dataesr/scientific-paragraphs-categorization huggingface dataset:
Landslides are a common type of natural disaster that brings great threats to the human lives and economic development around the world, especially in the Chinese Loess Plateau. Longxian County (Shaanxi Province, China), a landslide-prone area located in the southwest part of the Loess Plateau, was selected as the study area. The main purpose of this paper is to map landslide susceptibility using Alternating decision tree (ADTree) as well as GIS-based new ensemble techniques involving ADTree with bootstrap aggregation (Bagging) and ADTree with adaptive boosting (AdaBoost). Initially, a landslide inventory map was prepared with 171 determined historical landslides events in the study area, 120 landslides (70%) were randomly selected for training dataset and the remaining 51 landslides (30%) were used for validation dataset.
Data used In the present study, mainly five basic datasets were collected, including historical landslide records, a digital elevation model (DEM) in a resolution of 30 m, Landsat 8 Operational Land Imager (OLI) images in a spatial resolution of 30 m, Google Earth satellite images, and a lithology map at a scale of 1: 200,000. The historical landslide records were sourced from the Nuclear Industry Geological Survey in Shaanxi Province. DEM and Landsat 8 LOI images were obtained from Geospatial Data Cloud supported by Chinese Academy of Sciences, and available at http://www. gscloud. cn/. Besides, satellite images were interpreted in Google Earth Pro and geological map provided by China Geological Survey (http://www. cgs. gov. cn/) was used. ESRI ArcGIS 10. 2, and Matlab 2015 associated with Microsoft Excel 2010 were used to conduct data processing, while modeling process was performed in Weka 3.
2 environment. Besides, all factor datasets can be divided into continuous dataset and discrete dataset. Continuous dataset of each factor was reclassified into discrete subclasses with data in specific intervals using manual method (altitude, slope angle, slope aspect, distance to faults, distance to rivers, and distance to roads) or Jenks natural breaks classification method (plan curvature, profile curvature, and NDVI); discrete dataset of the rest factors (lithology, and landuse) was reclassified into specific discrete subclasses using unique values. The detailed information of the classes of each landslide conditioning factor is shown in Table  1. Topographical factors including altitude, slope angle, slope aspect, plan curvature, and profile curvature (Fig. 4  (a-e)) were extracted from DEM.
Training and validating data The 171 landslides were randomly divided into two parts, including 120 landslides (70%) used for training the ADTree model and the two ensemble models and the rest 51 landslides (30%) used for validating the built models. This ratio splitting scheme is selected in accordance to the vast majority of scientists in the LSM community, although recent studies have pointed out some issues that it may distort the global intercept and inflates the variability across replicates  (Lombardo and Mai, 2018; Petschko et al. , 2014). LSM is considered as a binary classification, and construction of the binary classification needs both positive and negative database  (Tsangaratos and Benardos, 2014; Chen et al. , 2017b) , a positive database has been built containing the 171 landslides with a value of "1" assigned to each point, a negative (non-landslide, assigned a value of "0") database which is the same amount as the built positive database (171 landslides) was also proposed.
171 non-landslides were randomly generated in the landslide-free area. This database was also randomly split into training (70%, 120 non-landslides) and validation (30%, 51 non-landslides). Then, the frequency ratio (FR) model was adopted to calculate the attribute values of all the layers of the eleven landslide conditioning factors for all the prepared training and validating points including landslides and non-landslides, then FR values were normalized to new classes as per Table  1. FR model is introduced to illustrate landslide occurrence probability in the given study area based on landslide densities, and it can be obtained through calculating the ratio of percentage of landslides and percentage of domain of the specific attribute of each landslide conditioning factor  (Lee and Pradhan, 2007; Pradhan and Lee, 2010; Shahabi et al.
x defines a set of instances and y means a set of prediction layers. , = , $ % & (' ( )) & (4) Finally, the classification rule is obtained which is expressed as formula (5), which is the sign of the sum of all the base rules in. class(-) = sign 01 " 2 3 (-)4 (5) In the present study, the ADTree model was performed using "expand the heaviest path" as search path on the training and validating dataset, the boosting iterations were tested to find the best amount iteration that could generate the highest accuracy, finally 10 boosting iterations were selected. The natural breaks method was selected to perform the reclassification of landslide susceptibility due its capability of reducing the variance within classes and maximizing the variance between classes  (Jenks, 1967). The final landslide susceptibility map was reclassified into five susceptibility zones: very low, low, moderate, high, and very high.
