dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the two datasets result in a mismatch for the long-term trends, with 15 N excess suggesting a cooling trend. To reconcile the 15 N excess with the borehole temperature profile, a correction of +0. 0046 ‰ is applied to 15 N excess : this minimizes the standard deviation of the temperature reconstruction as it tries to squeeze in rapid temperature changes to arrange for diverging datasets if 15 N excess is left uncorrected (Figs. A16  and A17 ). This 15 N excess correction for ice samples could be related to expulsion of gases through ice matrix during bubble formation in the firn-to-ice transition  (Severinghaus and Battle, 2006) , although the effect on 15 N excess has never been clearly quantified  (Kobashi et al. , 2015). We apply the same correction to all points because it results from physical processes during the formation of bubbles, and we consider it to remain constant at the timescales studied here.
Climate implications Many regions of the globe including Antarctica have been marked by a cooling trend during the past 2000 years, until the beginning of the industrial era  (PAGES 2k Consortium, 2013). Climate history of the Antarctic region was reconstructed from ice core water isotopes  (Stenni et al. , 2017) , with some cores on the East Antarctic plateau where ABN is located (EDC, TALDICE), and the coastal region of Wilkes Land (Law Dome, location of coring sites is indicated on Fig. 1 ). We selected ice cores from the Antarctica2k database  (Stenni et al. , 2017 ) that cover at least the past millennium until the pre-industrial era (1000-1900 CE) and plotted the δ 18 O or δD, representative of the temperature, and their 1000 to 1900 CE trend for each core (Fig. 10 ). To avoid biases based on differing calibration methods, comparisons are made using isotope values directly (δD or δ 18 O).
Elevation was determined with truck GPS position during upstream radar profiling. The profile was taken twice: moving away from the coring site (way out) and going back to the coring site (way back). Original GPS coordinates were not taken in optimal conditions (moving truck), hence the uncertainty. (Xiao et al. , 2013; Pang et al. , 2015)  and extended Antarctic snow database  (Goursaud et al. , 2018). We restricted our slope to sites at elevations between 2000 and 3250 m a. s. l. and longitudes from 80 to 160 • E. The model simulates the diffusion and advection of temperature in the ice. Given an initial state, the temperature will diffuse depending on boundary conditions such as surface temperature, advection due to accumulation of ice, compaction, and bottom temperature. We set the compaction to follow the ice density profile measured in the core, under the assumption that it did not change significantly in the past.
A5. 3 Age significance of data points in the reconstruction The diffusion of temperature in the firn depends on both snow and ice properties but also the accumulation rate at the ice core site. At the West Antarctic Ice Sheet Divide (WAIS Divide), the temperature measured at depths corresponding to the last millennium was estimated to integrate temperature with a smoothing window of several hundred years  (Orsi et al. , 2012) ; for ABN where the ice accumulation is half as high as WAIS Divide, we expect even greater smoothing windows. The temporal range of temperature changes recorded by 15 N excess at ABN is limited at the minimum by the diffusion time of gases between the surface and the lock-in zone and at maximum by the smoothing out of the temperature gradient by the temperature diffusion in the ice column.
5 covariance reached at 70 years of difference. Therefore, the reconstruction includes multiple gas data points for each inversion point, strengthening the confidence in the reconstructed temperature. In our figures, we chose to represent the inver-sion at the time resolution of points when there is a gas-based temperature constraint. Data availability. Data created during this study, including age models for ABN1314 ice and gases, water isotopes (δ 18 O and δD), gas isotopes (δ 15 N and δ 40 Ar), borehole temperature, methane, and ice density, as well as temperature reconstructions, are accessible in a repository on the Australian Antarctic Division data centre web site  (Moy et al. , 2023, https://doi. org/10. 26179/5qyn-xf50). Author contributions. MC directed the ABN ice core drilling project.
AO and AS designed the study. Raw water isotope data were measured and curated by AM. AO designed the gas isotope analysis, and AS performed the gas isotope analysis. TP measured the borehole temperature. AO secured financial resources for gas laboratory analyses and, together with AL, supervised stable isotope interpretations. JMC measured water stable isotopes in the shallow core used for calibration. JMC, XF, and JC measured the methane used for the gas age model. ELM provided upstream GPS elevation profiles and radar reflector surfaces. MC performed the annual layer counting and provided volcanic tie ages for the ice age model. AO adapted the ice diffusion model and designed the inversion algorithm. AS performed the diffusion model simulations. AS and AO interpreted the climate implications of the results.
