dataesr/scientific-paragraphs-categorization huggingface dataset:
[49]. Then, reads were coassembled using MEGAHIT  [50]  to generate contigs, and reads were mapped onto the contigs using Bowtie 2  [51]  to generate BAM files. Profiles were created for each individual sample and merged using the anvi'o metagenomic workflow  [52]. Samples were binned based on differential coverage and sequence composition. A bin was considered as a MAG when it showed a completion higher than 50% and a redundancy lower than 10%. In order to determine whether the assembled contigs contained ARGs, the merged profile was blasted against the CARD database [53] using Diamond. The obtained results were filtered at a minimum identity of 60% and a minimum length of 33 amino acids, and the best hit was selected. A summary of these assemblies is found in Table  S3. Since the assembly represented a very low proportion of the total of sequenced reads from soil microcosms, information regarding soil resistome composition could be lost using this approach.
Therefore, the resistome screening was carried out on nonassembled reads from bacterial enrichments and soil microcosms in order to evaluate all the sequences. Reads were trimmed using the Fastq Quality Trimmer tool of the FASTX-Toolkit. Nucleotides that did not meet a minimum quality score of Q20 were trimmed from the sequences, and sequences shorter than 100 nucleotides after trimming were removed. Then, reads from R1 and R2 were concatenated and blasted against the CARD database using Diamond. The obtained results were filtered at a minimum identity of 60% and a minimum length of 33 amino acids, and the best hit was chosen. Genes that had less than 10 copies in the ensemble of samples were removed, and the relative abundance of each gene was calculated. The genes that increased their relative abundance over time in nonpolluted samples were removed, i.
The first is to produce empirical susceptibility maps, using factors such as slope, lithology and estimations of ground shaking intensity to predict areas where landslides are likely to have occurred (e. g. Nowicki Jessee et al. , 2018; Robinson et al. , 2017; Tanyas et al. , 2019). The second is to estimate landslide locations based on their signal in satellite synthetic aperture radar (SAR) data, which can be acquired through cloud cover and so is often able to provide more complete spatial coverage than optical satellite imagery in the critical 2-week response window (e. g. Aimaiti et al. , 2019; Burrows et al. , 2019 Burrows et al. , , 2020;; Jung and Yun, 2019; Konishi and Suga, 2019; Mondini et al. , 2019 Mondini et al. , , 2021)). To generate an empirical model of triggered landslides following an earthquake, a training dataset of landslides is analysed alongside maps of "static" factors known to influence landslide likelihood, e.
g. slope and land cover, as well as "dynamic" causative factors, e. g. ground shaking estimates, and a model is produced that predicts landslide likelihood based on these inputs. A range of methods have been used to generate landslide susceptibility models, including fuzzy logic  (Kirschbaum and Stanley, 2018; Kritikos et al. , 2015; Robinson et al. , 2017) , logistic regression  (Cui et al. , 2020; Nowicki Jessee et al. , 2018; Tanyas et al. , 2019)  and random forests  (Catani et al. , 2013; Chen et al. , 2017; Fan et al. , 2020). When generating a susceptibility map for emergency response, the training dataset can be either a collection of landslide inventories triggered by multiple earthquakes worldwide (e. g. Kritikos et al. , 2015; Nowicki Jessee et al. , 2018; Tanyas et al. , 2019)  or a small sample of the affected area mapped immediately following the earthquake (e.
At each stage in this process, we assessed the ability of the model to recreate the landslide areal density (LAD) in the test area of the landslide dataset using receiver operating characteristic (ROC) analysis and by calculation of the coefficient of determination (r 2 ). For the modelling, we used random forests, a machine learning technique that has been demonstrated to perform well in landslide detection  (Chen et al. , 2017; Fan et al. , 2020). Rather than attempting to delineate individual landslides, we chose to model LAD as both empirical models and detection methods based on InSAR coherence perform well at relatively coarse spatial resolutions (within the range 0. 01-1 km 2 ,  Burrows et al. , 2019; Nowicki Jessee et al. , 2018; Robinson et al. , 2017). Similarly, the empirical models of landslide susceptibility released by the USGS following large earthquakes take the form of a predicted LAD, which can be interpreted as the probability for any location within the cell to be affected by a landslide  (Nowicki Jessee et al.