6a ), however, they are also fundamentally different, AdaBoost does random sampling with replacement over weighted data (Fig. 6b ) while Bagging does not (Fig. 6c ). AdaBoost builds base models sequentially, and it needs error rate of each classifier to be kept below 0. 5, whereas Bagging requires an unstable inducer and builds base models in parallel  (Rokach, 2010). Both AdaBoost and Bagging were applied in the present study to attempt to improve the performance of the ADTree model in landslide susceptibility modeling. Therefore, two ensemble models were constructed, namely, the ADTree with AdaBoost (ADTree-AdaBoost) model and the ADTree with Bagging (ADTree-Bagging) model. In the end, landslide susceptibility maps of the two models were classified into five susceptibility zones using the natural breaks method: very low, low, moderate, high, and very high.
, 2017c). Therefore, the AUC values representing success rates and prediction rates of the ADTree, the ADTree-AdaBoost, and the ADTree-Bagging model were obtained using training data and validating data, respectively. In general, the AUC value ranges from 0. 5 to 1, which can be classified as follows: poor (0. 5 -0. 6), average (0. 6 -0. 7), good (0. 7 -0. 8), very good (0. 8 -0. 9), and excellent (0. 9 -1), and higher AUC value always indicates better model performance  (Tien Bui et al. , 2014 , 2016b). Furthermore, more detailed ROC parameters of the three models were presented including standard error and 95% confidence interval (CI). Therefore, the general performances of the models were compared, and an optimizing model was selected.
Model performance and validation It is highly necessary to do some investigations on the performance of new models on LSM for spatial analysis and prediction of landslides, though there still exist some debates on the accuracy of various modeling methods which have been proposed  (Akgun, 2008; Pham et al. , 2016a). The ADTree model and its two ensemble models, namely, the ADTree-AdaBoost model and the ADTree-Bagging model, were applied in mapping landslide susceptibility in the current study. All the three models were constructed using the binary training data in Weka 3. 8. Then, landslide susceptibility indices (LSI) for the whole study area were calculated to generate the final rasterize landslide susceptibility map in ArcGIS 10. 2 environment. All landslide susceptibility maps were reclassified into five classes (very low, low, moderate, high, and very high) using natural breaks method.
AUC values of the three models using the validating data are shown in Fig. 10b. The ADTree-AdaBoost model has the highest value of AUC which is 0. 787, followed by ADTree-Bagging (0. 752), and ADTree (0. 696). According to Tien  Bui et al. (2014) , the ADTree model and the two ensemble models all have good prediction capability. Distinct differences between ROC curves of the ADTree model and the two ensemble models can be observed in Table  5. It can be found that very significant difference exists between the ADTree model and the ensemble models. Regarding AUC, the ADTree-AdaBoost model boosted the prediction accuracy of the ADTree model by 9. 13%, and the ADTree-Bagging model enhanced the prediction of the ADTree model by 5. 63%. through evaluation parameters obtained from the confusion matrix using validation dataset (   (Brenning, 2005; Youssef et al.
Acknowledgments We are very thankful to  Victor Jetten , editor of the CATENA journal and two anonymous reviewers for their valuable comments and suggestions to improve the quality of our paper. This study was supported by  National Basic Research Program of China  ( 973 Program , No. 2014CB744701 ), the  National Natural Science Foundation of China  (No. 41072213 ). The authors acknowledge the PhD scholarship awarded to  Yutian Ke  (No. 201706180008 ) by the  China Scholarship Council.
