dataesr/scientific-paragraphs-categorization huggingface dataset:
(1984)  (Table  S3 ). Each simulation began with a 50year spin-up of the soil moisture using recycled meteorology. This evaluation focused on seasonal and annual timescales of fluxes. We started with daily measurements from the sites, then masked any modeled outputs on days when measurements were not available and calculated monthly means when > 50 % of the data was present. To evaluate the model performance, we used four metrics: normalized absolute error (NAE), variance ratio (VR), correlation coefficient (r), and root-mean-squared error (RMSE). The NAE gives an indication of the average model-data mismatch: NAE = X obs -X mod X obs , (12) where X obs is the observed flux, X mod is the modeled flux, and the overbar denotes an average taken over the entire simulation period. The other metrics were calculated from monthly mean fluxes.
New treatments of soil moisture stress We ran the 10 experiments (Sect. 2. 3, Table  1 ) at a subset of 11 sites that span the categories listed in Sect. 3. 2. This included four sites where soil moisture stress was the main contributor to model biases (soil-moisture-stressed sites: GF-Guy, BE-Vie, DE-Tha, and CA-Oas), sites with a Mediterranean climate (IT-Col, US-Var, US-Ton), and sites with soil moisture stress plus other errors (LBA-K67, LBA-BAN, ZA-Kru, and RU-Che). Because some experiments focused on extending the soils far below the deepest soil moisture measurements available, we were unable to use prescribed data for these experiments. Taylor diagrams for GPP and LE for all sites are shown in Figs. S5  and S7 , respectively, and.
Outlook for modeling soil moisture stress in JULES and other land surface models In this study, we used flux tower observations and detailed site information when possible. Working with site researchers enabled us to narrow down reasons for model biases by prescribing soil moisture and LAI at some sites and to better understand mechanisms of drought responses at others. These are invaluable benefits of working with site-level data. Future studies could benefit from incorporating more sites (the full FLUXNET2015 dataset includes 212 sites), particularly if the focus is reducing biome-scale model biases. There is potential to extract even more information from available datasets to improve the representation of soil moisture-vegetation interactions  (Gentine et al. , 2019). This includes better utilization of satellite data, and one particular opportunity is to consider soil moisture measurements in parallel with those of solar-induced fluorescence, which is used to estimate photosynthesis  (Lee et al.
, 2013). Satellite records have large spatial coverage, and modern machine learning algorithms could be used to characterize Earth observation datasets of drought conditions  (Huntingford et al. , 2019). Such methods could address the difficulty in modeling the high complexity and geographical diversity of plant adaptive responses to soil moisture deficits that exist in nature. Future work should build upon these results to further evaluate JULES response with these parameterizations, focusing on deeper soils and either using a non-zero p 0 (we used 0. 4 in this study) or using the soil matric potential (ψ) rather than volumetric water content for calculating β. We note that such alternative parameterizations are not a replacement for improved representations of the soil-plant hydraulic system that have been developed for many models  (Bonan et al.
g. ,  Keenan et al. , 2010; De Kauwe et al. , 2015). Currently, the land partially offsets anthropogenic CO 2 emissions by photosynthetic drawdown, but this could be reversed if droughts increase in frequency or intensity in the future. Feedbacks from the land surface can amplify and lock-in existing drought conditions  (Morillas et al. , 2017) , and land surface responses to regional drought can affect precipitation and circulation in other regions  (Harper et al. , 2013; Lian et al. , 2020). Improving responses of vegetation to drought in land surface models such as JULES would have far-reaching implications for global climate modeling and are therefore of utmost importance. The GF-Guy site is supported by an Investissement d'Avenir grant from the Agence Nationale de la Recherche (CEBA: ANR-10-LABX-0025; ARBRE: ANR-11-LABX-0002-01).
