediction are close to the ground truth. Erosion great structures are well recognized, and even smaller parts of erosion seem to be highlighted as it can be seen on the right side of Figure5. Quantitative results are shown in Table4. We report results for the three classes of interest: unchanged, erosion and accumulation. We remind that the class "no data to compare" is not a class of change and is a bit subjective as it depends on annotator confidence in existing surrounding points in an area. In particular, per class IoU indicates that unchanged area and erosion are mainly well classified as already shown on qualitative results. Main differences with the ground truth appear at boundaries of erosion parts and in some more intricate areas like in the top middle left side of the Figure5where erosion, unchanged and "no data to compare" classes are almost mixed up and cliff structure is more complex.
Accumulation class obtains a lower score in comparison to erosion, surely explained by the only few accumulation examples available in the whole dataset. Indeed training set contains only 1. 51 % of points for accumulation whereas the erosion represents 33. 83 % of points. Worth noting that testing set follows the same trend (see Figure4(a), 5(a) and 6(a)). However, and as shown in Figure4, it remains quite well retrieved.
Table2, each class is not equivalently represented in each PCs. As a matter of fact, accumulation class is very rare compared to eroded and even unchanged areas. Thus the division of the dataset is made in such a way that each split is as representative as possible of each class of changes. Thereby, the split of pairs of PCs in each training, validation and testing set is made as indicated in Table3. Accumulation class is less represented in point clouds, thus we divided the 2017-2018 pair into an eastern and a western part at the dry valley of Petit Ailly (see Figure1) to have examples of accumulation in both the training and in the testing set. Split of the dataset into training, validation and testing sets. For each pair of PCs, years relates to the year of acquisition given in Table1. The annotation is always given for the second PC of the pair with regards to the first PC.
ACKNOWLEDGEMENTS This work is part of the  Service National d'Observation DYN-ALIT , via the research infrastructure ILICO. This work was also funded by  ARED/BreTel  and the  ISblue  project, the  Interdisciplinary graduate school for the blue planet  ( ANR-17-EURE-0015 ), and co-funded by a grant from the  French government  under the program " Investissements d'Avenir ". This research was also supported by the  CNES  (EROFALITT and HIRACLES projects). It is based on observations with Pléiades satellites. This work was granted access to the  HPC resources of IDRIS  under the allocation 2021-AD011011754R1 made by  GENCI. I. de Gélis  work is funded by  Magellium  and supported by a  CNES  grant.
All this has a strong influence on the spatial and temporal variability of soil moisture and soil moisture regimes, having wet regimes in the west and north, where the soil is hardly stressed, and semi-arid areas elsewhere, with a wet (energy-limited) and a dry (water-limited) season, with a dry down that might be interrupted by convective events. All this makes the modelling of soil moisture in Iberia a rather challenging task. Daily precipitation, temperature and potential evapotranspiration (PET) were retrieved from the SAFRAN-Spain database  (Quintana-Seguí et al. , 2017). SAFRAN  (Durand et al. , 1993)  is a meteorological reanalysis that produces gridded datasets by combining the outputs of a meteorological model and all available observations using an optimal interpolation algorithm.
It has been implemented over France  (Quintana-Seguí et al. , 2008)  and recently over the Iberian Peninsula  (Quintana-Seguí et al. , 2017)  with a 5 km × 5 km spatial resolution. The SAFRAN dataset used in this study includes not only observations from the Spanish part of the Iberian Peninsula but also ingested data from Portugal. The SURFEX LSM  (Masson et al. , 2013)  has been run using SAFRAN-Spain as the meteorological forcing dataset and on the same grid, as was done in  Quintana-Seguí et al. (2019). SURFEX uses the ECOCLIMAP2  (Faroux et al. , 2013)  physiographic database, and it uses the ISBA (Interaction Sol-Biosphère-Atmosphère) scheme  (Noilhan and Mahfouf, 1996)  for natural surfaces. ISBA has different options; we have used ISBA-DIF, the multi-layer diffusion version  (Boone, 2000; Habets et al.
