dataesr/scientific-paragraphs-categorization huggingface dataset:
Additionally, the importance of metals in the ocean, either as micronutrients, proxies of past ocean properties, or contaminants, is being increasingly recognized  (Anderson, 2020) , and understanding their burial rates is key to appraising their biogeochemical cycling. Quantified burials rates of all sedimentary components also help constrain Earth system models  (Bonan & Doney, 2018) , which are essential to projections of future ocean change. In this study, we compile an updated database of global major and trace sediment composition data from more than 12,000 globally distributed marine sediment cores. We then combine this composition information with a recently published database of Th-normalized fluxes for the Holocene (0-10 ka), containing 1,068 flux estimates across the deep ocean (Figure  1a )  (Costa et al.
, 2020). Combining the composition and Th-normalized flux data allows us to produce new atlases of the deep-sea burial of CaCO 3 , biogenic opal, TOC, lithogenic material, and other constituents including iron (Fe), mercury (Hg), and barium (Ba). These three trace elements were respectively chosen as examples of a micronutrient, an anthropogenic contaminant, and an element used as a paleo-proxy. This approach exemplifies how any sedimentary component of interest can be converted from relative concentrations to absolute burial fluxes. Furthermore, while this study focuses on constraining the modern ocean, a similar approach could be taken for the last glacial maximum, whose Th-normalized fluxes were also compiled by  Costa et al. (2020) , or for any other time-slices of interest for which Th data are available.
10. 1029/2020GB006769 data with more recent studies or studies from ocean regions where data gaps existed, including marginal seas such as the Mediterranean, the Gulf of Mexico, the South China Sea, the Arctic Shelf seas, and others. This compilation thus provides a useful global and regional resource. The database references more than 800 publications, dissertations, cruise reports and other data reports and thus reference details for these studies are given only in the reference data archive. In the archived data, each compositional data point has a link to its source manuscript and/or its original online data archive if one exists. The compositional database was examined using a series of quality screenings. In some cases, geographic coordinates indicated that samples were on land or near zero water depth in coastal sites; these data points were removed.
In other cases, reported coordinates were curtailed or rounded to the nearest degree, causing some location uncertainty. In these cases, or where geographic coordinates were given without water depth, the correct position information was located in the Index to Marine and Lacustrine Geological Samples (IMLGS; https://www. ngdc. noaa. gov/geosamples/index. jsp). If the sample did not appear in IMLGS, water depth was estimated using the cited coordinates and ETOPO1 (http://doi. org/10. 7289/V5C8276M) which HAYES ET AL. is a global bathymetry database available at 1 arc-minute resolution. Another common issue in prior data compilations was the same core being reported multiple times with different naming conventions. These were reconciled and core naming conventions used in IMLGS were used whenever possible.
Samples from the Deep Sea Drilling Project, the Ocean Drilling Program and the International Ocean Discovery Program were named using the convention Leg # -Site #. One or more of the above named issues were encountered in roughly 10% of the data we compiled. We did not exhaustively investigate the methods used to obtain compositional data, but they can be found through the links to the cited original manuscripts. The database produced by  Seiter et al. (2004)  gives some information on methods used for TOC analysis where available. Traditional methods to analyze total carbon in the sediments  (Bisutti et al. , 2004)  have involved CHN elemental analyzers; TOC is then found by difference, after the sample has been acidified to remove inorganic carbon. CaCO 3 is often measured directly by acidifying a sample and analyzing the evolved CO 2 by coulometric titration  (Mörth & Backman, 2011).
The traditional alkaline leach methods were designed for high opal, siliceous oozes but may underestimate the total reactive silicon (including authigenic clays) in low opal sediments. This bias potentially leads to underestimation of total silicon burial, especially in the continental margins with low opal contents but very high accumulation rates  (Rahman et al. , 2017). In an intercalibration exercise, the XRD method gave biogenic silica concentrations that were on average 24% higher than the wet chemical methods  (Conley, 1998). For the benefit of data users, the database contains a note as to the method used for biogenic opal when this information was available. No general statements can be made about the methods used for trace element analysis in the compilation, as these have evolved significantly over recent decades and many methods currently exist (e.