Acknowledgements. Flux tower measurements used in this study are from  FLUXNET2015  and the  LBA  project. Several site PIs contributed data for the soil moisture and LAI prescribed runs, we gratefully acknowledge their contribution here:  Simone Sabbatini  ( IT-CA1 ),  Sabina Keller  ( CH-Cha ),  Todd Schimelfenig ,  David Scoby , and  Tim Arkebauer  ( US-Ne1 ,  US-Ne2 ,  US-Ne3 ),  Caroline Vincke  ( BE-Vie ),  Chris Gough  ( US-UMB ),  Tanguy Manise  ( BE-Vie ),  Pasi Kolari  ( FI-Hyy ),  Russ Scott  ( US-SRG  and  US-SRM, US-Whs ,  US-Wkg ), and  Alessandro Araujo (LBA). The JULES soil moisture stress group gratefully acknowledges  Colin Prentice  and  Peter Cox  for input along the way. Financial support. LBA data was provided with support from  National Aeronautics and Space Administration (NASA) LBA investigation CD-32 ,  NASA   LBA-DMIP  project (no.
NNX09AL52G ), and the  Gordon and Betty Moore Foundation  "Simulations from the  Interactions between Climate, Forests , and  Land Use in the Amazon Basin: Modeling and Mitigating Large Scale Savannization " project. The authors also acknowledge the following funding sources:  EPSRC Living with Environmental Change Fellowship   EP/N030141/1  ( Anna B. Harper ); the  Met Office Hadley Centre Climate Programme (HCCP)  funded by  BEIS and Defra (Karina E. Williams ,  Debbie Hemming ,  Camilla Mathison );  Natural Environment Research Council's projects: "IMPETUS"  ( NE/L010488/1 ) ( Anne Verhoef ,  Azin Wright ) and " Newton/NERC/FAPESP Nordeste " ( NE/N012488/1 ) ( Rodolfo L. B. Nobrega ,  Anne Verhoef );  Newton Fund  through the  Met Office Climate Science for Service Partnership Brazil (CSSP Brazil)  ( Karina E.
Code availability. Both the model code and the files for running it are available from the Met Office Science Repository Service: https://code. metoffice. gov. uk/ (last access: 13 April 2021). Registration is required and code is freely available subject to completion of a software license. The results presented in this paper were obtained from running JULES branch https://code. metoffice. gov. uk/trac/jules/browser/main/branches/ dev/karinawilliams/r9227_add_gpp_unstressed_diagnostic (last access: 13 April 2021,  Williams, 2020)  which is a branch of JULESv4. 9 with the additional unstressed GPP diagnostic added. The runs were completed with the Rose suite https://code. metoffice. gov. uk/trac/roses-u/browser/a/l/7/5/2/u-al752-jpegpaper (last access: 13 April 2021,  Williams et al. , 2020) , which also includes Python scripts for creating the plots.
The Taylor diagrams (Figs. 7, S5 and S7) were made with Python scripts from Yannick Copin (https://gist. github. com/ycopin/3342888, last access: 13 April 2021,  Copin, 2018). Data availability. The FLUXNET2015 data used to run JULES are available for download from https://fluxnet. org/ data/fluxnet2015-dataset/ (last access: 16 August 2020, Pastorello  et al. , 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-14-3269-2021-supplement.
The use of multibeam echosounder systems (MBES) for detailed seafloor mapping is increasing at a fast pace. Due to their design, enabling continuous high-density measurements and the coregistration of seafloor's depth and reflectivity, MBES has become a fundamental instrument in the advancing field of acoustic seafloor classification (ASC). With these data becoming available, recent seafloor mapping research focuses on the interpretation of the hydroacoustic data and automated predictive modeling of seafloor composition. While a methodological consensus on which seafloor sediment classification algorithm and routine does not exist in the scientific community, it is expected that progress will occur through the refinement of each stage of the ASC pipeline: ranging from the data acquisition to the modeling phase.
