dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. All the coders of WRF, LMDZ, and OR-CHIDEE models are acknowledged for their hard work on developing and maintaining the models. Maria Antònia Jiménez  from the  Universitat de les Illes Balears  is acknowledged for her explanations on certain PBL calculations. Daniel Argüeso  from  UIB  and  Victoria Galligani ,  Juan Ruiz , and  Mariano Sebastián  from  CIMA  are also acknowledged for their commentaries. Anna Sörensson  and  Elsa Borrell  are also acknowledged for their assistance. Implementation tests were performed at the  CIMA HPC  resources "hydra" cluster supported by the  High Performance Computing National System (SNCAD)  of  Argentina. Lluís Fita  thanks the  CIMA IT  support staff for their work. Eleni Katragkou  and  Giannis Sofiadis  acknowledge the technical support and provision of resources from the  Scientific Computing Center of AUTH  (https://it.
Code availability. The discussed version of the module (1. 3) in the present article for four different WRF versions is available through the following digital object identifiers (DOIs). -WRF-CORDEX module version 1. 3 for WRFV3. 7. 1: https://doi. org/10. 5281/zenodo. 1469639 (Fita et al. , 2018a)    -WRF-CORDEX module version 1. 3 for WRFV3. 8. 1: https://doi. org/10. 5281/zenodo. 1469645 (Fita et al. , 2018b)    -WRF-CORDEX module version 1. 3 for WRFV3. 9. 1. 1: https://doi. org/10. 5281/zenodo. 1469647 (Fita et al. , 2018c)    -WRF-CORDEX module version 1. 3 for WRFV4. 0: https://doi. org/10. 5281/zenodo. 1469651 (Fita et al. , 2018d).
Silicon (Si) is the second most abundant element of the Earth's crust, and its terrestrial cycle depends on soil, vegetation, and human activities. The spatial extent of terrestrial Si perturbation is poorly documented since maps of Si concentration in soils are rare. In addition, Si content is rarely measured in non-paddy soil databases. Here we demonstrate that pedotransfer functions based on either pedological attributes (particle size fraction, pH, organic carbon, cation exchange capacity, calcium carbonate and parent material) or mid infrared spectra (MIRS) can be used to accurately predict total Si concentration. In this research, we utilised a unique dataset from the French monitoring network of soil quality (RMQS -Réseau de Mesures de la Qualité des Sols) database. Pedotransfer functions were built using a regression tree model on a subset of the data for which total Si concentration was measured.
, 2006; Reimann et al. , 2014). Soil silica content is mostly measured in rice-growing areas  (Minasny et al. , 2016) , but rarely measured in non-paddy soil databases, especially in most of the national soil monitoring networks of Europe  (Arrouays et al. , 2008; Imrie et al. , 2008; Morvan et al. , 2008). The possible reasons are (1) the cost of the measurement and (2) little attention paid to Si in soil and agriculture until now. In the literature, several soil characteristics, generally hydraulic properties or bulk density,  (Baldwin et al. , 2017; Van Looy et al. , 2017; Wösten et al. , 1999) , among others), have been estimated by predictive functions based on pedological characteristics (organic matter, particle size distribution etc. ). This is commonly known as pedotransfer function  (PTFs).
, 2002; Minasny et al. , 2009). These findings suggest that MIRS could be used for prediction of total Si as recently demonstrated by  Mohanty et al. (2016). In this study, we developed and compared pedotransfer functions of total topsoil Si concentrations based on either pedological data (particle size fraction, pH, organic carbon, cation exchange capacity, calcium carbonate and parent material) or MIRS. These data were derived from the French monitoring network of soil quality database, RMQS (Réseau de Mesures de la Qualité des Sols  (Arrouays et al. , 2003; Jolivet et al. , 2006). Pedotransfer functions were built on the RMQS data subset on which total Si concentrations were measured. Subsequently, the PTFs were applied to the whole RMQS dataset to predict topsoil Si concentration for the whole of France.
