dataesr/scientific-paragraphs-categorization huggingface dataset:
Chemical concentrations, as any other compositional data, are not independent, unconstrained, normally distributed, or symmetrical. A log-ratio transformation should therefore systematically be applied to all compositional data to fulfill the preconditions for statistical analysis. As shown by  Aitchison (1982 Aitchison ( , 1986)) , applying a log-ratio transformation to elemental concentrations largely discards the constant-sum constraints. In other words, calculating log ratios of concentrations opens the dataset and makes concentrations independent from each other, and therefore fully compatible with statistical requirements. Transforming compositional data into centered log-ratio (clr) prior to statistical operation (e. g. PCA, z-score, standard deviation) is crucial to obtain rigorous statistical results.
Therefore, the PCA analysis obtained on raw concentrations (Fig. 6a ) is dominated by closed-sum constraints, i. e. , the variability of the entire dataset is driven by CaO variations. Investigating the element correlations after clr-transformation (Fig. 6b ) reveals more information on the correlation between the elements. For example, it shows that MgO and, to a lesser extent P 2 O 5 , are positively correlated with CaO, which reflects the presence of MgO in carbonates. In addition, all lithogenic elements (SiO 2 , K 2 O, Al 2 O 3 , TiO 2 , Fe 2 O 3 ) show negative PC1 loadings, indicating that they are positively correlated with each other and are probably associated with the detrital sediment fraction. The negative correlation between lithogenic elements and CaO reveals a clear separation of detrital sediments and authigenic.
This difference reflects the higher variance along PC2 after clr transformation (Fig. 6 ). The PC2 scores computed from clrtransformed data show changes that are particularly marked at the base of the core, reflecting variations in sediment grain size  (Arnaud et al. , 2012). These changes would not have been visible without clr transformation (Fig. 8 ). This example therefore highlights the capability of clr transformation to extract additional information from geochemical datasets. The third approach to circumvent the closed-sum effects is to calculate mass accumulations, which requires having access to concentrations and a high-resolution core chronology (see Section 3. 2. 3). The text above was mostly aimed at raising awareness on the influence of data pre-processing on the results of multivariate statistical analysis.
However, centered log ratios become inappropriate for datasets with few (< 5) variables, such as sediment components. In this case, the log ratios of the variable can be calculated using the logarithm of the variable divided by "total -the variable" (e. g. , log (TOC[%]/(100-TOC [%])). Note that it is not necessary to express TOC in organic matter content, bioSi in opal content, and TIC in carbonate content (Section 3. 1) in this case, as this does not affect the log ratios. This approach is illustrated below using ICP-OES data obtained on fjord sediment core PC29A  (Bertrand et al. , 2014). The sediment of core PC29A is dominated by lithogenic particles (83-86 wt%), with significantly lower amounts of biogenic silica (4. 9-6. 2 wt%) and organic carbon (0. 9-1. 5 wt%). Carbonate concentrations are very low (<0.
1 wt %) but significant. As for Figs. 6  and 7 , PCAs were constructed on the Fig. 8. Log (CaO/K 2 O) of a sediment core from Lake Le Bourget compared with principal component 1 and 2 scores computed with raw and clr-transformed data (data from  Arnaud et al. , 2012). This figure illustrates that plotting PC1 scores downcore provides similar information as plotting the log-ratio between two elements with opposite PC1 loadings (CaO and K 2 O; see Fig. 6 ). In addition, it illustrates the difference between PC scores based on raw and clr-transformed data, highlighting the ability of the clr-transformation to extract more information from geochemical datasets (see PC2 scores). raw and clr-transformed geochemical data (here the 10 major elements + Zr, Ba and Sr). The four components of the sediment (Fig.
9c, d ) confirms that Si is mostly associated with quartz and that plagioclase does not play a significant role in controlling the concentrations of any of the measured elements (Fig. 9d ). Note that the interpretation of geochemical and mineralogical data from PCA biplots should not only be based on the correlations between variables but should also consider mineral chemistry (e. g. , Table  4 ;  Deer et al. , 2013). The statistical analyses mentioned above are relatively simple and can be rapidly achieved with most statistical software. For their ease of use, we recommend XLStat (an Excel add-on), Past, or the R "compositions" package (van den Boogaart and Tolosana-Delgado, 2008). The latter also includes clr-transformation of the geochemical data, which can otherwise be done manually (see Section 2.