Data and methods Empirical models of landslide hazard (represented here by LAD) adopt a functional form that is driven by a training dataset of mapped landslides rather than by the mechanics of slope stability. Since LAD is a continuous measure, our aim was to carry out a regression between a training dataset of mapped LAD and a selection of input features (e. g. slope, elevation, land cover) that influence landslide likelihood. The resultant function can then be used to predict LAD in areas where there are no mapped landslide data available based on these input features. In this section, we first describe the landslide data used to train and test our models. Second, we describe the different input features which we used in the regression to predict LAD. Third we describe the random forests method and its implementation in this study.
Landslide datasets We used polygon landslide inventories compiled for three large earthquakes that each triggered thousands of landslides: the inventory of  Roback et al. (2017)  of 24 915 landslides triggered by the M w 7. 8 2015 Gorkha, Nepal, earthquake; the inventory of  Zhang et al. (2019)  of 5265 landslides triggered by the M w 6. 6 Hokkaido, Japan, earthquake; and the inventory of  Ferrario (2019)  of 4823 landslides triggered by the M w 6. 8 Lombok, Indonesia, earthquake on 5 August 2018 (see Fig. 1  for the extent of triggered landslides from each of these earthquakes and the spatial and temporal coverage of the SAR data used here). The performance of five SAR-based methods using the same SAR data used here has already been carried out for these three case study earthquakes  (Burrows et al.
Training and test datasets When developing an empirical model, it is necessary to divide the data into two parts: a training dataset, which is used to train the random forest; and a test dataset, which is used to test model performance. Here we used two types of model setup: same-event models trained on a small mapped area of an event to predict the landslide distribution across the rest of the affected area (e. g. Robinson et al. , 2017)  and global models trained on historic landslide inventories to predict a new event (e. g. Kritikos et al. , 2015; Nowicki et al. , 2014; Nowicki Jessee et al. , 2018; Tanyas et al. , 2019). The real-world application of a same-event model is that a small number of landslides can be mapped manually from optical satellite imagery in the days following the earthquake.
Provided these landslides are dispersed across the study area to constitute a representative training dataset, their distribution can then be used to predict the landslide distribution across the whole affected area in much less time than would be required to manually map the whole area  (Robinson et al. , 2017). For our same-event model, we randomly selected 250 landslide (LAD ≥ 0. 01) and 250 non-landslide (LAD < 0. 01) pixels for use as training data. Robinson et al. (2017)  demonstrated that 250 landslide samples were sufficient to train their landslide probability model. Since here we use 200 × 220 m pixels, 250 pixels is equivalent to a mapped area of around 11 km 2. For the second approach, in which an empirical model is trained on a global inventory of past landslides, we trained the random forest on two of our case study events and predicted the third.
Input features A large number of possible input features have been used in previous work on landslide susceptibility mapping, including a wide range of topographic parameters, ground shaking estimates, rainfall data, lithology, land cover, and distance to features such as rivers, roads and faults. Here we limited the model input features to globally available datasets in order to ensure the widest applicability of the results. We targeted our model at earthquake-rather than rainfall-triggered landslides, as these have been more widely used as case studies when developing and testing SAR-based methods of landslide detection (e. g. Aimaiti et al. , 2019; Burrows et al. , 2019; Jung and Yun, 2019; Yun et al. , 2015) , and detailed polygon inventories of thousands of landslides triggered by a single earthquake  (Ferrario, 2019; Roback et al.
The table is divided into conventional predictors of landslide likelihood and those based on InSAR coherence. For the latter, the case where only data from the Sentinel-1 (S-1) satellite were used is noted; otherwise, both Sentinel-1 and ALOS-2 data were used. 200×220 m cell, we calculated the mean 20 m × 22 m cell elevation (used by  Catani et al. , 2013; Nowicki et al. , 2014) , the standard deviation of cell elevations (used by  Catani et al. , 2013) , the maximum slope within an aggregate cell (used by  Nowicki et al. , 2014) , the mean slope within an aggregate cell (used by  Nowicki Jessee et al. , 2018; Robinson et al. , 2017; Kritikos et al. , 2015; Tanyas et al. , 2019) , the standard deviation of pixel slopes within an aggregate cell (used by  Catani et al. , 2013; Tanyas et al. , 2019) , the circular mean of the aspect (used by  Chen et al.
