dataesr/scientific-paragraphs-categorization huggingface dataset:
It can also be stated that the Rb-Sr data were not biased by the preparation or by any analytical aspect, but correspond to a mineral phase that was not removed during the sample preparation. All obtained Rb-Sr and K-Ar data were combined into a chart that allows a straight comparison of the results (Figure  5 ). Relative to the stratigraphic reference, most age data are on the higher side by both the Rb-Sr and K-Ar methods. In detail, the untreated aliquot of sample L8 and all those of sample L10 yield higher age values than the stratigraphic reference. It is confirmed that the high age value of the untreated L10 aliquot is mostly due to the identified blödite with an impact on only the Rb-Sr system. However, for that L10 sample, the data plots of the leached/shaken aliquots remain unevenly above the stratigraphic reference, which suggests that they also probably contain insoluble detrital components such as illite-type clay crystals that are clearly of detrital origin.
Conclusions Authigenic glauconite pellets represent important historic references for the calibration of the time scale by K-Ar and/or Rb-Sr isotopic dating. As illite separates, they were often treated with different reagents such as dilute HCl and/or HAc, but seldom together with ultrasonic shaking. The same procedure has been strictly applied again here for a comparison of K-Ar dating of three samples previously dated by Rb-Sr in order to precisely compare the two sets of data. Two opposite K-Ar data trends for the elemental contents suggest various mineral compositions in the untreated pellets. The K-Ar data obtained before and after the treatments are analytically similar for all aliquots of two of the analyzed samples, while those of the third one yield a slightly higher age difference.
In fact, this sample also contains blödite identified by XRD, known as Sr enriched and for its marine origin, that affected the Rb-Sr data and not the equivalent K-Ar data. Based on its 87 Sr/ 86 Sr ratio above that of the depositional marine environment, this mineral appears to have crystallized during a late diagenetic episode relative to the glauconite precipitation. The chemical analyses show that the sample preparation has a determining impact on the isotopic dating of clay-type minerals. This technical aspect becomes especially important when glauconite isotopic dating is of stratigraphic application, by removing, or not, accessory minerals trapped in the pellets during initial crystallization or delayed diagenesis. It is essential to systematically leach the selected aliquots with dilute acid, run and, preferentially, compare the Rb-Sr and K-Ar data for a consolidated evaluation of the obtained ages.
Acknowledgments: We would like to dedicate cordial thanks to the late Emeritus  Professor Dr. Paul Pasteels  for having allowed us to complete the  Rb-Sr study of three glauconite separates by chemical  and  K-Ar  analyses that became the basis of the present study. Two of us, who knew him for years, would also like to dedicate this, as a token of appreciation, to a very gentle colleague and a highly-respected scientist. Two anonymous reviewers are also sincerely thanked for their remarks, corrections and suggestions that were very useful to improve this presentation. The editorial team is also thanked for efficiency and constant availability during the final proofing round.
The camera is driven at up to 3 frames per second. The MPO has a large depth of field, (~1cm) allowing rough unprepared surfaces to be imaged with minimal loss of resolution. The CCD is exposed and read rapidly (several frames per second) such that each pixel records Figure  2  shows a partial MapX dataset collected from a quartz sandstone decorated with hematite crystals (similar to magnetite crystals observed on the surface of mudstones by the MSL Curiosity rover). The instrument nominally collects ~10,000 0. 3 second images into an HDF5 data cube. These data cubes can be as large as a Gigabyte, so they must be processed on-board. An unsupervised machine learning algorithm resident on the instrument computer produces ROI comprised of common elemental compositions.
image of basalt breccia, scale bar = 1 cm (note: the image shown is the obverse of the side used for elemental imaging); b). RGB elemental image from EDAX commercial instrument, Fe=Red, Ca=Green, Mg=Blue; c). RGB elemental image from MapX-III prototype, same color scheme as b); d). instrument selected ROI, B = basalt breccia, C = Carbonate, MC = magnesian carbonate; e). XRF spectra from ROI. Quantifiable XRF spectra are generated from each ROI, which are analyzed offline using a fundamental parameters technique. ROI compositions are used in conjunction with the RRUFF database [4] to determine putative mineralogy. Figure  3  shows a partial MapX-III dataset collected from a brecciated basalt infilled with hydrothermal carbonate cements from Spitsbergen, Norway (analogous to Comanche Carbonate reported from MER  [5, 6] ).