g. ,  Sandroni et al. , 2003; Wien et al. , 2005; Yuan et al. , 2004). For Fe, reported data included in the database are bulk elemental concentrations. For Hg, we report total Hg concentrations as they were determined in the original studies, in addition to some unpublished work from the Aix Marseille Université group. In cases where inorganic and methylated Hg were reported, we used the sum of these species. We ignored Hg concentrations above 500 ng/g, which are likely due to local Hg contamination. With regard to barium, we focused on a fraction of the total sedimentary barium referred to as excess Ba, biogenic Ba, or Ba xs. The upper continental crust contains on average 628 ppm Ba  (Rudnick & Gao, 2014). Excess barium refers to the fraction that occurs in the sediments in excess of the lithogenic component.
We contend that interpolation of the observed Th-normalized fluxes within zones for the deep sea is a reasonable solution. Even in the deep sea, future determination of Th-normalized fluxes could help fill data gap regions (Figure  1a ). We derive component fluxes for the major components (F i in Equation  2 ) in two ways. First, for each sediment composition data point (X i ) in the database, a biogeochemical province was assigned based on its geographic coordinates and a Th-normalized flux (F) was assigned based on its corresponding province and water depth. This method produces an estimate of the component flux at each point for which a composition data point is available and allows us to retain as much information as possible from the sediment composition databases. Second, we also derived zone averages of the compositional data and used these along with the assigned Th-normalized fluxes to derive zone-average component fluxes.
CaCO 3 The database of CaCO 3 content on the seafloor is the most spatially complete, with 7,792 data points distributed across all ocean basins (Figure  2a ). Sediments at shallower water depths and areas with higher carbonate ion concentration in subsurface water, such as the North Atlantic, can have 80%-100% carbonate content  (Archer, 1996) , and near zero concentrations can be found in the deep North Pacific, the Southern Ocean and parts of the Arctic Ocean. When computed as 230 Th-normalized fluxes (Figures  2b  and 2c ), the North Atlantic still stands out as the highest flux region (>2 g/cm 2 kyr), whereas the high carbonate content regions of the Pacific have a more subdued signal due to the lower fluxes in that basin (<1 g/cm 2 kyr). Interestingly, the Arabian Sea has CaCO 3 fluxes nearly as high as the North Atlantic, and there is a sharp transition from higher to lower CaCO 3 fluxes in the Southern Ocean near ∼55°S in the Pacific and ∼40°S in the Atlantic and Indian oceans.
, 1969). The Fe concentrations of samples included in this ocean sediment database range from 0. 02% to ∼20% Fe, with a median of 3. 1%. The distribution of the Fe/Al ratio (Figure  6 ; note not all Fe data points had available Al data) is one indication of the source of iron, as the upper continental crust has a Fe/Al weight ratio of 0. 48  (Rudnick & Gao, 2014)  and mid-ocean ridge basalts have a ratio of 1. 01  (White & Klein, 2013). Excluding highly Fe enriched samples with Fe/Al > 1. 5, about 50% of the ratio observations are near or below the upper continental crust average. Areas with enriched Fe/Al ratios include GEOTRACES expedition GP16 (∼10°-20°S, 90°-140°W), which underlies an extended hydrothermal plume emanating westward from the East Pacific Rise  (Fitzsimmons et al. , 2017; Resing et al.
Excess Barium Our database contains 1,457 estimates of Ba xs. As a relatively minor component of the sediments (0-∼5000 ppm), Ba xs weight fractions are influenced by dilution of the varying total sediment flux, for instance with lower weight fractions in the North Atlantic versus the North Pacific on average (Figure  9a ). When converted to flux (Figure  9b ), there are higher Ba xs fluxes in the eastern equatorial Pacific, the subarctic Pacific, across several frontal zones in the Southern Ocean, and in the Arabian Sea, with lower fluxes found in the central Pacific, the tropical Atlantic and the Arctic. Importantly, barite, the main carrier of Ba xs , is not preserved in sulphate reducing sediments  (Riedinger et al. , 2006; Torres et al. , 1996) ; hence, areas like upwelling zones or restricted basins may not preserve the original deposition flux.