2. 1. 1-The RMQS database The RMQS monitoring network encompasses 2088 sites sampled following a 16 × 16 km² regular grid across the French metropolitan territory (550 000km²). At each site, land-use, climate, soil type and parent material were described. Twenty five individual cores were sampled, from the topsoil (0-30 cm), using an unaligned sampling design within a 20 × 20 m area  (Jolivet et al. , 2006). Core samples were then bulked. The resulting composite samples were air-dried and sieved to 2 mm before analysis. Particle-size distribution, pH in water, organic carbon (OC), cation exchange capacity (CEC), calcium carbonate (CaCO₃) were analyzed according to international (ISO) or French (NF) standardized methods by the accredited Soil Analysis Laboratory of INRA (Arras, France) (Table  1 ).
2. 1. 2-MIRS data MIRS were also acquired on RMQS samples  (Grinand et al. , 2012). 0. 5-g aliquots of < 0. 2mm ground sample were scanned from 4000 to 400 cm -1 (i. e. , 2500-25,000 nm) at 4 cm -1 resolution using a Nicolet 6700 Diffusive Reflectance Fourier Transform Spectrophotometer (Thermo Fisher Scientific Instruments, Madison, WI, USA). Then, 32 scans per sample were acquired and averaged. Spectra were recorded as absorbance. MIRS were pre-processed, before statistical modelling to reduce baseline variations, enhance spectral features, reduce the particle-size scattering effect, remove linear or curvilinear trends of each spectrum, or remove additive or multiplicative signal effects  (Boysworth and Booksh, 2008). The pre-processing routine consisted of an 11 bands window smoothing Savitzky-Golay filter  (Savitzky and Golay, 1964)  using the sgolayfilt function from the signal R package  (Ligges et al.
2. 1. 3-Selection of samples for total Si analysis To develop the pedotransfer functions, a subset of 673 samples from the 2088 RMQS samples, called hereafter the Si dataset, were analyzed for total Si concentration. The subset sites were selected using the following criteria: (1) one site out of four from the original grid excluding Corsica, and (2) 160 sites randomly selected from the remaining sites (Figure  1 ). This sub-sampling of the grid preserves the systematic grid sampling. This gridsampling method was established by  (Brus and Saby, 2016)  as a flexible design for statistical soil surveys leading to relatively accurate estimates of the statistical distribution of spatial parameters. Total Si concentration was measured on air-dried, less than 2 mm samples by inductively coupled plasma atomic emission spectrometry (ICP-AES) after sodium peroxide fusion of the samples.
2. 2. 1-Pedotransfer functions (PTFs) Pedotransfer functions predicting total Si content was established using two different types of soil variables as inputs: (1) basic pedological attributes including particle size fraction, pH, organic carbon, cation exchange capacity, calcium carbonate and parent material, or  (2)  pre-processed MIRS data, These PTFs are termed as pedological PTF and MIRS PTF hereafter. The variables of the pedological PTF were chosen from the most common soil analytical variables in soil databases. For that reason, parent material was also included, while soil type was not as it had only a limited influence in the model (data not shown).
Calibration and evaluation steps: The modelling approach is summarized in Figure  2. we used a leave p out cross-validation approach combined with a bootstrap step  (James et al. , 2013). The p cross-validation leaves out a p proportion of samples for validation. We used a 75%-25% split for calibration and validation respectively, and this procedure was repeated 10 times. Cross-validation allows a more robust assessment of the quality of the prediction. The subdivision was performed using the conditioned Latin Hypercube Sampling (cLHS) method  (Minasny and McBratney, 2006). This method is a stratified random procedure that provides an efficient way of sampling variables from their multivariate distributions. The bootstrap step involved simulating 100 datasets by random sampling with replacement from 95% of the calibration dataset (formed at the cross-validation step).
Software: Our modelling approach involves a large number of model calibrations and parallel processing was used to handle the computational load. Parallel processing was implemented in R using the packages foreach  (Calaway et al. , 2017b)  and doParrallel  (Calaway et al. , 2017a). All data analyses were performed using the R statistical environment (R core Team, 2017) for descriptive statistics, spectrum pre-processing and model building. We used the Cubist implementation from the Cubist package  (Kuhn et al. , 2016) , the cLHS function implemented in clhs package  (Roudier, 2011).
2. 3. 1-Representativeness assessment Before evaluating the prediction ability of the two PTFs, we first checked that the Si dataset used to develop the PTFs was representative of the whole RMQS dataset. Graphical and numerical comparison of the statistical distribution of the basic pedological data, and the MIR spectra were performed. The Wilcoxon test for the quantitative soil attributes was also performed to compare the two datasets (the Si dataset and the RMQS database). In addition, principal component analyses (PCA) was performed on the pedological attribute as well as the MIRS variables. Using the PCA, samples with or without Si measurements can be readily compared. The PCA allows comparison of the multivariate variables, while the distribution analyses only consider one soil variable at a time.
