dataesr/scientific-paragraphs-categorization huggingface dataset:
In summary, we conclude that currently there is no single best, consensus method but several diverse, valid practices. Looking at the results from a corresponding ensemble can provide a robust mean as well as a realistic estimate of related uncertainties. Yet the present experiment was limited to a few sites with validation data. A larger sample of benchmark datasets from airborne or high-resolution spaceborne sensors is needed for a comprehensive evaluation of different workflows and processing steps, e. g. for different co-registration approaches  (Li et al. , 2022; Pfeifer et al. , 2014). A comparison of results between ASTER and airborne lidar and high-resolution satellites on about 500 glaciers  (Hugonnet et al. , 2021)  indicated that a large amount of the differences to the reference data are random and, hence, might cancel out for estimates at larger scales.
Looking in more detail at the different workflows, we found that DEM production, selection, and co-registration have the biggest impact on results. Compared to these three basic processing components, the other steps generally have a smaller impact but can become important for individual cases and depending on the use of optical or radar data. Overall, we could not find if any single workflow performs best, as our experiment was limited to a small number of validation sites, but we identified several good practices on the impact of processing steps. Assessments of glacier elevation changes ideally have full control over the entire processing workflow and come with a validation using high-quality DEMs from airborne or higher-resolution satellite data. Here we see a great potential for a coordinated community effort to compile a benchmark dataset of validation DEMs for selected glaciers around the world.
Future inter-comparison experiments could look in more detail at the impact of different implementations of individual processing steps at the glacier scale or extend the scope to other sensors and entire glacier regions, in order to investigate whether the discrepancies found in this inter-comparison experiment for small areas cancel out over larger regions or remain. This could be achieved through community-driven software development that provides peer-reviewed, open-source, and transparent workflows on a public forum, such as GitHub. In addition, we see an urgent need to develop common good practices for uncertainty assessments and the material density assignment that is required to convert elevation change to glacier mass balance. Altogether, these will improve the reliability and credibility of large-scale spaceborne glacier change assessments.
Acknowledgements The present work is a contribution to the working group on  Regional Assessment of Glacier Mass Change (RAGMAC)  of the  International Association of Cryospheric Sciences (IACS)  and to the  Glacier Mass Balance Intercomparison Exercise (GlaMBIE)  of the  European Space Agency (ESA). We thank  T. Barnhart  ( USGS ) and  K. Arie  ( JAXA ) for their feedback on the manuscript. The study was carried out as a community effort with partial support from IACS and from the following projects of participants: L. A. : "Regional  massebalanseestimat av norske breer " (#N80524); E. B. : French Space Agency (CNES); C. F. , C. M. , L. S. : U. S. Geological Survey Ecosystems Climate   R&D Program ,  Benchmark Glacier Project  (USG); R. H. : University of Toulouse; R. H. , M. H. :  Swiss National Science Foundation  (# 184634 ); L.
Data availability The data used in the present study can be accessed from the original data providers: Airborne validation data for Hintereis (2010: Prinz et al. (2023); 2019: RealityMaps (Digital Elevation Model of Hintereisferner based on airborne laser scanning survey of 21 September 2019. RealityMaps, Munich, Germany, 2023)), Aletsch (2011: Ginzler and Hobi (2015); 2017: Swisstopo (2023)), and Vestisen (2008 and 2020: NVE (2023)), SRTM DEM (USGS, 2017), Copernicus DEM (ESA and Airbus, 2022), glacier outlines (RGI Consortium, 2017), and mass balance observations (WGMS, 2021). ASTER L1A data are available from EarthData portal (ASTER L1A Reconstructed Unprocessed Instrument Data, version 003. DOI: 10. 5067/ASTER/AST_L1A. 003, 2023) and the DEMs can be processed from the L1A granules with Ames Stereo Pipelines (DEMs processed from a batch of ASTER L1A images using Ames Stereo Pipelines (ASP), accessed from GitHub.
https://doi. org/10. 5194/egusphere-2023-2309 Preprint. Discussion started: 6 November 2023 c Author(s) 2023. CC BY 4. 0 License. insights. A much larger, worldwide sample of high-quality airborne or spaceborne benchmark datasets would be required for a quantitative evaluation of best practices. Among the various approaches employed by the groups participating in this inter-comparison experiment, Hugonnet et al. (2021; labelled "ETH" in this study) have developed a fully automated workflow for processing and post-processing the entire ASTER archive, and have also published elevation change time series for almost all glaciers globally. The experiment results.