CaCO 3 Burial The deep-sea burial of CaCO 3 derived from our interpolation is 136 ± 34 Tg C/yr. Cartapanis et al. (2018)  discuss that the range of existing deep-sea CaCO 3 burial estimates is 100-150 Tg C/yr, strikingly in-line with our estimate (Figure  10 ). To examine our estimate further, we look at the relative contributions of the ocean basins to the total CaCO 3 burial. In our flux estimations, the following fractions of CaCO 3 flux were buried in the Atlantic (40%), Pacific (22%), Indian (20%), Southern (17%), and Arctic (0. 4%) Oceans. The basin boundaries were defined using the Longhurst provinces in Figure  1. These percentages compare well to the ranges for the basin CaCO 3 flux percentages from  Cartapanis et al. (2018) , based on a smaller data set of Th-normalized fluxes, and from the model of  Dunne et al.
Each flux data point (i. e. , as plotted in Figures  3b, 4b , and 9b) in our database was matched with the closest grid point in the NPP and EP estimates. We also compared discrete observations for which Th-normalized flux (at >1000 m water depth) and component composition were measured on the same core (n = 444 for opal flux, n = 247 for TOC flux, and n = 211 for Ba xs flux) to NPP and EP, to remove the uncertainty associated with spatial interpolation. It is only for the relationships with these directly observed component fluxes that we report correlation statistics below.
Imprints of Deep Particulate Flux in Sedimentary Fluxes While it proved challenging to find the imprint of near surface productivity in our sedimentary database, we explored the relationship of our burial fluxes to subsurface particle fluxes. Deep particle fluxes have undergone some regeneration/dissolution processes since being exported from the surface but have not yet experienced diagenesis at the seafloor while being buried. The knowledge of deep particulate flux is much more limited than either surface or bottom fluxes, but this information is critical especially for the interpretation of paleo-productivity proxies. The information we have is mainly limited to sediment trap studies, although the GEOTRACES program is currently mapping out deep particulate fluxes on transect cruise studies using in-situ pumped particulate 230 Th  (Hayes et al.
This is higher than the often quoted 1% preservation of organic carbon export (e. g. ,  (Hain et al. , 2014) ) but, of course, our comparison here represents a relatively limited number of sites. As more water column particulate fluxes become available, particularly from understudied regions, the exercises shown here demonstrate how the sedimentary flux database will be valuable in evaluating preservation controls on key components of the ocean's biogeochemical cycles.
Conclusions In this study, we have prepared an updated global marine sediment composition database, including major and minor components, covering many marginal seas that were previous data gaps. We further demonstrated how a global compilation of Holocene Th-normalized fluxes can be used to estimate the flux of sedimentary components in the deep sea. This allowed us to compute global deep-sea burial rates of CaCO 3 , opal, TOC, Hg, and Ba xs. Our estimate of deep-seal opal burial is roughly a factor of 2 greater than from previous estimates and suggests roughly equal magnitude in deep-sea and continental margin Si burial. The other burial rates derived here will provide a quantitative target for ocean biogeochemical models. We also investigated preservation effects in the relationship between surface productivity, export productivity, deep particulate flux and sedimentary fluxes.
Un signal d'amplification significatif est obtenu pour la plus forte quantité d'extrait introduite (0,9 µl). Témoins négatifs : blanc d'extraction et blanc de PCR. B -Confirmation de l'espèce identifiée par analyse des séquences d'ADN (11 163 fragments). Sur le panneau du haut, la hauteur de chaque lettre indique la fréquence relative du nucléotide correspondant. Seule la séquence située entre les amorces de PCR est présentée. Le panneau du bas montre que la séquence consensus est identique à la séquence de référence du génome mitochondrial de Bison priscus (Genbank : NC_027233) et présente 2 à 3 substitutions par rapport aux fragments orthologues de Bison schoetensacki (NC_033873), Bison bonasus (NC_014044) et Bos primigenius (NC_013996). C et D -Photographies de l'échantillon LF8-19. Échelle : 1 cm.