The uncertainty of the ShakeMap product varies depending on the local seismic network used to gather the shaking data on which the shaking estimates are based  (Worden et al. , 2020)  but is not currently taken into account when using these estimates in empirical models  (Nowicki Jessee et al. , 2018). An initial estimate of ground shaking is generally available within hours of an earthquake from the USGS ShakeMap web page and is then refined as more data become available. The difference that these updates to the ground shaking estimates can make to empirical models of landsliding has been explored by  Allstadt et al. (2018)  and  Thompson et al. (2020). Here our aim was to investigate changes to modelled landsliding due to the incorporation of SAR data, and so for simplicity we chose to keep all other input features constant through time.
Lithology Lithology is one factor that determines rock strength, and therefore landslide likelihood  (Nadim et al. , 2006) , and has been used in several empirical landslide susceptibility models  (Chen et al. , 2017; Kirschbaum and Stanley, 2018; Nowicki Jessee et al. , 2018). We used the Global Lithological Map database (GLiM)  (Hartmann and Moosdorf, 2012) , which has 13 basic lithological classes, with additional "water bodies", "ice and glacier" and "no data" classes, which are supplied as polygon data. These data are provided in vector format. We rasterised these data onto the 20 × 22 m grid at which the SAR data were processed. Then for each 200 × 220 m aggregate pixel, we took the dominant basic lithological class (i. e. that with the largest area share), resulting in a categorical input feature.
We found that by replacing the individual input features with the model output of Nowicki Jessee et al. (  2018 ), we improved model performance in Nepal and Lombok and obtained a more consistent result across the three events. The models of Nowicki Jessee et al. (  2018 ) are published within hours following a large earthquake for use in hazard assessment and emergency response coordination and are available from the USGS website. They are generated using logistic regression based on PGV, slope, CTI, lithology and land cover data  (Nowicki Jessee et al. , 2018). As described in Sect. 2. 3. 2, USGS estimates of PGV are updated multiple times after an earthquake, and the ground failure products based on these are also updated. Again, we use final published version of the ground failure product (based on the final PGV estimates) in all our models.
, 2005; Yun et al. , 2015)  are all examples of processes which can alter the scattering properties of the Earth's surface and so lower the coherence of an interferogram. InSAR coherence is a measure of the signal-to-noise ratio of an interferogram estimated on a pixel-by-pixel basis from the similarity in amplitude and phase change of small numbers of pixels  (Just and Bamler, 1994). We used single-polarisation SAR data from two SAR systems in this study: the C-band Sentinel-1 SAR satellite operated by ESA and the Phased Array type L-band SAR-2 (PALSAR-2) sensor on the Advanced Land Observation Satellite 2 (ALOS-2) operated by the Japan Aerospace Exploration Agency (JAXA). For each case study earthquake, we used ascending-and descending-track Sentinel-1 SAR imagery and a single track of ALOS-2 PALSAR-2 imagery (Fig.
1 ). This volume of SAR data is available in the immediate aftermath of the majority of earthquakes  (Burrows et al. , 2020). These two satellite systems acquire SAR data at different wavelengths and so interact slightly differently with the ground surface, with ALOS-2 generally being less noisy in heavily vegetated areas than Sentinel-1  (Zebker and Villasenor, 1992). SAR data are acquired obliquely, which in areas of steep topography can lead to some slopes being poorly imaged by the SAR sensor. Here all data were acquired at an angle of 31. 4-43. 8 • to vertical. Descending-track data were acquired with the satellite moving south and looking west, while ascending-track data were acquired with the satellite moving north and looking east. Therefore, slopes which are poorly imaged in ascending data are likely to be better imaged by the descending track and vice versa.