2. 2) or using CoDaPack. The main difference between these is that only a few of them have the option to plot supplementary variables, for which XLStat is particularly handy. Note that other variables such as grain-size mean or mode (expressed in phi), grain size fractions, magnetic parameters, etc. can also be projected as supplementary variables in PCA biplots. Here too, the compositional data (e. g. , grain-size classes) should be clrtransformed before analysis. For non-compositional data (e. g. , grainsize mean, magnetic parameters), it is important to ensure that the software used for PCA analysis normalizes the data (e. g. , Z-score) before analysis since these supplementary variables are expressed on a different scale.
Aluminum is commonly used as conservative element when using ICP-OES and WD-XRF datasets, but since it is a light element that cannot be measured with high precision by XRF-CS (see 2. 2), it is generally replaced by Rb when using XRF-CS data. An alternative method that is sometimes used in the literature is dividing an element enriched in the coarse fraction of the sediment (e. g. , Zr) by an element mostly represented in the fine fraction (e. g. , K). These ratios can be particularly effective at detecting event deposits (e. g. ,  Wilhelm et al. , 2012a Wilhelm et al. , , 2013)). Indiscriminately applying any of these elements or elemental ratios as grain-size proxies is, however, particularly risky since the association of elements with specific grain-size fractions of the sediment highly depends on mineralogy, and therefore on the geology of the lake watershed.
Zr, for example, is particularly sensitive to grain size in the coarse silt fraction but totally insensitive to particle size in fine silts and clays (Fig. 11 ). In sands, it actually decreases with grain size (Fig. 11 ). In other words, no element is linearly related to grain size from clay to sand. The only possible exception is Si, but the presence of biogenic silica in the sediments of most lakes precludes using Si as a grain-size proxy. To circumvent this issue, several lithogenic elements need to be used together. Such a technique was proposed by  Liu et al. (2019) , who developed a simple grain-size prediction model based on partial least square regression. The method only requires a small set (~10) of calibration samples, and it can be applied to geochemical datasets generated with traditional techniques (ICP-OES, WD-XRF) and core scanners (see example in Section 4.
, 2020)  or SIFT  (Pulley and Collins, 2018). Since the concentration of the elements used to reconstruct provenance is also affected by grain size (see Section 3. 3. 1), some models include particle size corrections  (Collins et al. , 1998). An example of source discrimination for provenance analysis is presented in Fig. 12. In this example, 105 bedrock samples and 5 soil samples from a ~ 50,000 km 2 area in Chilean Patagonia were used to evaluate the possibility to discriminate between three different bedrock types and volcanic soils, based on major elements only (Al, Ca, Fe, K, Mg, Mn, Na, P, Si and Ti;  Liu et al. , 2020). After application of the Kruskal-Wallis test, Al was discarded as it was not able to discriminate between the four sediment sources and therefore added noise to the dataset.
Theoretically, the best approach to estimate biogenic silica from XRF-CS measurements is to apply the excess silica technique to fully calibrated XRF-CS data (Section 2. 2. 2c). To our knowledge, this approach has yet to be applied to lake sediments. show that transforming the data in clr results in a much better correlation with the quantitative Ca measurements obtained by ED-XRF. The biplots in (c) show that Ca is mostly associated with carbonates in the sediments of Lake Le Bourget. The carbonate content was apparently overestimated by Rock-Eval analysis (i. e. , the 100% CaCO 3 intercept corresponds to ~ 35% Ca instead of the theoretical 40%; see section 3. 1. 3). 1 Log (Ca/(100-Ca)) is preferred over clr for datasets with one overly dominant element (in this case Ca). It also allows comparing datasets with the same transformation on both axes.
