dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the abundance  the small sample sizes (9, 17 and 8 otoliths in MIS 20, 19 and 18, respectively). Other families identified in the dataset have too few specimens to identify any significant trends (electronic supplementary material, figure  S4 ;  [60] ). (a) 0 1 -1 -2 -3 2 3 0 1 -1 -2 -3 2 3 0 1 -1 -2 -3 2 3 The median weight among cold-water species does not significantly differ between the three stages (Kruskal-Wallis test χ 2 = 4. 065, d. f. = 2, p = 0. 13; figure  4 ), while the weight of warm-water species decreases from MIS 20 to MIS 19 (Kruskal-Wallis test χ 2 = 16. 843, d. f. = 2, p < 0. 001; pairwise Wilcoxon test p < 0. 001 for MIS 20-19, p = 0. 05 for MIS 20-18 and p = 0. 42 for MIS. The relative abundance of coldwater (temperate and a few subpolar species) clearly drops during the MIS 19 interglacial, whereas warm-water species have a higher contribution to the MIS 19 assemblage (figure  4 ).
Acknowledgements. The authors would like to acknowledge  Jean-Jacques Cornée  and  Pierre Moissette  for showing us the Lardos section and for the insightful discussions on the geology of Rhodes. We would like to thank  George Kontakiotis  and  Vasiliki Lianou  for their help in the field, and  Norbert Frotzler  for drawing the fish head used in our electronic supplementary material, figure S1. This research contributes to the objectives of Q-MARE (a PAGES working group). We would like to thank the five anonymous reviewers for the very helpful comments on our manuscript.
Eulerian gridding of Lagrangian velocities The Eulerian gridding consists of mapping the Lagrangian data into a Eulerian gridded field. The gridded field is obtained either by averaging Lagrangian velocities in geographical bins  (LaCasce, 2008)  or interpolating all Lagrangian data surrounding a location to a cell. Various methods of interpolation can be used to determine the values of the cell such as linear interpolation, optimal interpolation, variational methods etc. The average of the observations inside the cell also corresponds to an interpolation method if it is used with a filter that eliminates data farther than a certain distance from the center of the cell. Rather than the classical averaging of all Lagrangian devices available together in the same grid, we propose an alternative method that corrects for the number of data points reported in a given area by slowly and quickly moving instruments.
This averaging procedure also accounts for differences in the number of Lagrangian devices between studies -studies that use a lot of Lagrangian data will compare it to a large amount of Eulerian data. However, in the limit of a single Lagrangian instrument, this method converges to the comparison outlined in step #1. Thus, it can be necessary to first assess whether the Lagrangian data sufficiently cover the region of interest before using this progressive averaging method. One way to assess the coverage of data is to compare the gridded Eulerian field following this method to the time-averaged Eulerian field covering all time steps. The gridded Lagrangian and Eulerian products are then compared to evaluate the differences relative to spatial patterns of velocity and kinetic energy, directions and magnitudes of currents, and even spectra of velocities if the size of the domain allows it.
As pointed out by LaCasce (  2008 ) the choice of bin size in this type of analysis is not trivial. Bins that are too large will smooth out the field and bins that are too small will make the mean very sensitive to eddies and other fine-scale structures. In most of the cases, a convenient choice to facilitate the comparison is to use an identical grid as the original Eulerian gridded products. However, it may be interesting to test a higher resolution to evaluate the induced changes and the impact of gridding. The gridding of Lagrangian data performed on the same grid than the Eulerian product and the gridding performed on a higher resolution grid are compared in the same way as the gridded fields from Lagrangian and Eulerian data streams (spatial patterns, direction and magnitudes. ).
Observed and synthetic trajectories The third part of the validation consists of the computation of synthetic Lagrangian trajectories from the gridded Eulerian field (Fig. 3 ). This corresponds to a conversion of the Eulerian data into the Lagrangian frame of reference. For this, the main idea is to compute a synthetic trajectory by advecting a Lagrangian particle through the Eulerian velocity field  (Liu and Weisberg, 2011). The resulting synthetic trajectory must then be compared to the observed trajectory and evaluated using a metric. Various methods could be used such as the distance between the ending points of trajectories, the main direction of trajectories, or a cloud of points, etc. We have found the methodology proposed by  Liu and Weisberg (2011)  particularly instructive: at each location of the Lagrangian data, a new synthetic particle is released and advected through the velocity field for three days.
