dataesr/scientific-paragraphs-categorization huggingface dataset:
Palaeoclimatic reconstruction To test the influence of GPM choice on fossil-based palaeoclimatic reconstructions, we estimate the palaeolatitudinal extent of (sub-)tropical climatic conditions using two climatically sensitive entities: warm-water coral reefs and terrestrial crocodylomorphs. These entities are frequently used to infer the extent of (sub-)tropical palaeoclimatic conditions due to their limited thermal tolerance and geographic distribution today (e. g. Markwick, 2007; Burgener et al. , 2023). Fossil crocodylomorph occurrences (="Crocodylomorpha") were downloaded from The Paleobiology Database (https://paleobiodb. org/#/) on the 9 th of November 2022 and were restricted to terrestrial taxa and body fossils. Marine taxa were excluded as they appear to be less constrained by climatic factors than terrestrial taxa  (Mannion et al.
, 2015). Fossil coral reef occurrences were downloaded from the PaleoReef Database (Kiessling and Krause, 2022) on the 10 th of March 2022 and were restricted to scleractinian 'true reefs' with a tropical affinity. This led us to exclude pre-Anisian reef occurrences. At the end of the cleaning, the midpoint age of oldest crocodylomorph occurrence was 232. 5 Ma, and the oldest reef occurrence was 231. 5 Ma. Finally, fossil occurrences were split into 10 Myr time bins using the midpoint age of each occurrence's age range. After data processing, 4638 terrestrial crocodylomorphs and 419 warm-water coral reef occurrences remained for analyses. Using the five GPMs (Table  1 ), we reconstructed the palaeocoordinates for each occurrence based on the midpoint of its respective age range. Assuming hemispheric symmetry in climatic conditions, we subsequently identified the maximum absolute palaeolatitude within each time bin-for each model and entity-to infer the palaeolatitudinal extent of (sub-)tropical climatic conditions.
2% of cells having a value of more than 1000 km in the Cenozoic, 20. 2% during the Mesozoic, and 80% during the Palaeozoic (Fig. 3b ). Differences in Cambrian (538. 8-485. 4 Ma) reconstructions are especially large, with a considerable proportion of cells having a palaeolatitudinal SD of more than 5º (~76. 3%) and mean PGD of more than 1000 km (~90. 1%) (Fig. 3 ; Fig. S3 ). Notably, despite this overall trend of increasing palaeogeographic uncertainty with age, uncertainty is relatively low during the latest Carboniferous and Permian in comparison to the rest of the Palaeozoic and early Mesozoic (Fig. 3 ). Triassic (Tr) , Jurassic (J), Cretaceous (K), Paleogene (Pg), and Neogene (Ng). The Quaternary is not depicted. The Geological Time Scale axis was added to the plot using the R package 'deeptime' ver.
43; P = 0. 05 for coral reefs and R = 0. 55; P = 0. 005 for crocodylomorphs). Comparisons of reconstructed palaeocoordinates for all occurrences further support an increase in palaeolatitudinal uncertainty with age (Fig. S5 ). For example, the average palaeolatitudinal range between the five GPMs for each occurrence-within each time bin-increases significantly with age (Pearson's correlation test: R = 0. 49; P < 0. 001 for coral reefs and R = 0. 33; P < 0. 001 for crocodylomorphs). Global Plate Model abbreviations are the same as in Figure  1. The Geological Time Scale axis was added to the plot using the R package 'deeptime' ver. 1. 0. 1  (Gearty, 2023).
Pre-Jurassic palaeolongitudinal uncertainty The direct reconstruction of palaeolongitude is challenging for most of Earth's history. While palaeomagnetic data can be used to constrain the relative palaeolatitudinal position of plates and continents, they cannot directly constrain palaeolongitude  (Vérard, 2019). Hotspot tracks, which record the motion of a tectonic plate over a mantle plume, enable the determination of palaeolongitude (with respect to the base of the mantle), but owing to the incessant recycling of oceanic crust, well-resolved hotspot tracks are limited to the last ~130 Ma  (Seton et al. 2023). Marine magnetic anomalies enable the determination of relative palaeolongitude back to the time of Pangaea breakup (~200 Ma), but they cannot constrain absolute palaeolongitude. Other attempts to constrain palaeolongitude in deeper time remain controversial  (Torsvik et al.