3. 1-Representativeness of the Si dataset compared to the whole RMQS dataset Measured Si concentrations range from 22. 81 to 455. 8 g kg⁻¹ over the Si dataset with a median equal to 327. 2 g kg -1 (Figure  3 ). Soils with low total Si concentrations (under 124 g kg⁻¹, the statistical threshold for outliers in this dataset) were poorly represented (17 over 674; Figure  3 ). The corresponding samples originated from soils developed in sedimentary parent materials, mostly calcareous with a carbonate concentration of greater or equal to 395 g kg⁻¹. The parent material distribution of both the Si dataset (n=673) and the RMQS set (n= 2088) are very similar (Figure  4 ). For the pedological attributes, the empirical density estimates of soil properties were well represented, both in the Si dataset and the RMQS (Figure  5 ).
The summary statistics of the pedological attributes were reported in Table  2. The empirical density functions for both datasets overlapped. This is supported by the Wilcoxon test which showed no significant difference in the distribution for the considered attributes (p-values recorded in Table  2 ). Considering the whole dataset, the PCAs showed a good overlap between the RMQS sites with and without Si measurement (Figure  6 ). Therefore, we can consider the Si dataset to be representative of the whole RMQS.
80 for the slope. In comparison, the MIRS PTF has an intercept of 22 g kg⁻¹ and slope of 0. 07. The bias maybe due to the low representation in the dataset of samples having Si concentration lower than 12. 5 %, as discussed earlier. The bias can also come from sites which were over predicted where the Si concentrations are between 200 and 300 g kg⁻¹. All in all, the MIRS PTF tends to show an accuracy as good as the chemical analysis when considering both the prediction and analytical uncertainties. However, as predictions were made on the basis of analytical measurements, the prediction uncertainty does not only come from the model accuracy but also from the uncertainty of analytical measurements  (Janik et al. , 1998). In our case, the analytical uncertainty was not taken into account in the prediction uncertainty calculation, as they were not always available.
Despite this, the obtained PTFs show an exceptional accuracy that is rarely obtained in the PTFs literature  (Minasny et al. , 2009; Viscarra Rossel et al. , 2006). Viscarra  Rossel et al. (2006)  reported the accuracy of MIRS PTFs for different soil properties from the literature have R² values ranging from 0. 07 to 0. 98, where one third of the cases an R² value larger or equal to 0. 90 was obtained. Minasny et al. (2009)  built MIRS PTFs for predicting soil properties on three different databases and reported R² values from 0. 0 for total S to 0. 92 for CEC and OC. They concluded that basic soil organic and mineral constituents, as well as properties that are related to the mineral and organic components could be well predicted. This study confirms the hypothesis. PTFs based on pedological properties are generally used to predict hydrological properties that are difficult to measure (e.
3. 4-Domain of potential application of the developed PTFs We compared pedological PTF predictions of total Si concentration for the non-Si analysed RMQS sites to that predicted by the MIRS PTF (Figure  10 ). The relative difference of predictions between the two PTFs is small, 90% of the time, the difference between the 2 PTFS is less than 20%. This result highlights the consistency of the two PTFs and confirms that despite less accurate, the pedological PTF gives a reasonable estimation of the soil Si concentration for the whole dataset. The soil observations of this study came from a systematic probability sampling which leads to good spatial coverage, i. e. the sites are uniformly spread over France. This design proves to be efficient in providing accurate estimates of means over the whole area and can be used to generalize the results for the whole area of France  (Brus and Saby, 2016).
We further investigated if the domain of application of our PTFs at the European scale by comparing the Si concentrations statistical distribution of the Si measurements analytically measured from the RMQS to that of the Geochemical Mapping of Agricultural Soils (GEMAS) dataset  (De Vos et al. , 2006; Reimann et al. , 2014). The GEMAS study provided a few soil Si data over the French territory with one site every 2500 km², i. e. 214 sites for France. Comparing those two distributions for France showed 1) a slight over-estimation of the occurrence of soils with concentrations around 300 g kg⁻¹ ; 2) an under-estimation of soils with concentrations around 400 g kg⁻¹ ; 3) a slight smaller median of the French soil Si concentrations (320. 7 g kg⁻¹). Nevertheless, the Mann and Whitney test shows no significant difference between the two datasets (p-value = 0.