21b ). As often in paleolimnology, the first component (PC1; 43. 4% of the total variance) represents the lithogenic material (e. g. , K, Mg, Na, Fe, Al; positive PC1 loadings) versus organic matter (e. g. , P, Br, LOI 550 ; negative PC1 loadings) (Fig. 21b  and c ). Most of the variability in the lake dataset from c. 9000 BP to c. 1100 CE occurs along PC1 (Fig. 21b ). The gradual evolution of the PC1 scores towards negative values during that period indicates an increasingly organic sedimentation, likely due to the development of mature forests and soils that resulted in reduced erosion and transport of mineral matter, in addition to the build up of terrestrial organic matter (cf. Engstrom et al. , 2000). The natural Holocene landscape evolution is interrupted from 1150 CE, as indicated by a small but distinct negative shift along PC2 (35% of the total variance), which reflects the contribution of ore-related metals (Fig.
Mining then declined and ore processing moved away from the lake shores in the 19th century, resulting in a return of the Cu concentrations and PC scores to near-natural levels (Fig. 21a, d ). The multi-element dataset of the Hyttgölen sediment record and its analysis by PCA demonstrate how lake sediment geochemical records can reveal important details about the environmental evolution of a site. Those results complement and, more importantly, extend, what might be known from archaeological and historical contexts. This example also illustrates the need to investigate principal components beyond PC1, as secondary variations in lake sediment geochemistry may hold more information about the processes of interest than PC1 scores. Here, the varied history of ore processing is reflected in the variations in three components (PC2-4), in agreement with the historical record.
Conclusions The use of inorganic geochemical data in paleolimnology has significantly increased in the 21st century, particularly due to the development of XRF core scanners. These instruments allow the rapid acquisition of elemental profiles along sediment cores non-destructively and at high resolution. Although XRF-CS are currently superseding traditional data acquisition techniques such as WD-XRF and ICP-OES, the latter remain fundamental to generate high-quality quantitative data. The high resolution that can be achieved with XRF-CS is particularly attractive to paleolimnologists but the results require more intense data processing, which is not yet routinely done by all laboratories. In particular, we recommend avoiding the use of raw XRF-CS data. Instead, we recommend using elemental log ratios and/or centered log ratios of single elements when working with XRF-CS data, which is a simple and efficient way to significantly reduce most of the limitations that are inherent to XRF-CS analysis.
In parallel to technical developments, the high number of lake sediment records generated over the last decades provided clear evidence that inorganic geochemical proxies are not universally applicable but should be evaluated on a lake-by-lake basis. The best data exploration approach involves a combination of multivariate statistics with quantification of the main components of the sediment (organic matter, biogenic silica, carbonates and lithogenic particles) and/or mineralogical assemblages. After proper processing of the compositional data, this approach allows associating elements with one or several components of the sediment, resulting in a rigorous interpretation of the geochemical results. In lake sediments, inorganic elements are predominantly hosted in the lithogenic fraction. Their abundance is mostly driven by changes in grain size and provenance, which offers opportunities to use major element geochemistry to estimate variations in sediment grain-size and sediment provenance at high resolution.
Although elemental ratios have often been used as grain-size indicators, simple prediction techniques are now able to generate more accurate downcore grain-size profiles from multi-elemental data. In addition, weathering indices tend to be unsuitable for lake sediments due to the overwhelming influence of grain size on major element geochemistry. In the future, we expect that the use of inorganic geochemical data will continue increasing in paleolimnology, particularly to obtain more quantitative reconstructions of the processes occurring in lake watersheds. This will require additional data processing efforts, but it has the potential to increase the use of lake sediments as high-resolution archives of climate and environmental change, including in natural hazard research. 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.
Acknowledgements This review results from enlightening discussions with many paleolimnologists and geochemists over the last decade. Katleen Wils ,  Jana Molenaar ,  Loic Piret , and  Matthias Troch  are thanked for constructive comments on an earlier version of this manuscript. Dawei Liu  is acknowledged for providing the data used to make Figs. 12 and 17. We wish to thank  Gert-Jan Weltje  for convincing us of the need to use logratios for compositional data analysis. SB acknowledges the  French Ministry of Higher Education, Research, and Innovation  and the  Agence Nationale de la Recherche (ANR), France , for financing his research chair at  Paris-Saclay University. The authors are also grateful to the three anonymous reviewers for their constructive suggestions. This article is dedicated to the memory of  Bruno Wilhelm , who tragically passed away during the writing of this manuscript.