This research focuses on the stage of the feature extraction; the stage wherein the spatial variables used for the classification are, in this case, derived from the MBES backscatter data. This contribution explored the sediment classification potential of a textural feature based on the recently introduced Weyl transform of 300 kHz MBES backscatter imagery acquired over a nearshore study site in Belgian Waters. The goodness of the Weyl transform textural feature for seafloor sediment classification was assessed in terms of cluster separation of Folk's sedimentological categories (4-class scheme). Class separation potential was quantified at multiple spatial scales by cluster silhouette coefficients. Weyl features derived from MBES backscatter data were found to exhibit superior thematic class separation compared to other well-established textural features, namely: (1) First-order Statistics, (2) Gray Level Co-occurrence Matrices (GLCM), (3) Wavelet Transform and (4) Local Binary Pattern (LBP).
Finally, by employing a Random Forest (RF) categorical classifier, the value of the proposed textural feature for seafloor sediment mapping was confirmed in terms of global and by-class classification accuracies, highest for models based on the backscatter Weyl features. Further tests on different backscatter datasets and sediment classification schemes are required to further elucidate the use of the Weyl transform of MBES backscatter imagery in the context of seafloor mapping.
g. , hydrographic surveying)  [13]. Seafloor bathymetry (depth) and reflectivity (backscatter strength) are the primary data recorded by a MBES. Bathymetry, measured from echoes flight times and angles, is processed to provide continuous topographic information. Backscatter, retained until recently only as a sonar byproduct, is now recognized as a proxy of the seafloor nature (texture and composition), related to five fundamental physical quantities: (a) the acoustic impedance contrast (or hardness), (b) the seafloor roughness, (c) the water-sediment interface volume heterogeneity, (d) the acoustical frequency and (e) the sound pulse angle of incidence onto the seafloor  [8]. Due to the scattering properties of different seafloor substrates and features, backscatter can help determine bottom type  [14] [15] [16].
This offers the potential to derive several textural and statistical features, for example, derived by geospatial neighborhood analysis based on the pixel values. Indeed, complementing spatial imagery datasets with seafloor samples acquired from precise locations (i. e. , ground-truth data), forms the basis of acoustic seafloor classification (ASC): the discipline that seeks to integrate and characterize the hydroacoustic (geophysical data and explanatory variables) and ground-truth data (response variable) in the interest of geology, sedimentology, and biology, ultimately producing maps providing a detailed understanding of the seafloor environment  [19, 20]. Acoustic seafloor classification has drastically evolved over the past decades, moving from primarily univariate and manual digitisation of acoustic images to multivariate, and automated classification approaches: machine learning supervised predictive modeling algorithms have particularly gained popularity due to their accuracy, repeatability, and ability to handle several features simultaneously  [21] [22] [23] [24] [25] [26] [27] [28].
On this basis, we develop an effective and robust MBES backscatter feature extraction method which, compared to well-established techniques, can significantly enhance thematic cluster separation. We use this approach to characterize the MBES backscatter imagery of a high-frequency survey of a shallow (10 m) coastal area of the Belgian Part of the North Sea (BPNS). We test the performance of the Weyl transform and its category distinguishability potential against four Folk (1954) sedimentological classes. The main contributions of this work are: (1) A novel Weyl transform based approach to MBES backscatter characterization and its comprehensive evaluation; (2) Analyzing the effects of scale on cluster consistency, and the selection of an optimal scale for feature extraction in image-based ASC with different feature extraction methods; (3) Thorough quantitative evaluation of the Weyl features on real MBES data in combination with common classifiers.
Materials and Methods This section presents the acquisition and processing of the geophysical MBES and ground-truth datasets. The textural descriptor based on the Weyl transform and the statistical predictive modeling approaches are detailed. The study is based on MBES data acquired in November 2017 in the nearshore coastal area of the Belgian part of the North Sea (BPNS), off the Oostende Harbour. The location of the surveying area is shown in Figure  1. The survey covers around 11 km 2 and encompasses a designated site for the dumping of dredged material (i. e. , harbour and channel maintenance)  [43].