Study limitations We quantified Phanerozoic (540-0 Ma) palaeogeographic uncertainty between five GPMs using a global hexagonal grid (~100 km spacings). While the use of additional models such as the commonly applied Getech plate model would be desirable to further quantify palaeogeographic uncertainty (e. g. Chiarenza et al. , 2019; Saupe et al. , 2019) , not all GPMs are open access and broadly available to the community. Despite this, the use of additional models is unlikely to change the general trends observed in this study which are primarily driven by limited data availability (e. g. palaeomagnetic data and constraints on palaeolongitude) and geological interpretation (e. g. tectonic boundaries). Furthermore, the spatial resolution of our study (~100 km spacings) may influence our results in the identification of areas of high palaeogeographic uncertainty.
Acknowledgements We are grateful for the efforts of all those who have contributed to collecting the fossil data used in this study and entering them into the Paleobiology Database and the PaleoReefs Database. L. B. was funded by the  École Normale Supérieure de Lyon, France. L. A. J. and S. V. were funded by the  European Research Council  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement  947921 ) as part of the  MAPAS  project. L. A. J. was also supported by a  Juan de la Ciervaformación 2021   fellowship  ( FJC2021-046695-I ) funded by  MCIN/AEI/10. 13039/501100011033  and the  European Union NextGenerationEU/PRTR. SZ was supported by  Australian Research Council  grant  DE210100084. (py) GPlates  development is funded by the  AuScope National Collaborative.
METHODOLOGICAL BASIS OF GLOBAL PLATE MODELS Global Plate Models (or Global Plate Rotation Models, GPMs) use the Euler rotation theorem to describe the motion of geometries-such as tectonic plates or geological terranes-on a sphere. A variety of tools have been developed to build global plate tectonic models. However, the most widely used tool is the cross-platform and open-source GPlates (www. gplates. org) software  (Boyden et al. , 2011; Müller et al. , 2018)  which is frequently applied to reconstruct the past geographic distribution of fossil samples using various GPMs (e. g. Allen et al. , 2020; Boddy et al. , 2022; Jones & Eichenseer, 2021). A GPM is primarily made up of two key components. The first component represents the geometries that define geological boundaries, such as terranes and/or parcels of oceanic crust.
To isolate the low-frequency content of the large DAS dataset (11 Terabytes) and make it manageable for signal processing in a standard workstation, we applied a temporal moving average on the strain time series of each channel independently. Then, to convert the LF-DAS apparent strain values into corresponding absolute temperature anomalies (variations), we used the approximation: dε/dT = nα + dn/dT  38, 42, 46, 47  , where where ε is the (apparent) strain recorded by DAS, T is temperature, n the optical fiber refractive index and α its ther- mal expansion coefficient. This conversion, as well as the full data pre-processing scheme, are detailed in the "Methods" section.
Methods Instruments The DAS interrogator unit used for our main analysis is a φ-OTDR hDAS (High fidelity distributed acoustic sen- sor) designed by Aragón Photonics, which provides measurements in strain units. One specificity of the hDAS system is the fact that it sends a chirped light signal. Details can be found in  57, 58. The time series data sampling frequency was 100 Hz in the first couple of days of the campaign and then switched to 500 Hz. The DSTS system used to validate the simultaneous LF-DAS (indirect) measurements was a Febus Optics G1-C set to record with a gauge length of 10 m and sampling resolution of 2. 0 m over 30 km. The temporal sampling was set to 15 min to keep the data noise level at a reasonable level. The DAS system in this case was a Febus A1-R DAS interrogator with gauge length of 10 m and sampling resolution of 4.