And on the practical side, surface currents can be used to aid search and rescue operations  (Serra et al. , 2020) , oil spills, and other debris-related searches  (Durgadoo et al. , 2021). We also have large data sets of surface velocities that are worth comparing. Indeed, the Global Drifter Program (GDP) produces a large data set of in situ Lagrangian data from surface drifters since the 1980s, and satellite altimeters have produced gridded maps of sea-surface height (SSH) and altimetry-derived surface currents since 1993. As an application, to test the methodology with a surface current problem, we applied the three steps presented previously to a set of Surface Velocity Program (SVP) drifters to evaluate the accuracy of Altimetry-Derived Surface Currents (ADSC) around Cape Farewell, the southern tip of Greenland.
The freshwater from the East Greenland Coastal Current rounding Cape Farewell is a critical component for the climate system because of the proximity to sites of deep open ocean convection that are sensitive to surface density changes. Using the presented methodology, we evaluate the ADSC to check whether it is acceptable to use this very valuable observational data set to study the pathways of freshwater in the region where the observations were historically very sparse and where the discrepancies between modelling studies concerning the shelf-basin exchanges must be investigated.
, 2021). This MDT gathers altimetry, gravity and drifter data. It shows better results in all regions around the globe compared to the previous product CNES-CLS15 especially 195 in coastal areas. The geostrophic currents are computed using a nine-point stencil width methodology  (Arbic et al. , 2012). In the following work, we refer to this data set as "Geostrophy". In addition to the two ADSC data sets, we also compare the drifter velocities to the "Arctic Ocean Physics Analysis and Forecast" product from the operational TOPAZ4 Arctic Ocean system  (Sakov et al. , 2012). This is a completely different data set in that it assimilates all possible data streams (e. g. Argo, satellites) rather than only relying on satellite altimetry and wind products. The goal of including TOPAZ in the first step of this methodology is to provide context to the results with ADSC data sets.
Taylor diagrams and skill scores S (equ. 9)  (Taylor, 2001)  are used to concatenate all statistics. S = 4(1 + r) ( σf + 1/ σf ) 2. (1 + r 0 ) (9) with r 0 the maximum expected correlation coefficient (here taken equal to one). In the second step of the methodology, the velocities from the surface drifters are mapped onto a Eulerian grid that has the same 1/4 • grid as the ADSC. A separate grid is constructed for each of the 34 drifters and then averaged together to produce a single mapped velocity field from the drifters. The same process is then applied to ADSC to compute mean flows synchronous drifters' velocity vectors are determined and finally the ratio ADSC velocities over drifter velocities is computed. The influence of gridding and the consistency of the 1/4 • drifter gridding with the original drifter data is evaluated by comparing the results at 1/4 • resolution to 1/12 •.
The Ekman component can influence the along-shelf currents by winds that are misaligned with the currents. This misalignment occurs frequently in the vicinity of Cape Farewell as the dominant wind patterns are no longer constrained by the topography of Greenland and the winds cross the shelf. Thus there is good reason for why the Ekman contribution is surprisingly large in the along-shelf direction. The Geostrophy+Ekman data set are also available at 6-hour frequency due to the higher frequency wind products, so we also investigated the difference between the 6-hourly data and the 6-hourly drifter data. Recall that we chose the daily Geo. +Ekman data for more direct comparison to the Geostrophy only fields, which are only available at daily resolution. The 6-hourly Eulerian data compared to 6-hourly drifter data have comparable correlation coefficients but weaker normalized standard deviations than the other altimetry-derived products.
This data set has also a larger RMSE (Fig. 5 e ) but the percent variance explained is close to the daily data. Two parameters affect this comparison simultaneously: the higher temporal resolution of the Geostrophy+Ekman 6-hourly field that should imply a better accuracy and also the higher temporal resolution of the drifter data to which the Eulerian data are compared that contains more variability than the daily drifter data. The 6-hour data is therefore likely more realistic, but the drifters' motion is also more variable at this shorter temporal scale and thus more difficult to reproduce. The drifters, Geostrophy and Geostrophy+Ekman products have been gridded using the method prescribed above in section 2. 2. The vector map  (Fig 6)  shows that drifters mainly sampled the shelf around Cape Farewell.