Ground-Truth Data Acquisition and Processing The ground-truth data were collected by means of cylindrical box-core samples. This type of sampling gear conserves the surficial composition and configuration of the seafloor while conserving the subsurface stratigraphy, providing qualitative geotechnical appreciations of the nature of the sediment in the study area. Using the ternary sediment classification of Folk (1954), samples were labeled as Sand (S), Sandy mud (sM), Muddy sand (mS), and Gravelly sand (gS) as shown in Figure  3. Sediment categories were determined by a combination of particle size analysis (by means of a Malvern Mastersizer 3000) and expert visual observation. Overall, the ground-truth dataset consists of 19 samples and their locations are displayed using colored circular markers in Figure  2.
Seafloor Characterization Based on the Weyl Transform Within the framework of ASC, MBES data are integrated with ground-truth data. As MBES backscatter data relates to seafloor surficial structure and composition, texture is regarded as one of the most efficacious features in image-based sediment mapping research. When dealing with the texture image classification tasks, we are interested in representations that are invariant under a given group of transformations, such as translation, scaling, and rotation. The Weyl transform has recently shown remarkable results in the context of texture classification with standard texture images  [40] , outperforming some common textural descriptors including Histograms of Oriented Gradients (HOG)  [45]  and Local Binary Pattern (LBP)  [46]. HOG and LBP are very popular methods for texture description in computer vision, such as human detection and biomedical recognition problems.
Texture Descriptor MBES backscatter data record quantitatively acoustic intensity scattered to the underwater transducer from the seafloor. Through a series of data processing stages, the backscatter strengths are processed into sedimentologically meaningful relative backscatter level, which subsequently serves as the source data to be mapped into image pixels using an appropriate grid resolution and a reasonable resampling strategy  [9]. We hypothesize that autocorrelation, and especially the dyadic autocorrelation framework from Section 2. 3. 1, can provide good discrimination between greyscale patterns corresponding to different sediment types. Hence, we design here a texture descriptor based on the Weyl transform to represent the local pattern of the backscatter mosaic as concomitant characteristics relating to seafloor physical property.
Cluster Consistency Comparison with Classical Texture Features In an ideal classification problem scenario, an optimal textural descriptor should maximize the interclass variability while minimizing the intraclass variability (the same principle applies to the goal of the classification itself; the textural feature enhances this possibility). To evaluate this process in a simplified scenario, 19 blocks were identified within the backscatter image of the surveyed area, being centered with box-core samples and including a very limited area around their neighborhoods (with an area of 256 m × 256 m). Figure  6  shows four backscatter blocks centered with different sediment types. Based on prior knowledge and data analysis, the block size is determined by manual interpretation. We assumed that each block displays a representative and consistent backscatter image texture.
First-order statistics contained five features derived from greyscale imagery, including maximum, minimum, mean, variance, and mode. Statistical features derived from GLCM included contrast, correlation, entropy, homogeneity, and angular second moment. Wavelet coefficients were extracted by performing a level one decomposition using Daubechies wavelet (db2), containing mean and standard deviation of one approximation coefficient and three detail coefficients. LBP encoded local texture structures as normalized histograms using 1 norm. The Weyl coefficients were calculated using Algorithm 1. In order to quantitatively state the cohesion and separation of the sample clusters in each feature space, the silhouette index (SI) was introduced to access the data consistency within clusters  [47]. The silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation).
The silhouette index is calculated using the mean intracluster distance and the mean nearest-cluster distance (Euclidean distance) for each sample. The silhouette validation technique calculates the silhouette index for each sample, average silhouette index for each cluster and overall average silhouette index for a dataset. After the dimension reduction, a series of x, y, z cartesian plots will aid the visualization of the cluster discrimination potential and separation for those feature extraction methods above at multiple scales. SI computation and all feature extraction methods were implemented using built-in functions in MATLAB R2020b (64-bit) on a computer with an Intel Core TM i7-7700HQ CPU.