The numbers with a yellow circle inside the model column are the ranks of the MPI tasks in its model MPI communicator of the corresponding member. As shown in the data assimilation column, the first three global MPI tasks also form a filter MPI communicator. This filter MPI communicator is used to perform the EnKF analysis. All the MPI tasks with the same rank (number with a yellow circle) in the model MPI communicators form a coupled MPI communicator to exchange data between ensemble members and the EnKF component. Thus, every model member has an identical domain decomposition and so does the DA component. This facilitates the data exchange between ensemble members and the EnKF component. As in Fig. 2 , each member uses its own three MPI tasks to read in the corresponding initial condition and integrate the model to the end of the cycle; then the first MPI task of each member sends its corresponding segment of its states to the corresponding row and column of the ensemble matrix X in the first MPI task of the DA component which, in fact, is the first MPI task of the first member and so do the second and third MPI tasks.
Lustre parallel file system The parallel file system is a crucial component in a current supercomputer. There are several parallel file systems. Lustre parallel file system (http://lustre. org, last access: 18 December 2018) is best known for powering many of the largest HPC clusters worldwide owing to its scalability and performance. The Lustre parallel file system consists of five key components (see Fig. 4 ). The metadata servers (MDSs) make metadata (such as filenames, directories, permission, and file layout) available to Lustre clients. The high performance of the Lustre file system is mainly attributed to its ability to stripe data across multiple OSTs in a round-robin fashion. Figure  5  illustrates how a file is striped across multiple OSTs. A file is divided into multiple segments of the same size (usually the last segment is incomplete).
Experimental environments, designs, and results All the experiments are performed on the research supercomputer Beaufix in Météo-France, which is a Linux cluster built by BULL company. The SLURM system is used for the cluster management and the job scheduling. And this machine is equipped with a highly scalable Lustre file system of 156 OSTs. The parallel IO algorithm developed by ourselves can use both PnetCDF and netCDF with parallel HDF5 as the back end. PnetCDF 1. 10. 0 is adopted for all the experiments in this study. PDAF is an open-source parallel data assimilation framework that provides fully implemented data assimilation algorithms, in particular ensemble-based Kalman filters like LETKF and LESTKF. PDAF is optimized for large-scale applications run on big supercomputers in both research and operational contexts.
Experiments for assessing the proposed parallel IO algorithm The key advantage of the Lustre file system is that it has many parameters that can be tuned by the user to maximize the IO performance according to the characteristics of the files and the configuration of the file system. The most relevant parameters are the stripe size and the stripe count. The Lustre manual provides some guidelines on how to tune these parameters. It is interesting to see how the different combinations of the stripe size and the stripe count affect the performance of the proposed parallel IO algorithm with different numbers of IO tasks. Moreover, it is practical to determine the reasonable combination of these parameters by trial and error. Thus, a simple program using the proposed IO algorithm, which reads 40 files in parallel (each file is about 5 GB in size) into a matrix X as illustrated in Fig.
Acknowledgements. The authors are grateful to the anonymous reviewers and the topical editor  Adrian Sandu  for comments that greatly improved our article. Financial support. The work of  Yongjun Zheng  was supported by  ESA  under the  ESA  framework contract no. 4000125156/18/I-NB of phase 3 of the  ESA Climate Change Initiative Climate Modelling User Group. Review statement. This paper was edited by  Adrian Sandu  and reviewed by  Elias D. Nino-Ruiz  and two anonymous referees. https://doi. org/10. 5194/gmd-13-3607-2020 Geosci. Model Dev. , 13, 3607-3625, 2020.
Genotyping To obtain DNA for genotyping, we germinated field-collected seeds from all new populations in a controlled environment facility at the University of Stirling. We extracted genomic DNA from fresh leaves or flower buds using the DNeasy Plant Kit (Qiagen, Germantown, MD), with samples standardised to 100ng DNA for library preparation. We used genotyping by sequencing (GBS) to generate genome-wide polymorphism data  (Elshire et al. , 2011). For GBS library preparation, we used the same protocol as  Twyford and Friedman (2015) , using the enzyme PstI, and pooling samples in a 95-plex (plus one blank water control) for 100bp single-end sequencing on the Illumina HiSeq 4000 at the University of Oregon. We analysed raw sequence reads using the Tassel5-GBSv2Pipeline  (Glaubitz et al. , 2014) , using the M.