We are grateful to the two referees for their comments on the paper. Financial support. This research has been supported by the Australian Antarctic Division (grant no. AAS project 4075), the Institut Polaire Français Paul Emile Victor (CHICTABA project no. 1115), and the Institut national des sciences de l'Univers (LEFE project ABN2k). Review statement. This paper was edited by Elizabeth Thomas and reviewed by two anonymous referees.
ASTROPHYSICAL MODELING To assess the impact of the new collisional data on the astrophysical modeling of cometary comae, we performed radiative transfer calculations to simulate the excitation of CO in CO-dominated comae. Non-LTE radiative transfer calculations were performed with the R code (van der Tak et al. 2007) using the escape probability approximation at steady-state. In the radiative transfer calculations, we consider only radiative and collisional processes in order to model the relative population of the CO rotational levels, i. e. state-selective reactive processes such as photodissociation are neglected. All models presented in this work were performed assuming the typical physical conditions for a CO-dominated coma at an heliocentric distance = 3 au (see below). In order to describe the density profile of CO molecules in the coma, we used a classic Haser model  (Haser 1957)  which is expressed by the following formula: ( ) = 4 2 v (- v ) (3) where is the nucleocentric distance, is the production rate of the CO molecules fixed at 10 28 s -1 , v is the expansion velocity fixed at 0.
ACKNOWLEDGEMENTS F. L. acknowledges  Rennes Metropole  for financial support. This research was supported by the  CNRS   national program  ' Physique et Chimie du Milieu Interstellaire '. J. L. acknowledges support from  Internal Funds KU Leuven  through Grant No. M. A. C was supported by the  National Science Foundation  under Grant No. AST-2009253. We wish to acknowledge the support from the  CEA/GENCI  for awarding us access to the TGCC/IRENE supercomputer within the  A0110413001  project and from  Wroclaw Centre for Networking and Supercomputing.
s. l. , and was close to zero at higher elevations  (King et al. , 2020). Within this context a recent study surprisingly suggested substantial thinning of the South Col Glacier, located at 8020 m a. s. l. (Potocki et al. , 2022). Based on the interpretation of an ice core and surface energy balance modeling,  Potocki et al. (2022)  estimated that contemporary thinning rates -or ablation rates, because the authors did not consider ice dynamics -were as a single glacier in the original GGI18 inventory (Fig. 1 ). Following DEM differencing, surface elevation change data  (dH)  were filtered to remove outliers, with values outside the range of five times the standard deviation of dH estimates within 50 m elevation bands removed below 6800 m a. s. l. Above 6800 m, or from the base of the much steeper Lhotse face, we applied a threshold of three times the standard deviation of dH estimates, as the range of elevation change values were higher here.
Any resulting minor data voids were filled using mean values from surrounding pixels within the dH grid. Uncertainty associated with the average elevation change over glacier surfaces (σ ∆z,g ) was established following the methods of  Rolstad et al. (2009)  and  Fischer et al. (2015) , whereby: σ ∆z,g =    σ 2 ∆h Acor 5Ag ; A g ≥ A cor A g σ ∆h ; A g < A cor (1) where σ ∆h (m) is the standard deviation of dH over stable, off-glacier terrain, A cor = πL 2 , with L being the distance over which dH data are spatially correlated  (Rolstad et al. , 2009) , here measured to be 1193 m, and A g is the area of the glacier. Within a 50 m diameter circle centered on the drilling location, the elevation change is 1. 7 ± 2. 2 m for the period 1984-2017, corresponding to an elevation change rate of 0.
We horizontally shifted 14 images with poor co-registration, by manually selecting four ground control points. All other images are well co-registered and orthorectified in the original SRE product. We produce natural color composites using the band combination 7-4-3, corresponding to red, green and blue bands, respectively. We use the equalize_adapthist function from Python scikit-image package to obtain a rendering that highlights the blue ice areas (Fig. 2 ). We rely on visual inspection of the images only, and did not attempt to automatically classify the snow covered areas. The South Col Glacier exhibits strong seasonal contrasts in terms of snow cover. Here we show only one image per month for the year 2019 (Fig. 2 ), but the whole image series is available  (Brun, 2022). From January to June, the glacier surface is partially covered by snow and ice is exposed in multiple places.