Statistical Modeling and Protocol of Error Estimation To validate the performance of the proposed texture descriptor based on the Weyl transform, we employed an Random Forest (RF) as a classifier for the purpose of quantitative comparison  [48]. We implemented the routine in Python 3. 8 (64-bit) using the module of sklearn. ensemble. In the training process of the model, parameters were set as follows: the number of trees in the forest was 100 (more trees did not significantly improve the model performance in the test), the number of features to consider when looking for the best split was the square root of the number of all features, Gini impurity was used to measure the quality of a split, bootstrap samples were used when building trees, and the other parameters were left as default. In general, seafloor data are spatially autocorrelated, which means that those observations close to each other are more similar than distant ones.
To avoid the problem of spatial autocorrelation in the sampling data, we adopted a spatial blocking strategy for the allocation of samples to training set and test set. In each identified block of the whole backscatter image, we split the backscatter data spatially into several subblocks. The checkerboard pattern is adopted for the allocation of subblocks to two different folds. This strategy can avoid adjacent subblocks in a fold, which means that all samples of the test set are excluded from the training set  [49]. For each subblock, we took 8 × 8 patches by overlapping sampling with a sliding step of 4 pixels, with 32,400 samples being available in total both in training set and test set. In the experiments, we randomly took 2000 samples for every class of the sediment from the dataset, including backscatter patches and their corresponding labels.
Similarly, with respect to feature extraction, one possible way forward is that of developing deep feature fusion methods: an approach by which various features are recombined to reach their fullest discrimination potential capabilities  [52] [53] [54]. Recent marine habitat mapping studies by  Janowski et al. (2018)  confirm that for a dataset with an overlapping distribution of backscatter intensities between classes, secondary features derived from MBES data can substantially contribute to classification prediction accuracy  [55]. Therefore, this suggests that rather than relying on an ensemble map, refinements at the ASC pipeline stage of the feature extraction could largely benefit classification performance.
Conclusions In this investigation, the Weyl transform was introduced as a novel texture-based descriptor of MBES backscatter imagery, specifically as an aid to its classification into sediment categories that are at the center of attention of European seafloor sediment mapping harmonisation efforts (i. e. , https://www. emodnet-geology. eu/map-viewer/?p= seabed_substrate [56] (accessed on 10 December 2020). The texture descriptor based on the Weyl coefficients describes effectively the multiscale correlation features resulting from MBES backscatter images. The importance of multiscale assessments to avoid the arbitrary selection of the scale parameter was confirmed. Overall and by-class classification accuracy metrics demonstrated that the Weyl transform can improve model accuracy based on the tested dataset and under the spatial assumptions of neighborhood consistency put forward in the methodology.
Besides the favorable characteristics of the Weyl transform, no single feature could achieve the best predictive classification performance for all the sediment categories. Feature fusion, similar to hybrid mapping methods merging the outputs of disparate classifiers, shall be explored as a further way of improving classification. Further research is needed to elucidate the physical relationship between scale, Weyl transform, and sediment classes, including testing the method on different backscatter and ground-truth datasets and habitat classification schemes.
Acknowledgments: The  Operational Directorate Natural Environments of the Royal Belgian Institute of Natural Sciences  was responsible for hydroacoustic and ground-truth data acquisition and processing ( Vera Van Lancker ,  Giacomo Montereale Gavazzi ,  Lars Kint  and  Nathan Terseleer ) and provided the sampling instrumentation (Boxcore). We are grateful to the  Renard Centre of Marine Geology of the University of Ghent  for the use of the sediment laboratory facilities. We would also like to thank the editor and all anonymous reviewers for their constructive comments which have improved the manuscript.
However, they are not suited to detect fake news from original accounts. To deal with the shortcomings in existing methods, we introduce a source-based method focusing on the news propagators' community, including posters and re-tweeters to detect such contents. Propagators are connected using follower-following relations. A feature set combining the connectivity patterns of news propagators with their profile features is used in a machine learning framework to perform binary classification of tweets. Complex network measures and user profile features are also examined separately. We perform an extensive comparative analysis of the proposed methodology on a real-world COVID-19 dataset, exploiting various machine learning and deep learning models at the community and node levels. Results show that hybrid features perform better than network features and user features alone.