Alternatively, we observe two hot spots where the magnitudes of velocities are different: South-East of Cape Farewell where Ekman contribution increases the magnitudes and North-West of Cape Farewell, around Julianehaab Bight, with Ekman contribution decreasing them. The ADSC seem able to reproduce the current's direction with high accuracy, but the magnitude is strongly underestimated. We suggest that this underestimation is due to the gridding of the along-track altimetry data that smoothes the velocities, and likely underestimates their true variance. On the shelfbreak the direction and the magnitude of currents is well resolved, which seems to indicate a good ability of ADSC to represent shelf-basin exchanges. The Ekman contribution seems to be a small contribution to the total velocity but improves both direction and magnitude consistently in both the along-shelf and across-shelf directions.
In this section, we presented a methodology to compute mean flows from gridded Eulerian fields that closely simulates the progressive sampling of the domain by the surface drifters, so that the gridded Eulerian fields are averaged in the same spatial/temporal domain as the drifters. We also evaluated how sensitive this result is using a simple time average of the gridded Eulerian fields. Figure  S4  (supplementary material) compares the Geostrophy and Geostrophy+Ekman velocity fields computed with these two different methods. They yield very similar fields, except in the region near the shelfbreak south of Cape Desolation. In this region, Geostrophy+Ekman computed with the 'drifter following' methodology clearly differs from the other fields. In our example, the methodology does not change much for Geostrophy only data set and the time averaged Geostrophy+Ekman field is very close to those Geostrophy fields.
41 on the shelf. The ADSC data they used were a combination AVISO (delayed time) + Rio2009 (MDT) + Ekman, which corresponds to the "Geostrophy + Ekman" product evaluated in our work. When the Ekman component is removed from their ADSC product, the open ocean skill score does not change but the shelf one decreases to 0. 35. We can use their results as a reference to interpret the results obtained in the present work. Here, the vast majority of our drifters traveled on the shelf, and we calculate a mean skill score of 0. 47 for Geostrophy only and 0. 50 for Geostrophy+Ekman. Our skill scores on the shelf with the Ekman component (0. 50) exceed the Liu et al. (2014) skill scores on the shelf (0. 41) and are comparable to those obtained in the open ocean. Without Ekman, the mean score obtained with data mainly located on the shelf is 0.
Though our initial 'null hypothesis' was that the ADSC would have trouble resolving the shelf circulation, the common result across all three steps of this proposed methodology was that the ADSC were capturing critical components of the circulation. A large caveat of our results here has been that these results are specific to a roughly two-week period in Aug/Sep 2021 and may or may not be representative of the longer-term variability. To address this concern, we used a database of 34 drifters from the 6-hourly data set  (Lumpkin and Centurioni, 2019)  derived from Global Drifter Program (GDP) data that crossed onto the shelf from 1993-2021 (Supplementary Material Section 2). The results from extending the temporal scope show a good coherence between surface drifters' trajectories and altimetry-derived surface currents from 1996 to 2020 in all seasons investigated, leading us to believe that this good correspondence is not specific to our brief study period, albeit with limited data to test.
Acknowledgements. This work was funded by the  National Science Foundation  grant number  2047952. TERIFIC was funded from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement No  803140 ). The authors would like to thank  Lucas Drumetz ,  Eleanor Frajka-Williams ,  Renske Gelderloos ,  Christophe Maes ,  Bob Pickart ,  Florian Sévellec  and  Pierre Tandeo  for their input and guidance during this project.
Code availability. The python codes to implement the presented methodology are available on the "ADSC-SVP-Comparison" repository (https://github. com/arthurarsene/ADSC-SVP-Comparison). Data availability. The surface geostrophic current data set "Global Ocean Gridded L4 Sea Surface Heights And Derived Variables Nrt" is publicly available on the Copernicus Marine Service CMEMS website (https://doi. org/10. 48670/moi-00149). The version including Ekman contribution named "Global Total Surface and 15m Current (COPERNICUS-GLOBCURRENT) from Altimetric Geostrophic Current and Modeled Ekman Current Processing" is also available on this website (https://doi. org/10. 48670/moi-00049). Finally, CMEMS website also provides TOPAZ4  (Sakov et al. , 2012)  reanalysis in open access (https://doi. org/10. 48670/moi-00001).