The nearly 17 days of data were segmented in three (3) sections during acquisition due to two separate interrogator reboots. Visual inspection of the raw data shows that each of these sections has noticeable value offsets and two time gaps in-between (4 and 76 minutes each) in between (see Supplementary Fig. S6 ). To correct this, we demean the first time segment and adjust the remaining segments with respect to the last value of the previous ones to ensure continuity between them and to smooth out large data breaks. This "segment levelling" is performed on each channel separately. The two data gaps were filled using cubic interpolation between segments to ensure signal continuity for processing routines that require continuous time series (spectral decomposition and filtering). The resulting dataset shows good continuity, as observed from Fig.
Acknowledgements This work and J. P. Q. were supported by the  SEAFOOD  project, funded by grant  ANR-17-CE04-0007  of the  French Agence Nationale de la Recherche ,  Université Côte d '  Azur IDEX program   UCA   JEDI ANR-15-IDEX-0001 , and the  Doeblin Federation  ( FR2800  CNRS). J. P. Q. was partially supported by project  MARMOR , funded by  ANR-21-ESRE-0020  of the  France 2030 program  of the  French Agence Nationale de la Recherche. The June 2022 DAS acquisition was made possible by the  MoniDAS  project, funded by grant  ANR-19-CE04-0011 , of the  French Agence Nationale de la Recherche , and the support of  Febus Optics. The  T-MEDNet  project is funded by  MPA-ENGAGE (Interreg Mediterranean Programme ). We also want to thank  Anne Molcard  from the  Mediterranean Institute of Oceanography (MIO ) and  Camille Jestin  and  Gaëtan Calbris  from  Febus Optics  for enriching suggestions and discussion.
Data availability The fiber optic DSTS and the processed LF-DAS data, as well as times series used to produce Figs. 2 3 4 5 , and S1, S4 and S6 are available in the following OSF repository: https:// osf. io/ 6jf9r (https:// doi. org/ 10. 17605/ OSF. IO/ 6JF9R). The main DAS dataset (Figs. 2,4  and S1 ) was recorded on the seafloor LSPM (Laboratoire Sous-marin Provence Méditerranée) cable south of Toulon, which was part of the Mediterranean Eurocentre for Underwater Sciences and Technologies (MEUST) infrastructure at the time of acquisition (see Sladen et al. 23  for details) using an Aragón Photonics hDAS interrogator. MEUST is financed with the support of the CNRSIN2P3, the Region Sud, France (CPER the State (DRRT), and FEDER. Auxiliary DAS and DSTS datasets were recorded on the same cable using a Febus Optics G1-C and a Febus A1-R interrogators, respectively.
OPEN ACCESS EDITED BY Alessandro Amorosi, University of Bologna, Italy provenance, transport processes, and depositional environments. After 25 years of development, EMMA is now available in various numerical contexts, such as FORTRAN code  (Weltje, 1997) , Matlab script  (Dietze et al. , 2012; Patersion and Helslop, 2015) , and the R package EMMAgeo  (Dietze and Dietze, 2019). This method has been successfully and widely applied in many studies, including  Prins et al. (2002) ,  Dietze et al. (2012) ,  Collins et al. (2016)  and  López-González et al. (2019). The method, when combined with other available information such as wind conditions, tides, bottom flow has been used to determine sediment transport processes, provenance, and paleo-climate changes, including in Greenland, Lake Donggi Cona in China, the Harney Basin in eastern Oregon (United States), and the Alboran Sea.