Code and data availability. The dH map is available upon request and will be available on a public repository, pending publication. ,The Pléiades DEM is available at https://doi. org/10. 5281/zenodo. 6979691  (Berthier, 2022). License restrictions apply to the Pléiades ortho images, that are available upon request to E. B. , pending the signature of license agreement with CNES. VENµS data are available at: https://doi. org/10. 5281/zenodo. 6685515  (Brun, 2022)  The wind erosion code is available at: https://github. com/antonplanchot/wind-snow-scg  (Planchot and Amory, 2022)  Mass balance simulations presented in this study are available at https://doi. org/10. 5281/zenodo. 7006744, as well as forcing and initialisation information to reproduce Crocus simulations. SURFEX/ISBA-Crocus model based on version v8.
A similar coupling has previously been shown to successfully capture the spatial-temporal biodiversity patterns of early Paleozoic acritarchs  20. In the model, thousands of pseudo-species were generated, each one occupying a unique thermal niche sensu Hutchinson  25. In each model grid point, biodiversity is defined as the number of pseudo-species whose thermal niche overlaps the mean annual ocean temperature. The lowermost (tmin = -1. 8 °C) and uppermost (tmax = 44 °C) temperature bounds of the niches were chosen to provide the best simulation of biodiversity spatial patterns in the Modern ocean  21. We focused our analysis on the low-to-mid continental shelves and excluded both the abyss and polar (> 60 ° latitude) regions since they are not well represented in paleontological databases  20, 26, 27.
Our model does not capture the Late Ordovician Mass Extinction (ca. 445 to 443 Ma), which was caused by a combination of factors that are not accounted for here, including Hirnantian sudden cooling, sea-level fall and possibly anoxia  [29] [30] [31]. Model results were compared with three different fossil global biodiversity curves, mainly composed of brachiopods, trilobites and conodonts (Fig. 2 ). Differences between these curves arise from the variable temporal resolution, with the seminal curve of Sepkoski et al. (ref. 5  ) exhibiting the lowest resolution (> 5 Myrs). Differences also reflect the use of different datasets. Rasmussen et al. (ref. 32  ) based their compilation on the Paleobiology Database (PBDB), which mostly includes data from Laurentia and Baltica (> 50% in 2019), while the GeoBiodiversity Database 3 (GBDB) mainly includes Chinese data.
Regional biodiversification patterns Our spatially-resolved simulations allow biodiversification signals to be compared in different paleocontinents. For comparisons, we used the brachiopod database of ref. 6  (Fig. 5 ). Our model captures the increase in fossil brachiopod biodiversity in Laurentia, Siberia and South China (Fig. 5a-c ), although with varying degrees of model-data agreement. In Laurentia and Siberia, simulated biodiversity rises too early compared to data. In South China, the general timing of biodiversity increase matches the paleontological database between 490 Ma and 450 Ma. Afterwards, model biodiversity stabilizes while paleontological data show a large drop in biodiversity. This discrepancy in South China probably reflects the absence of Late Ordovician Mass Extinction in our simulations.
Model-data mismatch in Laurentia and Siberia may reflect limitations in our numerical model, uncertainties in temperature change during the Ordovician (see e. g. error margins of ref. 9  ), errors in the latitudinal position of landmasses (often exceeding 15°, see ref. 38 )  or biases in the paleontological database, but most probably represents a combination of all these factors. Baltica contrasts with other continents by showing a mostly flat simulated biodiversity temporal trend, while brachiopod data exhibit a strong increase between 490 and 450 Ma (Fig. 5d ). The muted model biodiversity change arises from the combination of the latitudinal drift of Baltica to the North during the Ordovician with the long-term Ordovician cooling trend, which together make Baltica closely track a climatic belt in response to global cooling (Fig.
1 ). As a result, temperature in the Baltica epicontinental shelves remain virtually constant, and so does simulated biodiversity. Although the same factors listed above probably contribute to model-data discrepancy, it is suspected that the continental reconstruction plays a key role here. Indeed, the uncertainty in the latitudinal position of Baltica is large in the Ordovician (Extended Data Fig. 5 ). Alternative paleogeographical reconstructions  39  notably exhibit a smaller latitudinal drift of Baltica -from lower latitudes at 490 Ma to higher latitudes at 430 Ma-that could help bring model and data into closer agreement (Extended Data Fig. 5 ). Yet, it should be noted that these reconstructions hold even larger uncertainties in the latitudinal position of Baltica due to the lower number of paleomagnetic poles in use (3 in ref.