, 2003). From this simulation, we have extracted the soil moisture of the first 60 cm of the soil by calculating the weighted average of the soil layers that fall within this range. This simulated soil moisture over the Iberian Peninsula is considered herein as the observed reference, in the absence of dense monitoring networks of soil moisture  (Martínez-Fernández et al. , 2016). From the ECO-CLIMAP2 database, elevation and land cover data have also been retrieved and aggregated into the following nine categories: water, bare, ice/snow, urban, forest, grass, dry crops, irrigated crops and wetlands. We also use the European Soil Database (ESDB) produced by the European Soil Data Centre  (Panagos et al. , 2012). The ESDB contains information on soil characteristics, including soil depth and texture for topsoil (0-30 cm) and subsoil (30-70 cm) layers at a grid resolution of 1 km.
86 (median of 0. 89) with RFs and 0. 81 (median of 0. 85) with soil maps. The Nash values in validation (testing set) are low, or even negative, only for mountainous ranges, as expected. Overall, the spatial patterns of the Nash coefficients obtained with RFs or the ESDB are very similar too. There are no significant relationships between model efficiency and the aridity index or the presence of irrigated areas, as identified in the ECOCLIMAP2 land database.
While these pedotransfer functions are process-based predictive functions of certain soil properties, random forests are not based on physical processes and are tailored to provide the best estimates in a statistical sense. Therefore, they provide a valuable alternative in contexts where high-resolution soil maps are not available, since they rely on a set of covariates that can be reliably estimated from global databases, such as satellite or reanalysis products  (Funk et al. , 2015; Hersbach et al. , 2020; Muñoz Sabater, 2020). It should be noted that the results presented herein are highly dependent on the quality of land-surface simulations, in the absence of dense monitoring networks of in situ soil moisture data; thus these results suffer from the same limitations as LSMs, notably, the lack of human process representation in these models (notably irrigation).
However, new remote sensing irrigation estimates are being developed  (Massari et al. , 2021) ; as a consequence, once the RF model is trained, irrigation estimates could be added to the precipitation forcing data in order to include the human impacts on soil moisture estimations. The results show that this ap-proach could allow for cheaply extending the value of highresolution LSM simulations to areas where no LSM is implemented (i. e. northern Africa), as long as the climate conditions belong to the range of values used to train the model, mostly in terms of precipitation and potential evapotranspiration ranges. Thus, the model train over the Iberian Peninsula could be applied to other similar areas such as northern Africa, Italy or Greece. As a perspective, other simulations from countries where high-resolution LSM simulations are available, such as France or the USA, could be added to the database in order to expand the coverage over different physiographic and climate contexts  (Ma et al.
, 2021). Consequently, the benefits of LSM simulations of soil moisture could be expanded to other areas, provided those suitable forcing datasets are available. Furthermore, if public meteorological and hydrological organizations were to create soil moisture observation networks, cleverly designed to cover the most relevant climates of their countries, this approach could be used to train the model using these observations and then regionalize the results to the rest of the territory, thus, converting an in situ observation dataset into a gridded dataset with a much greater spatial coverage.
Acknowledgements. This work is a contribution to the HyMeX programme through the  HUMID  (grant no. CGL2017-85687-R ,  AEI/FEDER ,  UE ) and  ANR HILIAISE  projects. We thank  Jaime Gaona  ( Instituto de Investigación en Agrobiotecnología -CIALE, Universidad de Salamanca, Villamayor, Salamanca, Spain ) for his comments on some aspects of the manuscript and two anonymous reviewers for their suggestions to improve the manuscript. Financial support. This research has been supported by the  Ministerio de Ciencia, Innovación y Universidades  ( HUMID  project; grant no. CGL2017-85687-R ,  AEI/FEDER, UE). Review statement. This paper was edited by  Christian Barthlott  and reviewed by two anonymous referees.