5757). For the European territory, the comparison is shown in Figure  11. The two datasets cover the same range of Si concentration with an over-representation of the soils with Si concentration ranging from 350 to 400 g kg -1 in French compare to other European soils, resulting in contrasted median Si-concentrations of 327. 2 g kg -1 and 313. 9 g kg⁻¹ respectively. When looking at the Si average, the Mann and Whitney test also shows a small significant difference between the two datasets (0. 05 > pvalue = 0. 02273 > 0. 01). This result was expected since France is one of the countries exhibiting the largest soil diversity in the world  (Minasny et al. , 2010). Thus, the established PTFs can be applied at the Europeans scale to predict total soil Si concentrations at a higher spatial density than that provided by the GEMAS study with the exception of some soil types that are not represented in France, such as Chernozems, Kasternozem, Solonetz.
7-List of figures and tables: Figures: -Figure French and European sites in GEMAS  (Reimann et al. , 2014). Vertical lines represent the median values: 327. 2 g kg -¹, 320. 7 g kg -¹ and 313. 9 g kg -¹ respectively. Tables: -Table  1  : Analytical methods employed for the selected soil properties -Table  2 : Summary statistics of pedological attributes for the two datasets: the RMQS and Si dataset. -Table  3 : Summary statistics of performance indicators of the cross-validation for the two PTF models. RMSE, bias and CRPS are in g kg ⁻ ¹. -Table  4 : Important wavelengths in MIRS along with reported peaks and their assignments.
1: Location of the sampling sites of the French monitoring network of soil quality (RMQS). Black triangles represent the sites for which Si measurements were available. Schematic diagram of the modelling procedure. -Figure 3: Distribution of the Si concentrations in the Si dataset: a-histogram; bboxplot. The dashed grey line represents the Si overall mean. -Figure 4: Percentage of samples in different parent material classes for both the French monitoring network of soil quality (RMQS) database and the Si dataset. -Figure 5: Comparison of the empirical density estimates of the pedological properties computed for all the sites (2088) of the French soil monitoring network (RMQS) and those of the Si dataset (673 sample). Principal component analysis of the whole dataset (2088 sampling sites) for two set of variables: Mid-infrared spectra (MIRS) (a and c) and pedological attributes (b and d).
Bivariate plot of the scores of PCs 1 and 2 (a and b). Most correlated MIRS band are plotted in a. Score plot of first two components (c and d) (black points correspond to the extrapolated dataset from the French soil monitoring network (RMQS) and grey points to the Si dataset). -Figure 7: Predicted versus measured Si concentrations (in g kg -1 ) for the first cross validation replication of (a) the mid-infrared spectra (MIRS) and (b) the pedological pedotransfer Functions (PTFs). In black, the one to one line and in red, the fitted regression line. Black vertical error bars represent the prediction's uncertainty and blue horizontal error bars represent the analytical uncertainty. -Figure 8: (a) Mid-infrared reflectance spectra (MIRS) of two sites randomly selected among those with low Si concentrations (in grey) and high Si concentration (in black) respectively; (b) average importance based on cubist models of spectral region for the prediction of total Si concentration.
developed PTFs based on either MIRS or pedological data to estimate the topsoil total Si concentration. Both PTFs provide accurate estimations of the total Si concentration for French soils. These PTFs are underlined by the link between Si and quartz, organic matter, and carbonate contents. The PTF based on MIRS data produces a highly accurate and precise estimates. Since the acquisition of MIRS data allows the estimation of a range of soil properties, such as particle size fraction, major elements or chemical properties that are related to surface solid characteristics like CEC, the use of MIRS PTF represents a powerful tool for populating soil databases. The pedological PTF is less accurate, but still provides a reasonable estimation of the Si concentration for French soils. It is an alternative method when only pedological data are available and an approximate estimation of Si concentrations is sufficient.