Meteorological data and drip discharge rate monitoring From 2020 to 2022, outside meteorological air temperature and precipitation were collected from a weather station above the Villars Cave. The ETP (potential evapotranspiration) and excess water (also known as effective rainfall) data come from the nearby Meteo-France weather station (Saint-Martin de Freissengeas, 4. 8 km East). The ETP of each grid point is calculated by the Penman-Monteith method. Drip water monitoring sites were set at different levels in the Villars Cave to detect potential differences in the recharge of the karst aquifer (Fig. 1D ;  Genty, 2008; Genty et al. , 2014). Vil-#10A is located at the upper level of the cave, and the drip points Vil-#1B and Vil-#3 (100 m away from Vil-#10A) are about 5 m apart, located on the lower level of the cave (Supplementary Table  1 ; Fig.
1C ). The use of various permanent markers facilitated the precise relocation of the survey lines. Each electrode's position was determined using a Leica® laser theodolite. It is worth noting that from one survey to the next, the previous electrode holes can still be identified. Therefore, the accuracy of these locations is within a margin of <1 cm. In parallel, we also tested a 1. 5 m electrode spacing to obtain a greater investigation depth (106. 5 m long). An inversion resistivity model was calculated using the Res2Dinv® software, applying the L1 norm to the data and a model associated with the refinement of the mesh model  (Xu et al. , 2015; Loke, 2015)  to account for large contrasts in resistivity. We performed seven iterations on each survey data set, after which the absolute error stabilized.
Using the estimated diffusivity, the heat equation was calculated in the rock mass to determine the temperature at each point within the massif over time. This temperature calculation is performed in conjunction with the time series data. Furthermore, a temperature correction was applied to the inversion resistivity data using a ratio model (Eq. (  1 )), which is commonly used to correct resistivity data with the corresponding reference temperature  (Ma et al. , 2011; Xu et al. , 2015 Xu et al. , , 2017;; Supplementary Fig. 3 ): ρ c = ρ i × [ 1 + δ × ( T c -T ref ) ] (1) where ρ c stands for resistivity after temperature correction, ρ i is interpreted as resistivity after inversion, and T c represents temperature during the ERT survey. δ is the temperature slope compensation value, which is set at 0.
By comparing the ERT's seasonal signal changes with rainfall data (input) and drip rate data (output), we estimated the inertia of the reservoir inertia and identified the fast-response zones in clusters 2-4 and the delay times in cluster 1 (1-2 month). We also confirmed that clusters 3 and 2. 1 are pathway of water flux pathways. ERT images were used to determine changes in the reservoirs (number, location, depth, size) and their water saturation, which can be linked to cave hydrological signatures (drip rate changes).
Acknowledgments We thank the Versaveaud family, who are always interested in our research, and their constant help makes the long-term monitoring work in Villars Cave possible. We want to express sincere thanks to  Olivier Mestre  for the help with meteorological data. We thank  Thierry Baritaud  for his help in data collecting and field trips. We thank  Daniel Chailloux ,  Laurent Magne ,  Gilles Souchet  for their help in determining the drip sites. This work was supported by  I2M  programs on decorated caves for geophysical surveys and thermal monitoring. This research was also supported by the  China Scholarship Council (CSC)  to  Jian Zhang  ( 201906990014 ). This long monitoring work (DG) has been funded, since 1996, by several programs from the  CNRS (INSU) , and three laboratories ( GEOPS ,  Orsay ,  Paris-Saclay University ;  Laboratoire des Sciences du Climat et de l'Environnement ,  LSCE ,  CEA/CNRS/UVSQ  and  Environnements et Paléoenvironnements Océaniques et Continentaux , EPOC,  University of Bordeaux, France ).