Earth system science Altimetry was first used for open ocean observations. With time, interest in altimetric data expanded to other areas of science. The so-called ''New frontiers of altimetry" (https://tinyurl. com/NewFrontiersAltimetry) today encompass coastal oceanography, the cryosphere, inland water hydrology, and climate -all beyond the initial and traditional mission objectives of open ocean investigations. As climate underlies and motivates many scientific studies in the field of Earth Sciences, more and more attention is being devoted to studying Essential Climate Variables (ECVs). The surface ocean ECVs, as identified by the Global Climate Observing System (GCOS), are listed in Annex A. Altimetry underpins five of them: Sea level, Sea state, Sea ice, Ice sheets, Surface current as well as Lake and River level changes.
, 2019). Observing the small scales is also crucial for understanding the ocean's energy budget, and the exchange of energy occurring between the large scale circulation, the mesoscales, down Fig. 3. Comparison of the standard deviation (in cm) of the sea surface height derived from the simulation of an ocean general circulation model (upper panel) and altimeter data (lower panel). The model was developed by a group at the MIT with eddy-permitting horizontal resolution of 18 km. The simulation was performed for the period of 1992-2006. The altimeter data were taken from the same period of time. See  Fu (2009). to the smallest scales of ocean mixing and dissipation for the global ocean. Satellite altimetry does not provide a direct observation of these dissipative scales, but can observe regions of rapid change in energy where mixing may be important and, therefore these are ideal target sites for intense studies and parameterization development.
Indeed, the level of the ocean can also change because the underlying land is rising or falling with respect to the ocean surface. Such relative sea level change usually affects a local or regional area, and in numerous cases, is actually outpacing the rate of sea level change. The primary requirements that are advocated for sea level science are: -A long and accurate global and regional sea level record for climate studies which implies sustained altimetry missions, and continuing Research and Development (R&D) activities to feed operational production of sea level (e. g. , the Copernicus Marine and Climate Change Services in Europe, https://www. copernicus. eu/en). Extending the ocean altimetry data record beyond 4 decades should be an objective. A key aspect is to maintain a high accuracy reference mission following Jason-3 and Sentinel-6; -The continuity of the measurements is essential, not only the continuity of altimetry but also of the other observing systems that accompany altimetry (Argo, deep Argo, GRACE, etc.
) and all geodetic data (DORIS, SLR or GNSS data) needed to improve the orbit of altimeter satellites and the geophysical corrections applied to altimeter measurements; -The regular assessments of closure of the sea level budget at global and regional scales. Uncertainty estimates on regional sea level and on regional sea level trends are still lacking to improve this closure. It includes the uncertainties that are estimated through comparison with other observing systems such as tide gauges  (Watson et al. , 2019). Improving the comparison between tide gauges and altimetry likely requires equipping more tide gauges with GNSS positioning, in order to achieve higher accuracy, and to improve the relative sea level trends estimations; -It is also essential to improve the sea level record at the coast using different techniques to study coastal impacts (see also Section 2.
, 2019; Dieng et al. , 2019)  and significant wave height  (Schlembach et al. , 2020) , which have brought the validity of dedicated altimetry dataset to the last 0-5 km to the coast, compared to the traditional limit of 30 km of proximity. It is necessary to understand how the spatial and temporal variabilities (i. e. , annual, interannual and decadal) near the coast are linked to the measured off shore change and how small-scale dynamics impact measured variability near the coast  (Woodworth et al. , 2019; Ponte et al. , 2019). A global multi-mission coastal altimetry data set, with vertical land motion, is needed (requiring reference against tide gauges with GNSS and InSAR). A high-resolution geoid, Mean Sea Surface and other range correction fields are similarly required  (Kumar et al.
Several processors and databases have been developed in the past 20 years. At the beginning of the 21st century three systems (in France, U. K. and USA) have emerged. These systems are ''River&Lake"  (Berry and Wheeler, 2009) ,  ''Hydroweb" (Cretaux et al. , 2011)  and ''USDA Lake database"  (Birkett et al. , 2011). Subsequent to these three pioneers another has been launched in Germany at the Technical University of Munich: ''DAHITI"  (Schwatke et al. , 2015). Most recently, the ESA CCI ''Lakes" ECV (http:// cci. esa. int/lakes, (Woolway et al. , 2020)  as well as other R&D initiatives, available online have been started. A further step forward has also been achieved by integrating altimetry products in the Water and Cryosphere component of the Copernicus Global Land Operational Service. Lake and River Water Levels are operationally produced on several thousands of targets spread worldwide, using the advanced capabilities of onboard tracking offered by Jason-3 and Sentinel-3A&B missions (https://land.
copernicus. eu/global/products/wl). Freely available radar altimetry data through these databases has led to an increasing number of studies focusing on the monitoring of lake and reservoir water level (e. g. Cretaux et al. , 2011; Okeowo et al. , 2017; Thakur et al. , 2020) , storage (e. g. Gao et al. , 2012; Van Den Hoek et al. , 2019; Zhou et al. , 2015) , outflow (e. g. Getirana et al. , 2020; Hossain et al. , 2014)  and bathymetry (e. g. Zhou et al. , 2015; Getirana et al. , 2018; Li et al. , 2020). Beyond supplying inland water level and volume variation, the current challenge is to derive river discharge, which is not directly observable from space independently from in situ data. Several studies have proposed algorithms merging radar altimetry with in situ observations  (Thakur et al.