"Global Drifter Program qualitycontrolled 6-hour interpolated data" from  Lumpkin and Centurioni (2019)  are publicly available (https://www. aoml. noaa. gov/phod/gdp/ interpolated/data/all. php). The comparison with GDP data has been perfored on the reprocessed versions of altimetry-derived surface currents available on CMEMS as "Global Ocean Gridded L 4 Sea Surface Heights And Derived Variables Reprocessed 1993 Ongoing" (https:// doi. org/10. 48670/moi-00148) and "Global Total Surface and 15m Current (COPERNICUS-GLOBCURRENT) from Altimetric Geostrophic Current and Modeled Ekman Current Reprocessing" (https://doi. org/10. 48670/moi-00050). The deployed drifters from GFWE and TERIFIC are in the process of being incorporated in the GDP data set at the moment of submitting this manuscript. Author contributions.
The composition of SOM was studied by a combination of isotopic (δ 13 C and δ 15 N values) and lipid (aliphatic hydrocarbons (n-alkanes and hopanes), sterols and stanols, fatty acids, fatty alcohols, and aromatic hydrocarbons including polycyclic aromatic hydrocarbons-PAH) analyses classically used to investigate the sources of SOM  [Freese et al. , 2008 , Volkman et al. , 2000]. The ability of sediments to release NH + 4 and PO 3- 4 fluxes through diffusion were determined using incubations with intact sediment cores under the same ambient experimental conditions (dark, controlled temperature, artificial overlying water)  [Louis et al. , 2021]. Finally such a dataset allows the use of canonical redundancy analysis (RDA) and variance partitioning in order to explain the spatial variability of the NH + 4 and PO 3- 4 fluxes as independent parameters related to (i) the variation of the SOM origin (elemental and isotopic ratios, lipid markers) and (ii) the variation of the elemental composition (N, P, C) and physical properties (grain size, porosity) of the sediment [from  Louis et al.
Even though the flux measurements were carried out with 10 cm depth sediments, the nutrient fluxes are the result of gradients over the first 2 to 5 cm  [Lee and Kim, 1990, Akbarzadeh et al. , 2018]. In order to capture this reactive zone, the first five cm of the sediment were analyzed for physico-chemical parameters. Elemental composition (total organic C, total N, total, organic and iron oxide bound P), grain-size distribution, porosity and benthic nutrients fluxes were described in a previous paper  [Louis et al. , 2021] , while isotopic and lipid compositions are the new data discussed in this paper.
A Canonical Redundancy Analysis (RDA) was chosen in both cases, assuming a linear relationship between the response and explanatory variables. This dissymmetrical analysis combines the concepts of ordination and regression, with explanatory variables used to recalculate the response variables. The new canonical axes are thus a linear combination of the initial explanatory variables. The best explanatory variables in each matrix were selected by the "ordistep" function of the "vegan" package [R software  ; Borcard et al. , 2018 , Oksanen et al. , 2013]  based on the Akaike Information Criterion (AIC) through automatic permutation tests of the ordination model and forward model selection. The non-collinearity between the selected variables was then checked with the Variance Inflation Factor (VIF) (a threshold value was set at 10).
Variance partitioning and canonical redundancy analysis Significant variables from the various datasets were selected to determine the relationship between biodegradability and OM sources while taking the sediment properties into account. The grain-size distribution, the porosity, the elemental composition of bulk OM and benthic nutrient fluxes were taken from  Louis et al. [2021]. Benthic NH + 4 and PO 3-  4   fluxes are shown in Figure  6. In the "SOM origin" data matrix, the selected variables were δ 13 C values, δ 15 N values, and six lipid marker groups: "Fecal", "Petroleum products", "Microalgae", "Plants 3", "Plants 4" and "Bacteria 1" (see Table  1  for the composition of these groups). Regarding the "physicochemical composition" data matrix, the selected variables were the Fe-P, Org-P and the porosity.