ACKNOWLEDGMENTS Calculations were partly performed using HPC resources from  DNUM CCUB (Centre de Calcul de l'Université de Bourgogne). This is a contribution of  UMR 6282 Biogéosciences Team  ' SEDS '. This is a contribution to  UNESCO  project  IGCP 735  " Rocks and the   Rise of Ordovician Life" (Rocks n ' ROL). The author acknowledges the support of the  French Agence Nationale de la Recherche (ANR)  under reference  ANR-22-CE01-0003  (project  ECO-BOOST ). We also thank the  CPER programme IDEAL.
LBGs reconstructed based on paleontological data and obscure model-data comparison 37 , simulated spatial-temporal patterns of biodiversity are supported by the LBG of Cambrian and Ordovician acritarchs, which show a minimum in biodiversity along the equator at 490 Ma and a shift of maximum biodiversity from ~60 °S at 480 Ma to the low latitudes during the Late Ordovician 20. Our results are also supported by the LBG independently reconstructed during the Ordovician by Kröger et al. (ref. 26 ) based on the Paleobiology Database 4 , which shows a transition from a bimodal Early Ordovician LBG to a.
Thus, in our extract, the specific peaks of the ν-carrageenan type are few, and only observed in G/D6S* at 871 cm -1 but with moderately low intensity. In contrast, traces of ι-, κand νcarrageenan peaks are well highlighted and we find that most of the peaks have ι-type traces. In addition to this identification, we performed a PCA (Principal Component Analysis) study to distinguish polysaccharides (Figure  7 ). This statistical analysis shows that the majority of the extracts are ι-type carrageenans. Principal Component Analysis (PCA) is an extremely powerful tool for compressing and synthesizing information, which is especially useful when there is a large amount of quantitative data to process and interpret  [44]. Twenty-five samples were analyzed by FTIR, coded by month and year of sampling.
A volume of absolute ethanol equal to twice the volume of the filtrate recovered is gradually added to the filtrate. Under strong manual stirring, the polysaccharides precipitate and form a ball around the glass stirrer. The precipitate is disaggregated and washed with ethanol. The resulting fibers are placed in an oven at 60 • C for one day. They are then finely ground in a mortar and stored away from light in a dry place at room temperature (18-22 • C) until use. For the same sample, the polysaccharide extractions were carried out in triplicate and the mass yields were expressed as a percentage of algal dry matter. The polysaccharide powders were analyzed by infrared spectrometer and then analyzed using the Unscrambler software. Polysaccharide yields and IR spectra were compared between samples harvested monthly (Jan = January; Feb = February; Mar = March; Apr= April; May; Jun = June; Jul =July, Aug = August; Sep = September; Oct = October; Nov = November; Dec = December).
FTIR Analysis FTIR spectra were recorded on a Nicolet iS5 FTIR spectrometer (Thermo Scientific, Waltham, MA, USA) using an iD7 ATR module with a diamond crystal. For the analysis of polysaccharides from Eucheuma denticulatum and Solieria chordalis, the dried powders were measured and compared to a standard ιand κ-carrageenan standard (Sigma commercial grade). Alginates and fucoidans from S. muticum were also compared to standards. All spectra resulted from 16 scans and a resolution of 4 (0. 482 cm -1 ). OMNIC 9. 2. 86 software (Thermo Scientific, Waltham, MA, USA) was used for data acquisition (absorbance mode). A background reference is obtained before each sample measurement. All the samples are recorded in triplicates.
Statistical Analysis One-way analysis of variance (ANOVA) was used to find differences in carrageenan yield (SigmaPlot 12. 0, Systat, Chicago, IL, USA). Significant differences between the different extracts were indicated by superscript letters at a significance level α = 0. 05. The comparison includes the analysis of samples with n = 3 replicates. Unscrambler X 10. 4 software (Aspentech, Bedford, MA, USA) was used to synthesize the spectra using the PCA method.