Geostatistics is used to improve the determination of this parameter. A new distance (Dg) is proposed though the analysis of the semi-variogram  (Poizot et al. , 2006). The choice of the trend type to be adopted is based on the vector modulus and the number of neighbors satisfying the same trend type condition. Despite the aim of universality asserted by the initiators of both STA ® and GSTA, some works have reported the failure of the model to recover true sediment transports  (Masselink, 1992; Carriquiry et al. , 2001; Ríos et al. , 2002). McLaren et al. (2007)  and  Poizot et al. (2008)  reviewed the various sources of uncertainties in STA ® /GSTA methods. In developing the software called GiSedTrend,  Poizot and Méar (2010)  applied STA ® /GSTA analysis, giving the highest possible degree of freedom to scientists in their choice.
Materials In September 2020, a total of 54 cores were collected from appr. 5-30 m water depth by a gravity core device (Figure  1 ). The core samples (max. 60 cm in length) covered the entire study area. Surface sediments from 0-5 cm depth were sampled from the tops of gravity cores. In each case, approximately 0. 2 g of surface sediments were treated with HCl and H 2 O 2 to remove carbonate and organic matter, respectively. Grain size analyses were performed using the HORIBA Laser Diffraction Particle Size Analyzer LA960. The measurement range was from 0. 01 μm to 5,000 μm (20 to -0. 5 ϕ). The GRADISTAT v9. 1 program from  Blott and Pye (2001)  was used to calculate textural parameters based on the  Folk and Ward (1957)  percentile statistics. The results of sediment parameters from GRADISTAT include mean grain size (Md), sort (So), and skewness (Sk), shown in Table  1.
End member modeling analysis The grain size distribution of surface (0-5 cm) samples (Figure  2 ) in the study area is wide-ranging, from 0 to 890 μm, and has frequency peaks between 7 and 300 μm. Samples from most locations are mainly composed of fine particle sediments. Using EMMAgeo, the R t 2 has been calculated from the data set (Figure  3A ) using 2 to 10 EM. The results show that with 2 EM, the coefficient reaches 0. 77 and that this value increases gradually (0. 855, 0. 86, 0. 89) as the number of end members increases to 3, 4, and 5. Using 4 and 5 EM, most of the grain sizes from the model results are consistent with the dataset. By increasing the number of end members, the average coefficient does not increase and tends to decrease, showing the optimal model results at 5 EM. Figure  3  shows the default graphical output, provided by EMMAgeo, for five end members.
Methods A model-data fusion product was created by applying adjustments to the outputs of 14 CMIP6 ESMs with the latest observational data in the steps below: 1. Sea surface temperature (SST), sea surface salinity (SSS), DIC, and TA were extracted or calculated from SOCAT (version 2022), GLODAPv2 (version 2022), and CODAP-NA (version 2021). The temporally adjusted DIC (to the year of 2010), as well as SST, SSS, and TA were interpolated onto a global surface ocean 1° × 1° grid. 2. Proxy-based DIC and TA calculated with gridded temperature, salinity, and dissolved oxygen content (DO) from the World Ocean Atlas (WOA-2018) were used to further quality control (QC) the gridded observational DIC and TA data, especially in data-sparse regions. 3. The temporal evolution (decadal averages of every 10 years) of global SST, SSS, DIC, and TA from 1850 to 2100 out of 14 ESMs was adjusted with offsets that were derived based on the differences between the model output in 2010 and the corresponding observational data in 2010 at that grid point (see Section 2.
Observational Data The latest observational ocean carbon data from  SOCAT (version 2022 SOCAT (version , 1957 SOCAT (version -2021, 33, 33. 7 million surface-only fCO 2 observations,  Bakker et al. , 2016 ), GLODAPv2 (version 2022 , 1972 -2021, 1. 4  million discrete bottle-based surface and subsurface observations,  Lauvset et al. , 2022 ), and CODAP-NA (version 2021 , 2003 -2018, 3,391  discrete bottle-based surface and subsurface observations,  Jiang et al. , 2021)  were used for this analysis. SOCAT provides the best temporal and spatial coverage, but has only one ocean carbon parameter: fCO 2. Only SOCAT data with a World Ocean Circulation Experiment (WOCE) QC flag of 2 (good data) and a cruise flag of A, B, C, and D (fCO 2 accuracy ≤5 μatm) were used for this analysis  (Bakker et al.