Detection of steppe bison (Bison priscus) DNA in LF8-19 sample. A -Real-time PCR analysis carried out using the TaqMan assay for steppe bison mitochondrial DNA. Note that a PCR signal is obtained using 0,9 µl of the DNA extract. Negative controls: PCR blank and mock extract. B-DNA sequencing (11, 163 DNA reads) confirms the presence of steppe bison DNA in the sample. The upper panel displays the sequence logo derived from DNA reads. Only the sequence located between the PCR primers is shown. The lower part of the figure shows that the DNA sequence derived from LF8-19 is identical to the mitochondrial genome reference sequence of Bison priscus (Genbank : NC_027233), and displays 2-3 substitutions with the orthologous fragments of Bison schoetensacki (NC_033873), Bison bonasus (NC_014044), and Bos primigenius (NC_013996) mitochondrial sequences.
En particulier, la richesse et la diversité taxonomiques d'un habitat constitue un aspect de la biodiversité de même que la rareté d'une espèce en fonction de l'espace considéré : par exemple la Dryade à huit pétales (Dryas octopetala L. ), plante abondante en milieu de toundra polaire ou en très haute montagne, représente un enjeu de conservation si elle est présente à basse altitude en tant que témoignage d'un habitat relique, comme c'est le cas sur certains éboulis froids  (Debay et Huc, 2015). Dans ce cas, on combine l'intérêt de patrimonialiser les écosystèmes et les supports géomorphologiques.
Analyse morphopédologique, morphométrique et anthracologique La démarche a consisté à recenser et cartographier les formes de sols polygonaux puis à se focaliser sur trois unités morphopédologiques. Une carte des sols polygonaux au Col du Noyer a été réalisée par photo-interprétation à partir d'orthophotographie à haute précision de l'IGN en utilisant le logiciel QGIS 2. 18 LTR -Las Palmas (2018). Ces données ont permis de recenser de manière exhaustive les polygones et d'en produire une analyse statistique descriptive. Des paramètres morphométriques et des profils topographiques (Figure  2  et 3) ont été relevés sur trois polygones choisis pour leur significativité dans la population recensée. Nous avons réalisé des coupes topographiques précises sur les trois sols polygonaux retenus afin de préciser la morphologie de surface et de la comparer aux données des relevés floristiques.
Earthquake Location and Magnitude Estimation Twenty-four stations from two recent temporary seismic networks installed in Afar (Figure  2a )  (Doubre et al. , 2017; Keir et al. , 2017)  were operational on March 24, 2018 when the M W 5. 2 earthquake struck the NWAM, east of Mekele. We visually inspected 41 days of continuous seismic recordings, from March 20 to April 30, 2018 and manually picked both P and S waves for earthquakes recorded by four or more stations (Text S1). We located the events using the Oct-Tree search algorithm implemented into the NLLoc software  (Lomax et al. , 2000). For the location, we used a 2. 5D seismic velocity model reproducing the large-scale crustal structure of the WAM (Text S2). A correct velocity model and an accurate Vp/Vs ratio are crucial for a precise earthquake location.
Seismicity is shallow, between 5 and 15 km (Figure  S2 ) with 75 earthquakes scattered in time rather than clustered in seismic sequences, as observed in the Dergaha graben. Furthermore, the occurrence of the earthquakes in this area seems to be temporally independent from the main seismic sequence (Figures  3d  and 3e ). Interestingly, a pattern in the spatiotemporal distribution of earthquakes with low error in the hypocentral location can be inferred from the profiles in Figure  S2. The main sequence takes place close to the southern tip of Dergaha at depth of 19-25 km, where the largest earthquake occurred. After that, earthquakes show a progression to shallower crustal levels and toward the northern tip of Dergaha which could indicate a migration of seismicity. To improve the resolution of hypocenter locations and highlight active fault planes, we relocated 262 earthquakes using the double-difference method implemented in the HypoDD software and a minimum of 10 observations per event pair  (Waldhauser, 2001; Waldhauser & Ellsworth, 2000).
(2020a)  suggested that extension is still ongoing along the NWAM. The spatial correspondence between our observations of dominant normal faulting and their structural and seismic data set are consistent. Additionally, we also show that deep seismicity occurs in this region and that dissimilarities between the temporal and spatial distributions of the seismicity in Dergaha and in the other sectors of the NWAM exist. In particular, the seismicity south of Berhale is spread over time and seems to occur independently from the main seismic sequence striking the Dergaha graben. Furthermore, our new observations of the 2018 sequence coupled with the ∼50 years long NEIC catalog (Figure  2 ) showing deep earthquakes focused below the Dergaha graben suggests that the mechanism driving seismicity here may be different from that causing seismicity along the rest of the NWAM, where only upper crustal earthquakes are observed (e.