Tree building We sought to resolve evolutionary relationships between populations and species using polymorphism-aware phylogenetic models implemented in IQ-TREE  (Nguyen, Schmidt, von Haeseler, & Minh, 2015). These models use population site frequency data, and therefore account for incomplete lineage sorting  (Schrempf, Minh, De Maio, von Haeseler, & Kosiol, 2016). This phylogeographic approach generates an initial visualisation of population history and broad scale geographic genetic structure from the genome-wide signal, prior to more detailed characterisation with population-level approaches (described below). We analysed two datasets, one for all sampled Mimulus taxa, and one for M. guttatus, with both datasets including M. glabratus as an outgroup. Each analysis used the full GBS sequences with invariant sites, filtered to include 8,798 sites with less than 50% missing data.
We calculated population allele frequencies using the counts file library (cflib) python scripts that accompany  (Schrempf et al. , 2016). Model-fitting was performed with ModelFinder  (Kalyaanamoorthy, Minh, Wong, von Haeseler, & Jermiin, 2017). IQ-TREE analyses subsequently used the best-fitting model (TVM+F+G4) allowing for excess polymorphism (+P) and with five chromosome sets per population (+N5). Tree searches were performed with settings recommended for short sequences, including a small perturbation strength (-pers 0. 2) and large number of stop iterations (-nstop 500). Topological support was assessed using an ultrafast bootstrap approximation approach  (Minh, Nguyen, & von Haeseler, 2013) , with 1000 bootstrap replicates. Trees were visualised with FigTree  (Rambaut, 2014).
Population genetic structure For population genetic analyses in M. guttatus, we filtered the SNP data (44,552 loci from 521 M. guttatus individuals) using VCF Tools and kept only biallelic loci that were genotyped in at least 75% of all individuals, which reduced the number of genotyped SNPs to 1,820 loci. We then removed individuals with less than 50% genotyped loci, reducing the number of individuals from 521 to 474. Finally, we used PLINK to thin the data set to reduce linkage disequilibrium among SNPS using a pairwise correlation coefficient of 0. 5 (--indep-pairwise 50 5 0. 5). The final M. guttatus dataset contained 1,498 SNPs from 474 individuals in 155 populations. To analyse population genetic structure, we conducted a principal component analysis using the glPca function in adegenet  (Jombart & Ahmed, 2011)  in R ver.
Introduction history reconstruction by ABC Our preliminary analyses indicated that introduced M. guttatus had a complex origin with multiple introductions in different non-native regions. In order to gain a more detailed understanding of the demographic history of non-native populations, we focused on the introduction of M. guttatus to the UK, which has been best studied both historically and genetically  (Pantoja et al. , 2017; Puzey & Vallejo-Marin, 2014). Therefore, we implemented an approximate Bayesian computation (ABC) approach to determine the most likely M. guttatus introduction history in the UK. For this analysis, we used the pruned data set consisting of 1,498 SNPs but included only individuals from the native range or the UK (399 individuals). Individuals from the native range were grouped into one of five groups ("genetic group") delimited by the genetic clustering and phylogenetic tree analysis (see Results section): North (NORTH; N=62), South (SOUTH; N=42), Coastal (COAST; N=30), Alaska and British Columbia (AKBC; N=70) or Aleutian (ALE; N=45).
A full version of other assumptions and simulation parameters is given in Supplemental Materials S1. For each demographic model, we simulated 10,000 genetic datasets consisting of 1435 independent SNP genotypes for 798 haploid individuals distributed following the sample size of all six populations in the real dataset using Fastsimcoal2 version 2. 6. 0. 3  (Excoffier, Dupanloup, Huerta-Sanchez, Sousa, & Foll, 2013)  called by ABCtoolbox version 1  (Wegmann, Leuenberger, Neuenschwander, & Excoffier, 2010). We passed a custom bash script to ABCtoolbox to add missing genotypes to the simulated dataset at an identical rate to the observed level in the real data. Then, we used ABCtoolbox to call the arlsumstat program  (Excoffier & Lischer, 2010)  to compute summary statistics from the simulated genotypes.