, 2012; Melo, 2019). Even though the hydrology remote sensing community makes a broad use of radar altimetry data, one of the main challenges still remains to convince hydrologists, a community with a long history, that satellite altimetry, notably through dedicated products in the above databases, can be usefully applied to their work, particularly in operational hydrological monitoring systems. This requires an understanding of the main challenges that can be tackled with satellite altimetry (for example understanding long term evolution of lake or river level linked to climate change and/or water resources uses). Data access is a big part of encouraging hydrologists to use more remote sensing, including altimetry. Although data are easily accessible, it is difficult for hydrologists who are not remote sensing experts to access the data as they become available in quasi near-real-time (NRT) from the providing agencies.
, 2006). The continued monitoring of the polar regions in the coming decade is to be addressed. As a baseline, the recommendation is to continue our current capabilities to measure and monitor variability of Arctic and Southern Ocean sea-ice thickness, which requires satellites that provide complete polar coverage to at least 88°inclination. Exploiting existing altimetry datasets, with new advanced techniques such as SARIn-based ''swath mode" processing  (Gourmelen et al. , 2018) , and fully focused SAR  (Egido and Smith, 2017) , should be prioritized, to increase the spatial resolution of altimetry products to beyond the boundaries of the original mission design. Knowledge of snow loading (snow depth and density) has been a major challenge in the accurate retrieval of sea ice thickness and ice sheet elevation changes  (Tilling et al.
Biological data like animal telemetry, fish catches, or acoustic densities demand ideally kilometric resolution, which is not achieved by altimetry observations available today. This is especially relevant for Lagrangian features derived from altimetry, which capture transport properties and seem to play a key role for the structuring of marine biota. Higher spatial resolution is needed as well to go beyond the ''large mesoscale" and observe SSH features smaller than 70-100 km resolution achieved today; -Higher temporal resolution. An enhancement in spatial resolution should be also accompanied by an enhancement in temporal resolution, because smaller features in general evolve faster than larger ones. This means that the development of one higher resolving altimeter such as SWOT is not enough, but ideally should be accompanied by a larger altimetry constellation, including swath and conventional altimetry, and/or the use of an integrated altimeter and a Doppler Wave and Current Scatterometer such as SKIM.
9 ): secured availability of constellation data in NRT, optimal sampling between missions; -Multi-sensor, satellite/in situ combination: consistency and combination require dedicated pre-processing before or during data assimilation; -In situ component has to be sustained and improved in the long term. Maintenance of complementary observing systems such as Argo  (Roemmich et al. , 2009)  and higher density in situ observations  (Rudnick, 2016)  is critical. Essential complementary information with respect to space data; and -Higher resolution wind and wave data (altimeters, SAR, CFOSAT) for coupled ocean/wave models are needed.
, 2016). This instrumental feature provides data in both the historical low-resolution mode (LRM) and a major evolution of the SAR-mode of Sentinel-3. This development was a response to new climate science requirements. The Sentinel-6 mission (Sentinel-6A/Michael Freilich and Sentinel-6B) will be able to ensure a seamless continuity with previous generations of climate reference altimeters (TOPEX/Poseidon and Jason-1 to 3) while providing, at the same time, high-resolution noise-reduced datasets in the open ocean and other SARM features (e. g. Fully-Focused SAR) such as in coastal margins and for inland water hydrology, for more than a decade. In addition to science-driven technology changes, various instrumental changes were actually technology-driven demonstrators (i. e. related to science research topics, but not necessarily driven by scientific requirements).
For future missions, one might expect technology to keep evolving as scientific requirements become ever more stringent: oceanographers try to observe smaller and faster mesoscale features (better sampling is needed), as well as SSH derivatives (smaller instrument noise is needed), and most importantly extremely small trends in the Earth Climate system (smaller biases and drifts are needed). This might become even more challenging because environmental conditions (e. g. potential corruption of actual microwave radiometry data with 5G networks) or space laws (e. g. deorbiting is substantially more difficult from the Sentinel-6 altitude than from lower altitudes). To that extent, it is essential that space agencies ensure not just continuity of existing datasets and performance, but also more precise instruments, higher space and time resolution and sampling, as well as more stable instruments and reprocessed calibrated products for climate science.