The model relies on six combinations of hydrogeomechanical parameters. In this study, we assess the most sensitive parameters to be the transmissivity, the well skin effect, the aquitard to aquifer mechanical parameters ratio (B'Ku'/BKu), as well as aquitard diffusivity and aquitard conductivity to aquifer storativity ratio. We apply our new model to a groundwater monitoring dataset from Cambodia and compare the results with pumping tests undertaken in the same formation. We used the diurnal (O1) and semidiurnal (M2) tides to better constrain the model. Results illustrate significant insight into subsurface properties. For example, we derive relevant information about T, S, D', K', and skin effect, when compared to the pumping test results. Overall, our new model can be used to shed light on previously inexplicable well water level behaviour and can be paired with other investigation methods to enhance understanding of subsurface processes.
Having a larger dataset allows for the quantification of the initial brash ice macroporosity, which is essential for validating brash ice growth models. In addition, the piece size distribution and its relation to macroporosity is important for brash ice model-scale tests, as well as in estimating the ship performance in brash ice  (Matala, 2021; Matala and Suominen, 2022). Considering the limited literature data analyses from full-scale brash ice channels, the current understanding of brash ice formation in ship channels and the parameters that influence the brash ice formation and growth as well as the ship's performance in brash ice channels can be improved. This article takes into consideration the brash ice variable conditions and aims to provide insights into two of its main physical properties including macroporosity and piece size distribution.
Acknowledgement The authors would like to acknowledge the financial support from  TotalEnergies SE, Luleå University of Technology , and the operational support from Luleå port. We thank the tug masters, for creating and maintaining for two consecutive years the brash ice test channels. We also thank the  LTU' s researcher  Jonny Nilimaa  for his contribution to the fieldwork investigation. We would like to thank the anonymous reviewers for their thorough review which helped us to improve this paper.
This vertical resolution is chosen to accurately represent the surface dynamics. The ocean is forced at the surface by hourly atmospheric forcings from the Climate Forecast System Reanalysis using a bulk formulation with relative winds  (Saha et al. , 2010). Tidal forcing is not included. The grid bathymetry is from the global SRTM30plus dataset (J. J. Becker D. T. Sandwell & Weatherall, 2009). The simulation is run for 13 months, from  December 1, 2007 , to December 31, 2008 , with a time step of 90 seconds and produces 3-hourly averaged outputs. The first month is dedicated to the spin-up phase, ensuring that submesoscale structures have time to develop. We therefore analyse the outputs for the year 2008. To discard boundary effects, all the results are computed within a subdomain excluding points within 100 km of the boundaries.
Acknowledgments T. P. received a Ph. D. grant from  École Normale Superieure Paris-Saclay. This work was also supported by the  ISblue  project, an  Interdisciplinary graduate school  for the blue planet ( ANR-17-EURE-0015 ), and co-funded by a grant from the  French government  under the program " Investissements d'Avenir ". J. G. would like to acknowledge support from the  French National Agency for Research (ANR)  through the project  DEEPER  ( ANR-19-CE01-0002-01 ). Simulations were performed using HPC resources from  GENCI -TGCC (Grants  2022-A0090112051 ), and from  HPC  facilities  DATARMOR of  " Pôle de Calcul Intensif pour la Mer " at  Ifremer Brest France. Acknowledgments T. P. received a Ph. D. grant from  École Normale Superieure Paris-Saclay. This work was also supported by the  ISblue  project, an  Interdisciplinary graduate school  for the blue planet ( ANR-17-EURE-0015 ), and co-funded by a grant from the  French government  under the program " Investissements d'Avenir ".
J. G. would like to acknowledge support from the  French National Agency for Research (ANR)  through the project  DEEPER  ( ANR-19-CE01-0002-01 ). Simulations were performed using HPC resources from  GENCI-TGCC  (Grants  2022-A0090112051 ), and from  HPC  facilities  DATARMOR of  " Pôle de Calcul Intensif pour la Mer " at  Ifremer Brest France.