It is for this reason that we employed two tracks of Sentinel-1 data, but this is currently not often available for L-band SAR. We processed the SAR data using GAMMA software, with Sentinel-1 data processed using the LiCSAR package  (Lazeckỳ et al. , 2020). The data were processed in a range × azimuth coordinate system and then projected into a geographic coordinate system with a spatial resolution of 20 m × 22 m. Further details on the spatial and temporal resolution of these SAR data, on their processing and on parameter choices made in the generation of the CECL, Bx-S, PECI, C_sum and C_max surfaces can be found in  Burrows et al. (2020). We aimed to combine InSAR coherence methods with empirical models. To achieve this, we used multiple InSAR coherence methods as input features. The first coherence method we used was the coherence of the co-event interferogram (formed from two images spanning the earthquake).
The only exception to this is the Bx-S method, which also uses a "sibling" coherence estimate in which an ensemble of pixels is selected from within a wider window for coherence estimation using the RapidSAR algorithm of  Spaans and Hooper (2016)   (Burrows et al. , 2019). This sibling coherence estimation requires additional data (a minimum of six pre-seismic images), and so it was not possible to calculate this using ALOS-2 data for the 2015 Nepal earthquake, as this event occurred very early in the lifetime of the satellite. The Bx-S method with ALOS-2 data was therefore not used in Nepal or in any of the global models we tested.
Random forest theory and implementation Random forests are an extension of the decision tree method, a supervised machine learning technique in which a sequence of questions are applied to a dataset in order to predict some unknown property, for example to predict landslide susceptibility based on features of the landscape (e. g. Chen et al. , 2017). A random forest comprises a large number of decision trees, each seeing different combinations of input data, which then make a combined prediction. This avoids overfitting the training data, a problem when using individual decision trees  (Breiman, 2001). First the training dataset is bootstrapped so that each tree sees only a subset of the original pixels. Each tree carries out a series of "splits", in which the data are divided in two based on the value of an input feature.
(2020)  and  Ohki et al. (2020)  have demonstrated that random forests can yield good results in landslide prediction. To implement the random forests method, we used the Python scikit-learn package  (Pedregosa et al. , 2011). The model is defined by a number of hyperparameters that can have a noticeable effect on the model. The first of these is the criteria on which a split should be assessed. In our models, splits were carried out that minimised the mean absolute error (MAE). This criterion was selected based on  Ziegler and König (2014). The second hyperparameter describes the bootstrapping step. Here the data were bootstrapped so that a number of random samples was taken equal to the number of pixels in the training dataset. Each individual pixel is therefore likely to appear at least once in around two-thirds of the bootstrapped datasets  (Efron and Tibshirani, 1997).
Feature importances Figure  6  shows the relative importance of each input feature in a random forest model when the model is trained on data from Hokkaido (Fig. 6a ), Nepal (Fig. 6b ) and Lombok (Fig. 6c ). For simplicity, the parameters have been grouped by the data required to create them. For example, the importance of "topography" in Fig. 6  was calculated as the sum of the importances of maximum slope, mean slope, standard deviation of slope, elevation, standard deviation of elevation, relief, CTI and aspect. As described in Sect. 2. 4. 1, these measures of importance are limited since our input features are not fully independent, but it is clear that the importance of the ICFs increases through time in each case. For each case study, topography was initially the most important input feature but its importance gradually decreased as more SAR data were added, particularly in Hokkaido, where it ceased to be the most important feature after the ICFs from the first ALOS-2 image were added to the model.
White polygons in the final row show areas not mapped by  Roback et al. (2017)  due to cloud cover in optical imagery. native scenario would be the case where cloud cover prevents manual landslide mapping in some parts of the affected area. Robinson et al. (2017)  noted that this clustering had a detrimental effect on the performance of their same-event model. To explore this, we tested the ability of a same-event model to predict LAD for a selected area using only training data from outside that area (Fig. 7 ). We chose to test this in Nepal, as this event covered the largest area. We began with a train-ing dataset comprising all of the data outside the test area (Fig. 7d ), which contained 5210 cells with LAD > 1 % across an area of 47 km 2 , so that after balancing by undersampling non-landslide cells (see Sect.