Its range of applications is then very large from geodynamics, paleomagnetism, mineral and geothermal resources, seismic risk but also paleontological or climate evolution. As indicated in the introduction and schematized on Figure  1 , different methods of argon extraction can be achieved with the different types of laser. This includes first the step-heating degassing of single grains or micro-populations of K-bearing samples by increasing progressively the power of a continuous infrared CO 2 laser beam applied to the grains. An age is calculated at each argon release increment and the whole step data is portrayed in an age spectrum and inverse isochron plot. In some instances, Ca/K and Cl/K vs. age correlation plots can help to better differentiate the various argon reservoirs that can coexist in samples with a complex history of metamorphic-deformation processes and fluid interactions [e.
In particular, we emphasize that log-ratio transformation is critical for the rigorous statistical analysis of geochemical datasets, whether they are obtained by XRF core scanning or more traditional techniques. In addition, we show that accurately interpreting inorganic geochemical data requires a sound understanding of the main components of the sediment (organic matter, biogenic silica, carbonates, lithogenic particles) and mineral assemblages. Finally, we provide a series of examples illustrating the potential and limits of inorganic geochemistry in paleolimnology. Although the examples presented in this paper focus on lake and fjord sediments, the principles presented here also apply to other sedimentary environments.
Introduction The inorganic geochemical composition of lake sediments contains valuable information about the processes occurring in lakes and their watersheds. Variations in the abundance of one or several elements can be used to reconstruct changes in catchment hydrology, lake productivity, pollution, lake level, wind strength, etc. (e. g. ,  Davies et al. , 2015). Inorganic geochemistry is also frequently used to identify event deposits in lake sediments, such as those caused by floods, earthquakes, tsunamis, and volcanic eruptions (e. g. ,  Sabatier et al. , 2022; Wilhelm et al. , 2022). The development and increasing availability of X-ray Fluorescence core scanners (XRF-CS) during the last two decades has resulted in a considerable increase in the use of inorganic geochemical data in paleolimnology.
Researchers are now able to acquire high-resolution multielemental inorganic geochemical data rapidly and non-destructively on split sediment cores with minimal sample preparation. As a result, XRF-CS has become one of the most frequently used paleolimnological techniques of the 21st century and inorganic geochemistry is increasingly part of the routine measurements made before sediment core subsampling. Despite these recent technical developments, more traditional techniques such as Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES), Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Wavelength Dispersive X-ray Fluorescence (WD-XRF) still have their place in paleolimnological research, especially when quantitative results and/or a direct comparison with the composition of sediment sources are needed.
The use of XRF-CS by the paleolimnology community in the last two decades has outpaced the development of data handling and interpretation tools, resulting in frequent data mis-or over-interpretations. Unlike techniques analyzing discrete samples, such as ICP-OES, ICP-MS, and WD-XRF, XRF-CS only measures a subset of major and trace elements, most commonly those with atomic masses ranging from Si to Zr. Analytical uncertainties are particularly high for lighter elements, such as Si, Al, and Mg, although, combined, these elements constitute about 85% of the earth's crust, and by extension of detrital sediments. XRF-CS results are also influenced by variations in the water content and physical properties of the sediment, such as bulk density, and therefore require specific data processing methods.
Accurately interpreting inorganic geochemical data, independent of the technique used to acquire them, also requires a profound knowledge of the mechanisms responsible for elemental changes, which differ between catchments depending on e. g. , bedrock geology, basin geomorphology, soil cover, climate, vegetation. It requires a comprehensive understanding of the associations of elements with one or several components of the sediment, such as lithogenic particles, carbonates, biogenic silica, or organic matter, and with specific minerals. The latter can be aided by multivariate statistical analysis of the geochemical datasets. In other words, interpretations of geochemical data are generally site-specific and not directly transferable from lake to lake. Therefore, they should not be taken as universally applicable.