We compared the most likely single introduction model and the eight two-waves introduction models. We then considered more complex models, comparing nine three-waves introduction models and the most likely single and two-waves introduction models (Table  2 ). We subsequently compared models assuming four-waves and five-waves of introduction while still including more simple models in the comparisons (Table  2 ). Demographic models were compared using a random forest approach implemented in the R package abcrf  (Pudlo et al. , 2016). We built a classification random forest model using 1000 trees and a training dataset consisting of the summary statistics computed for the 10,000 simulated genetic datasets for each model. We estimated the classification error rate for each model using an "out-of-bag" procedure to quantify the power of the genetic data given the models and prior distribution specifications to differentiate the different demographic models.
Then, we used the summary statistics computed based on the observed genotypic data to predict the demographic model that best fit the data using a regression forest with 1000 trees. We report the number of "votes" for each demographic scenario and the approximation of the posterior probability of the most likely model. We used the overall most likely scenario to simulate 100,000 genetic datasets using parameters and prior distributions described above to estimate demographic model parameters. We built a regression random forest model implemented in abcrf based on the summary statistics using 1000 trees. We estimated the posterior median, 0. 05 and 0. 95 quantiles of the model parameters by random forest regression model based on the summary statistics of the observed genotypic composition.
g. , estimated population sizes and introduction times per genetic group; E4 model) are presented in Table  S2. Classification of the datasets simulated under a five-wave introduction scenario showed that 83. 4% of the simulations classified were correctly assigned to a five-wave introduction scenario, and 23. 7% to the correct model (E4) (Table  3 ). Thus, the combination of the type and number of molecular markers and model prior specifications we used here contain enough information to confidently differentiate scenarios with different number of introductions (e. g. , single introduction vs five-wave introductions). Nevertheless, distinguishing the most likely scenario among these complex and sometimes very similar five-wave introduction scenarios proved more difficult (Table  3 , Supporting Material File 1).
In other words, our ability to distinguish the order of introductions of the five genetic groups is more limited. The posterior probability of 55. 1% for the E4 model (Table  2 ), supports a first introduction from ALE followed by additional introductions from the other four other origins (Figure  S3 ). However, most of the posterior distributions of demographic parameters (e. g. , effective population size, number of generations since introduction) for model E4 were nearly identical to the prior distributions (Table  S2 , Supporting Material File 2), indicating limited information content of the genetic dataset to estimate the demographic parameters of this complex introduction history.
WAXS peak fitting was performed in Igor, following the procedure described in a previous work (Amparo  Lopez-Rubio, Flanagan, Gilbert, & Gidley, 2008). The obtained values from the fitting coefficients are those that minimize the value of Chi-squared, which is defined as: 𝜒𝜒 2 = ∑ � 𝑦𝑦-𝑦𝑦 𝑖𝑖 𝜎𝜎 𝑖𝑖 � 2 (2) where y is a fitted value for a given point,   is the measured data value for the point and   is an estimate of the standard deviation for. The curve fitting operation is carried out iteratively and for each iteration, the fitting coefficients are refined to minimize  2. The crystallinity index XC was determined from the obtained fitting results by applying the following equation: 100 (%) × = ∑ Total Crystal C A A X ( 3 ) where ATotal is the sum of the areas under all the diffraction peaks and ΣACrystal is the sum of the areas corresponding to the crystalline peaks.
Image post processing The reconstructed slices contained two well-separated peaks, associated to air and starch/Spirulina. Standard grey-level thresholding was shown to be unable to accurately segment the foams, due to the large variance in grey-scale values between the thinner and thicker cell wall structures, as shown in Figure  S1A. Therefore, an automated (supervised) pixel-level classification method (based on intensity, edge and texture with sigma = 0. 7) implemented in the Ilastik software framework  (Sommer, Straehle, Koethe, & Hamprecht, 2011)  was applied. One slice from each type of sample group was used for combined training, wherein after the automated segmentation method was applied to all respective slices of the given data sets, as shown in Figure  S1B. In extrusion, both the material composition and the utilized processing parameters determine the pore size and cell wall thickness.