, 2016). TA, phosphate content, and silicate content, which are required to calculate the rest of the OA indicators, were estimated using the Empirical Seawater Property Estimation Routines (ESPER) from SST and SSS  (Carter et al. , 2021). ESPER is an empirical algorithm that is trained with global surface and interior ocean variables. It allows a user to estimate DIC, TA, and several other seawater properties from input variables, such as salinity (minimally required), temperature, and dissolved oxygen (DO), along with longitude, latitude, and depth. GLODAPv2 and CODAP-NA have substantially sparser spatial coverage than SOCAT, but offer additional ocean carbon variables. Only data from these products with at least a pair of DIC and TA, DIC and pH, or TA and pH, and a sampling depth of ≤20 m were used for this analysis.
If DIC, TA, and pH were all available, DIC and TA were used. In all observational data products, DIC was adjusted to 2010 using ESPER. Two sets of ESPER calculations were conducted based on input variables of temperature, salinity, longitude, latitude, and depth: one with the atmospheric CO 2 dry-air mixing ratio at the sampling time and another one with the atmospheric CO 2 dry-air mixing ratio at 2010. The difference in the output DIC between the two sets of ESPER calculations was assumed to be the temporal DIC change due to the ocean's absorption of anthropogenic CO 2 from the sampling year to 2010. This difference was then used to adjust DIC to the year of 2010. The CO 2 system calculation was performed using a Julia version of the CO2SYS (CO2System. jl, v2. 0. 5, https://github. com/mvdh7/CO2System.
jl,  Humphreys et al. , 2022)  with the dissociation constants and parameters as recommended by  Jiang et al. (2022) , that is, dissociation constants for carbonic acid of  Lueker et al. (2000) , bisulfate (HSO 4 -) of  Dickson (1990) , hydrofluoric acid (HF) of  Perez and Fraga (1987) , and the total borate content of  Lee et al. (2010). DIC adjusted to 2010, as well as SST, SSS, and TA from these three data products were then pooled as one observational database that was interpolated onto a global surface ocean 1° × 1° grid. This product is primarily based on SOCAT due to its much larger data quantity. 19,193,038 data points (or 99. 8% of the total) compared to only 44,165 data points (0. 2%) from the GLODAPv2 and CODAP-NA combined. That said, data points from GLODAPv2 and CODAP-NA play an unproportionally important role in filling up some data sparse regions (see Figure  1  of  Feely et al.
, 2023). Interpolation and gap-filling was done with a Julia version of the Data-Interpolating Variational Analysis (DIVAnd. jl, https://github. com/gher-ulg/DIVAnd. jl,  Barth et al. , 2014). The correlation lengths of 21° latitudinally and 42° longitudinally, as well as the noise to signal ratio of 0. 1, were selected to strike a balance between retaining some regional variability and smoothing over fine scale variability that is present in synoptic measurements but not representative of climatological conditions (see also  Jiang et al. , 2019).
Estimating OA Indicators in 1750 As most ESMs start their historical simulations at model year 1850, an approximation of OA indicators in 1750 is obtained using ice core-based atmospheric CO 2 data from 1752 (276. 39ppm) and 1852 (288. 57ppm) (Ethridge et al. , 1996; MacFarling Meure et al. , 2006). These data were used to approximate the oceanic fCO 2 change from 1750 to 1850 at all locations of the global ocean under the assumptions that SST, SSS, and TA were constant from 1750 to 1850. Similarly, the other OA indicators for 1750 were calculated using CO2System. jl with the recommended set of parameters described in Section 2. 1.