balanced training datasets of 3728 (Fig. 7f ), 1466 (Fig. 7g ), 538 (Fig. 7h ) and 180 (Fig. 7i ) cells. Figure  7  clearly shows that models trained on a smaller area have lower values of AUC (Fig. 7a ) and r 2 (Fig. 7b ), which is to be expected. r 2 increased in most cases as ICFs were added, with the exception of model (i), which was trained on only 180 cells. All models improved in terms of both AUC and r 2 when the first ALOS-2 ICFs were added. As more ICFs were added to the model, AUC improved for all models, and the difference in AUC between the best and worst models decreased. An alternative way of viewing this is that the inclusion of ICFs in such models decreases the area required to be mapped before an obscured area can be modelled with the same AUC. For example, the model trained on all the data outside of the test area (10 420 mapped cells) achieved an AUC > 0.
(2017) , in which point-mapped landslides are used as the landslide part of the training dataset. Non-landslide training data points are selected that are far enough from any landslide point to be unlikely to lie within a landslide, while being close enough that they are likely to have been included in the area mapped when the inventory was generated. This technique could be employed for models incorporating ICFs in the case where only point-mapped landslide data are available after an earthquake.
, 2014 ), Nowicki Jessee et al. (2018)  observed a noticeable improvement in performance for the model trained on more events. Random forests are not capable of extrapolation, meaning that if the predicted event has LAD values or input feature values outside the range of those present in the training dataset, these will not be well modelled. A model trained on more events will cover a wider range of input feature and LAD values. Furthermore, although the model of Nowicki Jessee et al. (  2018 ), which we used as an input feature to represent the combined effects of lithology, land cover, slope, CTI and PGV on landslide likelihood, was trained on a global set of landslide inventories, the combined effect of https://doi. org/10. 5194/nhess-21-2993-2021 Nat. Hazards Earth Syst. Sci. , 21, 2993-3014, 2021   2018 ) in all three events, resulting in an improvement in AUC of over 0.
All models using these first ALOS-2 ICFs outperformed the model  of Nowicki Jessee et al. (2018). Therefore, at this stage we would recommend adding the ICFs derived from ALOS-2 data or from a similar L-band SAR satellite to a global model. In all of the cases here, Sentinel-1 data were acquired earlier after the earthquake than ALOS-2 data, resulting in a brief period where only ICFs from Sentinel-1 could be added to the model. During this period, our same-event models outperform our global models in all three cases. The same-event models incorporating Sentinel-1 ICFs also outperform the model of Nowicki Jessee et al. (  2018 ) by 0. 18 in Lombok and 0. 03 in Hokkaido (the two models have the same AUC in Nepal). Therefore, there might be an advantage to applying a same-event model using Sentinel-1 ICFs before the first L-band SAR image is acquired.
However, other studies have found amplitude to be unreliable in landslide detection  (Czuchlewski et al. , 2003; Park and Lee, 2019). SAR amplitude depends on several factors including surface roughness, soil moisture content and slope orientation relative to the satellite sensor. The amplitude of non-landslide pixels is therefore likely to vary significantly across the study area. Landslides can result in both increases and decreases in radar amplitude. Since the random forest technique relies on establishing thresholds on which to divide data, input features based on amplitude methods may not be suitable. A second option would be polarimetric SAR, in which data are recorded by the SAR satellite at two polarisations (dual-pol) or both projected and recorded at two polarisations (quad-pol).
Polarimetric SAR data thus describe the scattering properties of the Earth's surface more completely than the single-polarisation data we have used here. It has been demonstrated that quad-pol data can be used to map landslides  (Czuchlewski et al. , 2003; Ohki et al. , 2020; Park and Lee, 2019). However, few SAR systems acquire quadpol data, and those that do (e. g. ALOS-2) do not acquire them routinely. Therefore, these data are often not available immediately after an earthquake and so are not suitable for use in a global model. The applicability of dual-pol SAR data to landslide-detection is less well explored, but as these data are acquired more commonly and are routinely acquired by Sentinel-1, they are likely to be available following an earthquake and so could be incorporated. When testing a range of polarimetric parameters that could be used in landslide detection,  Park and Lee (2019)  demonstrated that landslides could be identified in surfaces generated from dual-pol data, although using dual-pol methods resulted in more noise than quad-pol.