These specimens are part of the palaeobotanical collections of the Universit e de Montpellier; the specimen from Graissessac has accession number G2283; those from Autun have numbers ARX1, ARX2 and ARX3. At least 50 measurements were taken for each wood character using the free software ImageJ (https://imagej. net/Welcome). These data are archived in the Dryad repository  (Tosal et al. 2023). The measurements indicated in the text were made on the thin sections. A comparison of the tracheid diameter between the thin sections and the peels showed that although the range was similar, the average size could be slightly different. Such differences were taken into account when comparing the material studied with previous taxonomic descriptions. Throughout the text, equivalences between global and European regional chronostratigraphic units follow that of  Heckel & Clayton (2006).
Acknowledgements. This study was partly funded by a  Fundaci o Universit aria Agust ı Pedro Pons  grant ( Universitat de Barcelona) , the project  IBERINSULA  ( PID2020-113912GB-100 ) funded by  MCIN/AEI/10. 13039/501100011033  and the  European Regional Development Fund (ERDF) , and the project  Geologia Sediment aria  ( 2022 SGR-349 ) of the  AGAUR, Catalan Research Agency. This study is also a contribution to the research goals of AMAP (botAny and  Modelling of Plant Architecture and vegetation ), which is a joint research unit involving  Montpellier University ,  CNRS  ( UMR 5120 ),  CIRAD  ( UMR51 ),  INRAe  ( UMR931 ) and  IRD  (UR123). Finally, we are grateful to  Dr Ronny R€  obler, the two anonymous reviewers and the editor  Professor Benjamin Bomfleur , for suggestions that improved the manuscript.
Results Locating the landward limit of the oceanic crust. Mapping the Continent-Ocean Boundary (COB) using different criteria and datasets usually do not correlate to the same location revealing the ambiguity of COB definitions  20. Here we use the "landward limit of the oceanic crust" (LaLOC) that was previously introduced on both magma poor  21  and volcanic rifted margins  22. However, instead of using the seaward edge of SDRs  22  , that may be found locally in oceanic crust  23  , we base our work on the remarkably constant structure and thickness of oceanic crust formed by steady-state seafloor-spreading at mid-ocean ridges (Fig. 1 ). At fast spreading ridges, as well as at magma-rich intermediate to slow spreading ridges, along flow lines, the Moho is broadly parallel to the top of the crust (at tens of kilometres scale  18  ) indicating that oceanic accretion is a steady-state process.
g. 2 s TWTT). By contrast, leaving the oceanic domain, we determine the LaLOC as the first continent-ward occurrence of inflexion points at both the top of the crust, which shallows, and at the base of the crust, which deepens. These inflexion points mark thus the departure from the more or less constant crustal geometry (the top of basement parallel to the Moho) typically found in the oceanic domain (Fig. 1 ). Mean oceanic crustal thickness at the LaLOC was then calculated along 5 km-long sections ocean-ward of the LaLOC. The 6. 65 ± 0. 47 km mean oceanic crustal thickness, obtained using all seismic profiles, is similar to the 6. 62 ± 0. 86 km thickness average for > 100 Ma old oceanic crusts away from hotspots calculated using 23 thickness values of the compiled data set of Christeson et al.
Acknowledgements We thank ION for permitting publication of their  NamibiaSPAN™ ArgentinaSPAN™, UruguaySPAN™, PelotasS-PAN™ and BrazilSPAN™ seismic data. The MM6 consortium ( BHP, BP, CGG, CNOOC, Ecopetrol, Equinor, Hess, ION, Petrobras, Repsol, Shell, Sinopec ) financially supported this project. Support was also provided by the  French Institut National des Sciences de l'Univers INSU-CNRS. François Chauvet  and  Jenny Collier  are warmly thanked for discussion and sharing data. We also gratefully acknowledge  Sascha Brune ,  Attila Balazs  and the Editor,  Taras Gerya , for their review and helpful comments.
Data availability Thanks to ION™ the seismic data supporting this study are available within the paper and the supplementary information file and can be used as so to reproduce the findings. The raw data are now private properties of TGS™ which have to be contacted for any lending or acquisition (https:// www. tgs. com/). The gravity grid of Sandwell et al. (  2014 ) is freely available from the website of the UCSD (topex. ucsd. edu). The global Earth Magnetic Anomaly Grid at 2 arc minute resolution version 3 (EMAG2v3) is freely available from the web site of the NOAA (www. ncei. noaa. gov).