Code availability. Codes (such as MATLAB scripts and functions) are available upon request. Data availability. The SAFRAN dataset is available in the HyMeX database, published under https://doi. org/10. 14768/MISTRALS-HYMEX. 1388 (Quintana Seguí, 2015). The European Soil Database is available at https://esdac. jrc. ec. europa. eu/content/ european-soil-database-v20-vector-and-attribute-data (ESDAC,  2022).
A total of 6021 expressed genes were identified. Among these, 1804 genes were considered as DEGs (p ≤ 0. 05 and |log 2 (fold change)|≥ 1) in the treated (under nicosulfuron) and control (without nicosulfuron) groups, including 1102 up-regulated DEGs (61%) and 702 down-regulated DEGs (39%) (Fig. 3 ). The up-regulated expression genes might be related to the response strategy of nicosulfuron, while down-regulated genes (39%) suggested that nicosulfuron still had toxic effects on strain LAM1902  43, 44. The distribution of the top 30 most expressed genes in both groups is shown in Fig. S4. GO and KEGG enrichment analyses of DEGs. The differentially expressed genes (DEGs) were annotated and classified according to the GO and KEGG databases to determine the metabolic processes of LAM1902 induced in response to nicosulfuron exposure.
The DEGs were distributed as "cell part", "catalytic activity", "cellular process", "metabolic process", and "binding" categories based on the GO database (Fig. S5 ). According to the KEGG database, a total of 757 DEGs were dispatched within 200 pathways, including "environmental information processing", "cellular processes", "genetic information processing", "human diseases", "metabolism", and "organismal systems. " Among these, "metabolism" included the main (57%) metabolic pathways (in KEGG A class), whereas a total of 159 and 89 DEGs were related to the "carbohydrate metabolism" and "xenobiotics biodegradation and metabolism", respectively. According to the KEGG enrichment analyses  45  , DEGs were significantly enriched in "ABC transporters", "sulfur metabolism", and "ribosome" pathways (p ≤ 0.
Metabolic pathway analysis of the response of strain LAM1902 to nicosulfuron stress. Earlier studies have suggested that nicosulfuron degradation could be accomplished using co-supplementation with C-  42  , promoting microorganisms to survive in the nicosulfuron-containing culture/incubation medium. In the present study, the biodegradation of nicosulfuron was carried out in the presence of glucose, and the supplementation of this carbon source was quite effective. Many DEGs in strain LAM1902 were involved in glucose   metabolism (based on KEGG database annotation) and included glycolysis, gluconeogenesis, pentose phosphate pathway, glyoxylate, and dicarboxylate metabolism, tricarboxylic acid (TCA) cycle, and other major metabolism pathways (Table  1 ). These pathways might be important in cellular adaptation to nicosulfuron stress  41.
This is interesting to note that the DEGs related to fatty acid degradation were higher than the fatty acid synthesis under nicosulfuron stress. These results showed that the metabolism of glucose was affected under the exposure of nicosulfuron, which might be related to the adaptation strategy of strain LAM1902 to environmental stress. Furthermore, a prominent finding of the present study was the enhanced expression of a two-component regulation metabolism involving a total of 94 DEGs, based on the KEGG database (Table  1 ). The two-component signal transduction system enables microorganisms to sense, respond, and adapt to the changes in many different environmental conditions, thereby allowing strains to make a corresponding stress response to maintain their health under harsh conditions.
Zhao showed that genes related to "bacterial chemotaxis" were up-regulated under levofloxacin stress using qPCR and found that play a pivotal role in levofloxacin resistance of strain Stenotrophomonas maltophilia  59. Similarly, it is possible that the up-regulated genes of "bacterial chemotaxis" may provide strain LAM1902 with stronger adaptability to the exposure of nicosulfuron stress. Furthermore, 20 up-regulated genes associated with flagella assembly were determined in strain LAM1902, which suggests that this strain can target and degrade nicosulfuron and/or adapt to the contaminated environment. Data revealed several metabolism pathways associated with C-and N-metabolism, glycolysis, TCA cycle, and bacterial chemotaxis were affected by nicosulfuron, which would provide a better understanding of microbial metabolic networks of microorganisms in harsh environments (Fig.