-Figure 2 :-Figure 6 : 26 1: Location of the sampling sites of the French monitoring network of soil quality (RMQS). Black triangles represent the sites for which Si measurements were available. Schematic diagram of the modelling procedure. -Figure 3: Distribution of the Si concentrations in the Si dataset: a-histogram; bboxplot. The dashed grey line represents the Si overall mean. -Figure 4: Percentage of samples in different parent material classes for both the French monitoring network of soil quality (RMQS) database and the Si dataset. -Figure 5: Comparison of the empirical density estimates of the pedological properties computed for all the sites (2088) of the French soil monitoring network (RMQS) and those of the Si dataset (673 sample). Principal component analysis of the whole dataset (2088 sampling sites) for two set of variables: Mid-infrared spectra (MIRS) (a and c) and pedological attributes (b and d).
While the Holocene occupation history of penguins on the Danger Islands is currently undescribed, radiocarbon-dated remains from other northern Antarctic Peninsula breeding sites indicates a relatively recent (~600 bp) advent of breeding populations  47. Field survey. Surveys were conducted from the M/V Hans Hansson from December 9-18, 2015. We used a variety of survey methods on each island depending on conditions and time ashore, including one or more of the following methods: (1) manually counting individual nests, (2) counting individual nests in panoramic photos taken from the ground or the vessel, and (3) counting individual penguins from photographs captured by UAV. The combination of these methods allowed for efficient data collection with opportunities for cross-validation of survey methods.
UAV-based survey. UAV surveys were performed with a DJI Phantom 3 quadcopter using its stock 1. 2 Megapixel camera. The UAV was flown either manually or automatically using the mission planning software Map Pilot App to generate image coverage of each island with at least 70% overlap between images. Following the suggestions laid out by ref. 51  , a minimum height above ground of 25 m was set for all flights to avoid disturbance to wildlife, and a maximum height above ground of 45 m was selected to maintain image quality for penguin identification. The geotagged imagery collected with the UAV was post-processed using the commercial photogrammetry software Photoscan (Agisoft LLC, St. Petersburg, Russia) which generated full, georeferenced orthomosaics, a top-down view of the island -in which each pixel corresponds to a fixed physical dimension -of the surveyed islands and their penguin colonies.
Brash, Earle, Beagle, and Heroína Islands were surveyed using composite panoramic images captured by the UAV (example in Fig. 3 ). The timing of our survey was ideal for capturing incubating penguins on the nest and the imagery was, in the overwhelming majority of cases, unambiguous with respect to penguins that were incubating versus walking through the colony or from the ocean. To automatically identify and count the number of occupied nests in the UAV orthomosaics we used a Deep Neural Network (DetectNet) implemented in the open source software NVIDIA DIGITS (NVIDIA Corporation, Santa Clara, CA). DetectNet is based on the GoogLeNet image classification framework  52  and is specifically designed to locate multiple objects of the same type within an image, making it well-suited to the task of detecting penguins in aerial imagery.
2 ). Landsat satellite imagery analysis. Adélie penguin colonies identified in Fig. 2  were retrieved from Landsat imagery based on the algorithm described by refs  31, 57. The retrieval algorithm was originally developed for a single sensor and was modified in this case to operate on cross-calibrated data from Landsat-4 (imagery date 1990 in Fig. 2 ), -7 (2000) and -8 (2015). Cross-calibration among sensors was performed by calculating the mean difference of similar bands from Landsat-4 and-8 imagery compared to Landsat-7, and then adjusting the band values based on the mean differences in each spectral band. The algorithm was then applied to the cross-calibrated imagery to classify Adélie penguin colony areas. High-resolution commercial imagery. Areas of guano staining were manually identified in high resolution satellite imagery (Fig.
Acknowledgements We gratefully acknowledge the financial support of the  Dalio Foundation, Inc. through the  Dalio Explore Fund , which provided all the financing for the Danger Island Expedition. We are grateful to the crew of the yacht M/V  Hans Hansson -Dion Poncet ,  Juliette Hannequinn , Alec and  Gizelle Hazell  for getting us to the Danger Islands and ensuring the safety and well-being of the research team during their work. We would like to thank additional support for analysis from the  National Science Foundation  ( NSF PLR&GSS 1255058 -H. J. L. and P. M. ; NSF PLR 1443585 -M. J. P. ) and the  National Aeronautical and Space Administration  ( NNX14AC32G ; H. J. L. and M. S. ). Geospatial support for the analysis of high resolution satellite imagery provided by the  Polar Geospatial Center  under  NSF PLR awards 1043681 & 1559691.