With this in mind, the goal of this review paper is to provide guidelines to analyze, process, and interpret inorganic geochemical data obtained from lake sediments. Although these guidelines may seem familiar and routine for inorganic geochemists, they are specifically designed for the increasingly large community of paleolimnologists that use inorganic geochemical data but are relatively unfamiliar with the fundamentals of inorganic geochemistry. In this article, we specifically focus on bulk inorganic geochemical measurements, which are relatively easy to acquire, widely used in paleolimnology, and sufficient to answer most research questions. Techniques aimed at the analysis of rare earth elements, isotopes, or specific components isolated from the sediment (e. g. , leaching) are not discussed.
Those designed for the analysis of micro-samples and elemental mapping (e. g. , SEM-EDX, micro-XRF) are also not part of this review. We focus here on major and selected trace elements that are of particular interest in paleolimnology. Although most of the examples presented in this review correspond to lake and fjord sediments, the principles presented here also apply to other sedimentary environments (e. g. , marine sediments, peat). We hope that this article serves as a basis to help the paleolimnological community rigorously process and interpret inorganic geochemical data obtained on lake sediments, independent of their expertise.
BCR has lake sediment CRMs (BCR-280R and 701) but values are only certified for selected trace metals. As a result, river, estuarine, or marine sediments (e. g. , HISS-1, MESS-4, PACS-3, and STSD) are often used as an alternative since they allow covering the entire range of elements of interest. Making geochemical data available to the community through online data repositories is strongly encouraged. The EarthChem Library (https://earthchem. org/ecl/) is specifically designed for geochemical datasets obtained on discrete samples. For data obtained along sediment cores, PANGAEA (https://www. pangaea. de/) and the NOAA World Data Service for Paleoclimatology (https://www. ncei. noaa. gov/product s/paleoclimatology) are particularly well suited. Although best practices for reporting geochemical data are yet to be defined  (Klöcking et al.
, 1998; Croudace et al. , 2006; Tjallingii et al. , 2007; Weltje and Tjallingii, 2008). To circumvent these issues, the XRF-CS community has developed different strategies that are detailed below. The overall goal of these methods is to reduce the influence of physical properties and matrix effects on the XRF-CS measurements, and ultimately extract representative geochemical information from these measurements. Although raw intensities may contain information that is no longer visible after processing, those variations generally reflect changes in lithology (water content, organic matter content, sediment compaction, etc), but not necessarily in inorganic geochemistry (Fig. 3 ). Differences between preand post-processing datasets tend to be high for unconsolidated sediments, particularly in the upper meters of lake sediment cores, which are typically poorly compacted.
Given the rapid evolution of XRF-CS data processing techniques, it is currently recommended to report XRF-CS data in raw format in data repositories such as PANGAEA (http s://www. pangaea. de/) or the NOAA World Data Service for Paleoclimatology (https://www. ncei. noaa. gov/products/paleoclimatology) a) Normalization Normalization of element intensities can be an effective way to eliminate effects arising from changes in physical sediment properties (density, pooling of water, etc. ) or measurement times (live time). It is based on the basic principle that all the elements of a measurement are equally affected by physical properties  (Weltje and Tjallingii, 2008). Normalization of XRF-CS data has been done by dividing element intensities by (a) the intensity of a selected element  (Jansen et al.
Measurements with a large amount of zero values can be left out (outliers), whereas occasional zero values can be replaced by a small value (e. g. the detection limit or half of the minimum non-zero value). Additionally, clr calculations should only include element intensities (i. e. the coherent and incoherent scattering data should not be included), and they can be applied directly to elements measured in different runs (i. e. , with different X-ray source settings, kV and mA). Clr calculations are very straightforward, rapid, and they can be done either manually (see Excel example in appendix A), using software such as CoDaPack (http://compositionaldata. com/), or by means of a couple of lines of code in R, Matlab, or Python. The calculated clr values are dimensionless, can be positive and negative (positive for elements with high intensities and vice versa), and the sum of all clr values of a particular measurement should equal zero, which is a good test to verify the correctness of clr calculations (see checksum in appendix 1).