Conclusions A model-data fusion product was created by synthesizing 14 CMIP6 ESMs (Table  S3  in Supporting Information S1) and three observational data products. This product provides distributions of 10 global surface OA indicators, including fCO 2 , pH T, [H + ] total , [H + ] free , [CO 3 2-], Ω arag , Ω calc , RF, DIC, and TA, under past-reconstruction (1750), historical (1850-2010), and future (2020-2100) scenarios. The model outputs were bias-adjusted with the most recent and highest quality ocean carbon observations available, and drift-adjusted with preindustrial control (piControl) values to highlight the fundamental agreements among the CMIP6 models on the evolution of most carbonate system variables under historical to future scenarios. Despite several caveats, the use of the most recent observational datasets and a large ESM ensemble represents a major step forward in the projection of future surface ocean OA indicators and provides critical information to guide OA mitigation and adaptation efforts.
Data Availability Statement All model simulations are available at the CMIP6 data portals: https://esgf-node. llnl. gov/projects/cmip6/. Observational data used in this study: -The Surface Ocean CO 2 Atlas (SOCAT, version 2022): https://www. ncei. noaa. gov/data/oceans/ncei/ocads/ metadata/0253659. html (https://doi. org/10. 25921/1h9f-nb73). -The Global Ocean Data Analysis Project version 2 (GLODAPv2, version 2022): https://www. ncei. noaa. gov/ data/oceans/ncei/ocads/metadata/0257247. html (https://doi. org/10. 25921/1f4w-0t92). -The Coastal Ocean Data Analysis Product in North America (CODAP-NA, version 2021): https://www. ncei. noaa. gov/data/oceans/ncei/ocads/metadata/0219960. html (https://doi. org/10. 25921/531n-c230). -World Ocean Atlas (2018): https://www. ncei. noaa. gov/access/world-ocean-atlas-2018/.
The opposed variations of herbaceous plus steppic plus subdesertic plants compared to mesothermal elements suggest climate fluctuations along the Carmona succession. Climatic parameters have been quantified using the Climatic Amplitude Method. At first sight, climate at Carmona from the latest Miocene to earliest Pliocene was on the whole warmer and almost as dry as today (present-day values: MAT: 18. 6°C; MTC: 9. 7°C; MTW: 28. 7°C; MAP: 521 mm; source: Climate data for cities worldwide) with the MLV of MAT around 20-21°C, of MTC around 11. 5°C, of MTW around 26°C, and of MAP around 550-650 mm (Table  S2 ). Two minima and two maxima in MAT are remarkable and provide a potential for robust climatostratigraphic correlations with the oxygen isotope curve from the Montemayor-1 hole  (Pérez-Asensio et al.
Acknowledgements The late  G. Clauzon  actively contributed to two field trips, particularly in evidencing the valley fill including the Nested Sandstone Formation. E. J. Mayoral  guided us on the field. P. Guenet  handled the coring process. The late  F. Gautier  contributed to the coring campaign, especially in paleomagnetic logging and measurements in Grenoble. J. Pocachard  and T. Thomas  ( LETI-CEA, Grenoble ) contributed in providing the paleomagnetism logging material used in situ and access to the measurement bed in Grenoble. Some additional information on foraminifera and paleomagnetism were provided by  F. J. Sierro  and  W. Krijgsman , respectively. The coring campaign was financially supported by  CNRS-INSU, Total  and  Elf-Aquitaine  companies. We particularly appreciated the comments and suggestions addressed by the two referees,  P.
Remerciements Notre équipe souhaite remercier  Sebastiaan Knippenberg  pour son travail pionnier et pour toutes les informations et échantillons qu'il a accepté de partager avec nous. Ce travail a été financé par le  Ministère de la Culture français (DAC Guadeloupe)  et  le Conseil régional de Guadeloupe. Cette étude a reçu  le soutien financier du gouvernement français dans le cadre du programme IdEx "Investissements d'avenir" de l'Université de Bordeaux / GPR "Passé Humain ". Nous remercions le TraceoLab de l ' Université de Liège  pour nous avoir permis d'utiliser leur équipement afin de réaliser les clichés microphotographiques.