How the origin of SOM impacts benthic nutrient fluxes? The origin of SOM has often been suggested as a driver of SOM biodegradation and benthic nutrient fluxes  [LaRowe et al. , 2020]. This large dataset allowed for the first time to significantly quantify this relationship for NH + 4 and PO 3- 4 fluxes produced by mudflats in tidal environments. The benthic NH + 4 and PO 3- 4 fluxes, measured from the sediment core incubations, were significantly correlated (p < 0. 005) to the SOM origin. The percentage of variance for the NH + 4 and PO 3- 4 fluxes explained only by the "SOM origin" variables was 16%, compared to 6% (for NH + 4 fluxes) and 3% (for PO 3- 4 fluxes) for the "physico-chemical composition" variables. By combining all variables, the explained percentage of variance for the NH + 4 and PO 3- 4 fluxes reached 30 and 34% respectively.
Acknowledgments This work was funded by  Loire-Bretagne Water Agency  and the  Regional Council of Brittany (France). It was carried out as a part of the IMPRO research project. The authors would like to thank  Josette Launay  ( CRESEB ) for the project coordination. The authors would also like to thank  P. Petitjean ,  C. Petton ,  G. Bouger ,  O. Jambon  and  C. Roose-Amsaleg  for their assistance in the field. The CEVA, the  Syndicat Mixte EPTB Rance-Fremur , the  Pays de Guingamp , the  Syndicat Mixte des Bassins du Haut-Léon , the  Syndicat des Eaux du Bas-Léon, Lorient-Agglomération , the  Syndicat Mixte du SAGE Ouest-Cornouaille (OUESCO), Concarneau-Cornouaille-Agglomération , the  Syndicat Mixte de la Ria d'Etel (SMRE)  and the  Syndicat Mixte du Loc'h et du Sal (SMLS)  are acknowledged for their help preparing the field campaign.
2019 also recorded the highest rate of increase between two consecutive years. This rise in temperature may be man-made, with manganese extraction in the Okondja region by Nouvelle Gabon Mining (NGM) beginning in earnest in 2019. This assertion is supported by the IPCC, whose latest report states that global warming is unprecedented and caused by human activities  [20]. There is a close link between climate signals (temperature and precipitation) and plant production. Nevertheless, we deplore the absence of precipitation data. The non-exploration of rainfall data is due to their relative unavailability due to the small number of rain gauge stations in Central Africa  [27]. Changes in these climatic variables would therefore have an impact on the reproductive cycle of certain trees  [7]. This supports the idea that an increase of one hundredth of a degree Celsius in mean annual temperature influences the reproductive process of Moabi.
ACKNOWLEDGEMENTS I would like to thank the  Direction des Eaux et Forêts du Haut-Ogooué (Haut-Ogooué Department of Water and Forests)  for kindly introducing me to the advantages of conducting this study in  Lékédi National Park. I would also like to thank all those interviewed who kindly gave up some of their precious time to answer my questions. With this modest contribution, I would also like to express my deep gratitude to the  Institut de Recherche pour le Développement (IRD)  for its financial and technical support throughout this thesis.
Description of data Data from nine cohorts of adult patients who underwent LB for evaluation of NAFLD with concomitant blood work-up for routine biological markers and LSM by VCTE (FibroScan, Echosens, France) were gathered. Data came from North America, Eastern & Western Europe and Asia. The TRIPOD guidelines  6  were followed to report the development and internal and external validation of the prediction model for diagnosis of cirrhosis and AF (Table  S1 ). Seven cohorts came from secondary/tertiary hepatology clinics, one cohort came from the baseline visit (including screen failure patients) from a clinical trial and one cohort came from the NAFLD Adult Database 2 of the Non-alcoholic Steatohepatitis Clinical Research Network (NASH CRN, NIDDK) (also all tertiary care hepatology clinics).
All cohort data were collected in the framework of a clinical study for which the local ethical committee granted approval and may have already been used completely or in part for other publications (Tables  S2  and S3 ). Patients gave written informed consent to participate in the studies. Each study was conducted in accordance with the Declaration of Helsinki and in agreement with the International Conference on Harmonization guidelines on Good Clinical Practice. FibroScan operators were masked to patients' clinical and histological data. All LB results were read by expert pathologists blinded by patients' clinical data and FibroScan results. Among these nine cohorts, seven were pooled together to constitute the internal dataset that was then randomly split into a training set (TS) and an internal validation set (VS) (2:1) by stratifying on cohort and fibrosis stage.