Data Availability Statement The continuous seismic data used in this study are archived on the IRIS-DMC (http://www. fdsn. org/networks/detail/YQ_2017/; http://www. fdsn. org/networks/detail/YP_2017/). The data will become fully open access with the publication of this manuscript, or once the 3-years embargo period finishes at the end of 2021, whichever is sooner.
g. Balzeau et al. , 2009; Balzeau and Charlier, 2016; Balzeau et al. , 2017; Buck et al. , 2019)  to allow comparisons with these studies. Metric data were used for two different purposes: to study variation in the 3D extension of the sinuses among and between samples and to study possible differences in the bilateral extension and proportions of the sinuses. For the quantitative analyses, eight linear dimensions were measured as 2D projections in three different orientations (figure  2 ). These measurements define the maximum extension of the frontal sinuses superiorly, inferiorly, laterally, anteriorly and posteriorly, including bilateral data for the two sides of the sinus. Because of the variation observed among extant samples, and for future applications to incomplete hominin fossils, complex quantification including multiple characterizations of smaller details is not possible as it would produce noisy results, without clear information, and would allow only very small samples or would imply a multitude of comparisons for few variables.
Morphometric comparisons Comparisons between samples of the extension of pneumatisation concerned variables W and H, as well as the lateral extension of the sinus in the anterior and superior views and the anteroposterior lengths of the sinuses. The lateral extensions were measured separately on the left and right side for bilateral comparisons. For the comparative analyses, the two bilateral measurements were combined together; the maximal length of the two sinuses in the anterior and superior views are labelled 2AL (corresponding to ALr + ALl) and 2SL (corresponding to SLr + SLl). The two anteroposterior dimensions were also combined as a single variable, defined as 2AP, by addition of the two measurements (AP and AP2). The raw data for these five variables (W, H, 2AL, 2SL, 2AP) were compared in three dimensions.
Sinus volume is reported as the cube root of the volume of pneumatisation (denoted RCV). Cube root values are used instead of volume for two reasons. The first is that it is in comparable dimensions to the other variables (moreover, the incomplete preservation of the sinuses may have the most influence on volume of all the variables analysed). Using cube roots allowed us to minimize the magnitude of any potential measurement error. Finally, this variable was used for trait size correction for subsequent analyses. The resulting relative data set analysed corresponds to the variables labelled Wr, Hr, 2ALr, 2SLr and 2Apr. The calculated size-corrected dimensions were also used to compare variation in the shape of pneumatisation between the samples. The resulting dataset was subjected to multivariate analyses, including PCA (with the variancecovariance matrix) and discriminant analyses.
PCA is an unsupervised method that plots specimens independently of their group of origin. It can be informative to observe the relationships between variables. Discriminant analyses are useful to produce the maximum separation between groups along two axes and to evaluate levels of classification accuracy a posteriori. These two complementary methods were chosen to maximise the information extracted from the dataset. The coefficient of variation (CV = SD/mean), corrected for small sample size using the V* parameter, calculated as [(1 + 1/4N) × CV] and expressed in percentages  (Sokal and Braumann, 1980; Wood and Lieberman, 2001) , was used to compare the extent of variation between samples. All the statistical procedures were conducted in Past ver. 4. 02  (Hammer et al. , 2001). Linear regressions were calculated with the Reduced Major Axis method  (Miller and Kahn, 1962) , which handles errors in both variables  (Smith, 2009).
This protocol for the morphometric quantification of the sinuses and study of their bilateral variation is similar to those used to quantify the petalias of the brain  (Balzeau and Gilissen, 2010; Balzeau et al. , 2011). The complete morphometric dataset was carefully checked for sampling errors through visual inspection of calculated distances and examination of their variation within and between samples. Grubbs' test statistic  (Grubbs, 1969)  was used to recognize statistically significant outliers (which have more effect on the variance than on the mean). Significant outliers were excluded from analyses of FA, even if the values were related to normal variation and were not measurement errors, so as not to bias the detection of patterns of symmetry. Shapiro-Wilk tests were used to determine whether the variables followed a Gaussian distribution.