, 2017; Peitzsch et al. , 2021a). Therefore, larger ensembles of individual snow avalanche chronologies remain exceptionally rare in the tree-ring literature and the estimation of avalanche occurrences remains limited to the path scale. Peitzsch et al. (2021a)  conclude that a robust regional snow avalanche chronology should include an increased number of individual paths across a more considerable area, as well as "multiple paths nested in subregions". Favillier et al. (2023)  assessed snow avalanche activity in 11 paths in the Goms valley and compared reconstructed process activity with an event database gathered from regional archives. They conclude that the considerable variation in avalanche activity between different paths within the same valley as well as the weak synchronism in activity in adjacent paths and from different slopes may question the representativity of the paths included in regional chronologies.
Sample analysis All collected samples were air-dried, mounted and prepared following standard dendrochronological procedures described by  Bräker (2002). Tree-rings from disturbed and undisturbed samples were measured using a LINTAB-5 positioning table, connected to a Leica stereomicroscope and TSAP-Win Professional 4. 64 software  (Rinn, 2013). The undisturbed samples were visually and statistically crossdated and a reference chronology was built, expressing the general site characteristic growth pattern. This reference chronology was utilized to crossdate the disturbed samples and distinguish between geomorphic disturbances and other signals, such as growth influenced by climate or insect outbreaks  (Favillier et al. , 2017; Martin and Germain, 2016). The reconstruction of past snow avalanches by means of tree rings requires the identification and precise dating of growth disturbances  ( Šilhán et al.
Nevertheless, until 2018, the monitoring program was active solely in two areas: the Bucegi and Fȃgȃraș Mountains. To supplement the limited archival data and to improve our understanding of snow avalanche dynamics in the Fȃgȃraș Mountains, the present study employs tree-ring based techniques to reconstruct past avalanche occurrences on 17 distinct snow avalanche paths. The snow avalanche chronologies obtained in this study, like in any tree-ring based approach, identify years with significant avalanche activity and not individual events. Consequently, a reconstructed event year indicates the occurrence of at least one major avalanche within a particular snow season. Snow avalanche chronologies were built for each of the 17 investigated paths by applying the GD-I t method (  (Favillier et al.
Acknowledgements The research leading to these results has received funding from the  EEA Norway  Grants  2014-2021 , under project code  RO-NO-2019-0415 /  contract no. 30⁄2020. We would also like to thank  Mihai Lupșan ,  Adrian C. Ardelean ,  Adriana Sȃrȃșan  and  Constantin Brâncuși  for support in the collection of samples.
The absorption spectra and relative retention times were used to determine the peaks using the Agilent OpenLab software. Pigment concentrations were calculated with the corresponding calibration curves of ββ-carotene, canthaxanthin, chlorophyll a, chlorophyll b, chlorophyll c2, diatoxanthin (DT), diadinoxanthin (DD), fucoxanthin and pheophytin a standards. The relative abundance of each pigment (%) was obtained from their concentrations in the samples. The de-epoxidation state (DES) was calculated as DT/(DD+DT)x100.
GC-MS The Agilent data files obtained from GC-MS were converted into mzXML using MSconvert software  (Chambers et al. , 2012)  to be processed on R by the eRah package version 1. 1. 0  (Domingo-Almenara et al. , 2016). The latter realize peak deconvolution (min. peak. width = 2. 8 (1. 5 for MeOH fractions), min. peak. height = 2,500 (1,500 for MeOH fractions), noise. threshold = 500, avoid. processing. mz = c (73,149,207)), peak alignment (min. spectra. cor = 0. 90, max. time. dist = 2 (8 for MeOH fractions), mz. range = 40:600) and missing compounds recovery (recMissComp function, minimum number of samples was set at 3 for CHCl 3 and 5 for MeOH fractions). The compounds dataset obtained was first filtered by a signal/noise ratio of peaks present in blanks superior to 10. Peaks of QC having a coefficient of variation superior to 35% were removed from the data.