c) Calibration Quantification of XRF-CS data can be achieved by calibrating element intensities with quantitative geochemical data (i. e. , expressed as concentrations) obtained through traditional discrete sample analysis (e. g. , ICP-OES, WD-XRF or ICP-MS). The advantage of such a calibration is that it minimizes many of the inhomogeneity and instrument-specific differences, allowing for inter-instrument comparisons (e. g. Dunlea et al. , 2020). Additionally, calibrated XRF data can be directly compared to other quantitative geochemical measurements and can be used to calculate mass accumulation rates. Although there are multiple ways to calibrate XRF-CS data  (Gregory et al. , 2019) , calibrating element intensities directly should be avoided. The initial log-ratio calibration model of  Weltje and Tjallingii (2008)  was only able to provide semiquantitative results expressed as a proportion (%) of the sum of the intensities of the measured elements (as in Fig.
3b ). However, the multivariate log-ratio calibration model developed by  Weltje et al. (2015)  provides fully quantitative results, and generally achieves the best results (e. g. ,  Gregory et al. , 2019; Dunlea et al. , 2020). Both the log-ratio and the multivariate log-ratio calibrations can be done using the free software package Xelerate  (Weltje et al. , 2015). In addition to calibrating element intensities, this software also has a function to optimize the amount and location (depth) of calibration samples  (Weltje et al. , 2015).
Interpretation of inorganic geochemical data The large amount of geochemical data produced by analytical instruments, particularly XRF-CS, can be daunting to unfamiliar users. After scanning sediment cores, the first question paleolimnologists generally ask is "which element should I use"? A solution is often found by browsing the literature in order to find an example that is deemed similar, which has been facilitated by the publication of compilations of examples (e. g. ,  Rothwell and Croudace, 2015; Davies et al. , 2015). This approach is however relatively risky as interpretations of downcore changes in one element or elemental ratio are not universally applicable. Rather, we argue that they should be evaluated on a lake-by-lake basis, depending on the environmental context of each lake.
Therefore, we attempt here to guide paleolimnologists through a series of fundamental steps to interpret inorganic geochemical data from lake sediments. As there is no direct answer to the question "which element should I use", this section provides a line of thought to help users pick the most appropriate element(s), depending on the nature of the sediment, and the process(es) of interest. The first step towards a sound interpretation of inorganic geochemical data obtained on lake sediments consists in understanding the association of elements with the main components of the sediment. A rigorous study therefore starts by quantifying (or at least estimating) the amounts of organic matter, biogenic silica, carbonates (and other authigenic minerals, if any) and lithogenic particles of the sediment (Section 3.
An alternative, less time-consuming, approach is to analyze the correlation or co-variability of elements measured on a lake sediment core, using either a correlation matrix or a principal component analysis (PCA; Section 3. 2). This may provide enough information for simple lake systems but may not be sufficient for lakes in which sediment composition is driven by a complex mixture of processes. When the objective is to reconstruct a specific process, it is also advised to compare downcore elemental variations obtained on well-dated short sediment cores with the climate or environmental variable of interest (calibration-in-time approach; e. g. ,  Grosjean et al. , 2009). Although it is tempting to formulate an interpretation based on XRF-CS data only, this frequently leads to misinterpretations, which we hope this section will help avoid.
Exploring compositional data A relatively easy way to explore element associations is through the use of principal component analysis (PCA). PCA is a statistical data reduction technique that groups correlated elements, which can in turn provide an idea of the processes driving their variation. Although similar results can be achieved by using a correlation matrix, a PCA biplot is particularly useful to visually explore element associations (Fig. 5 ). In addition, a PCA allows reducing the dataset to a lower number of important variables. As indicated in Section 2, elemental abundances are generally reported as proportions of the total sample mass (in %, ‰, or ppm). In other words, elemental abundances are compositional in nature and the total of all elements is a constant (e. g. , 1, 100, 10 6 ).