SSH data SSH is the first obvious product from altimetry. Again, the key recommendation concerns the requirements of the continuity of observations over time and that data be provided in real-time. Continuity is an important first criterion that is mentioned by scientists of all disciplines. Also, the time scales of the phenomena involved are important, and those with a long-time scale are key. The measurement of sea level is a good example of this. There are several recommendations regarding the resolution, in time and space. But not surprisingly, they are not necessarily consistent depending on the applications and scientific fields. Regarding swath altimetry, so far there is only the SWOT mission programmed. This is a limited edition wide-swath mission, and there is no continuity assured for the HR coverage after 2024, yet many research and operational users will make use of these data and systems will be put in place to exploit the high-resolution SWOT observations.
SWH and wind speed data Wave height is another important piece of information provided by altimetry. High-resolution observations of wind and waves for coupling the ocean and wave models are crucial for operational oceanography. Measuring wind and waves and the ''weather" of the ocean is one way to increase collaboration between different science teams and the meteorology community. Additional effort is required to secure an accurate wave height measurement as close to the coast as possible, and also to develop instruments permitting access to other important variables like wave direction, spectra, etc. (e. g. CFOSAT). This relates to coastal oceanography and also to sea level monitoring requirements as the evolution of the near shore wave field may contribute to sea level changes near the coast.
River and lake level Monitoring water levels of lakes and rivers worldwide has been made possible using satellite altimetry. Hydrologists, some of them being not expert in altimetry, and, consequently, that are unable to calculate these variables for their region of study, can now find them in different databases such as ''River&Lake", ''Hydroweb", ''USDA Lake database", ''SAC-VEDAS database" and ''DAHITI" already mentioned. All the data produced for these systems is provided at no additional cost to the user, and is easily accessible. Part of the data in these databases is updated in near real time using the IGDRs altimetry products. In Hydroweb and DAHITI moreover, water extent and water volume changes are produced for dozens of lakes, using synergy between altimetry and imagery. If combined with other available water extent time series data (e.
g. Pekel et al. , 2016; Zhao and Gao, 2018; Yao et al. , 2019) , the existing altimetry level databases can potentially be used to estimate multi-decadal volume changes for at least hundreds of lakes and reservoirs worldwide. The next step is to satisfy the requirement for automated space-borne estimation of river discharge, using in synergy altimetry and optical sensors to increase the space-time sampling, and also consider river discharge as a candidate for being the next Essential Climate Variable from space  (Woolway et al. , 2020; Williamson et al. , 2009).
, 2011; Howe et al. , 2019). Wideswath altimetry has significantly unveiled the important internal tide signal. A recommendation is to develop an internal tide model for use by the community (e. g. , in CMEMS processing, in Radar Altimetry Database System (RADS), etc. ). It is necessary to better understand, estimate and model internal tide variability, its nonstationary element, and learn how to handle the highfrequency motions of internal tides and waves.
, 2019; Wilkinson et al. , 2019). In addition, SLR is the only tracking system that can provide absolute estimation of radial orbit accuracy. A stable reference frame is a fundamental requirement for altimetric satellite POD. New SLR systems providing better, and more frequent data have been deployed and are under development. The present realization, ITRF2014, used space geodetic data obtained over the last 30-40 years of the space age for its definition  (Altamimi et al. , 2016). Accurate and timely estimates of the Earth Rotation Parameters (e. g. , polar motion and UT1, see  Bizouard et al. (2019) ) are also important both for low-latency products (including near-real-time) and longer latency products (for Geophysical Data Records). The challenge in the future will be to continue to deliver sub-cm radially accurate orbits for altimetric satellites.
DUACS In 1997 the first homogeneous and user friendly Sea Level data set based on TOPEX/Poseidon and ERS-1 & 2 missions was released to the scientific community: from this date, it was no longer necessary to be an altimetry expert to use sea level time series. This was the beginning of DUACS (Data Unification and Altimeter Combination System). For 25 years  (Taburet et al. , 2019) , the system has integrated and merged all the altimeter missions into a multi-mission dataset Level 3 (along-track crosscalibrated SLA) and Level 4 products (multiple sensors merged as maps or time series). It was disseminated first on Aviso and now CMEMS (Europe) and used worldwide. Regional multi-mission datasets were also produced in NASA (US) and IMOS (Australia) showing some limits in the operational maps, notably in terms of spatial resolution.
Integration Models and data assimilation have appeared in all scientific domains whatever their developments, as key elements to extract all the benefits of observations and of altimetry in particular (for a recent review, see  Chassignet et al. , 2018). Data assimilation covers an ensemble of various mathematical techniques that provide a rational way to synthetize information provided by models and by observations (assuming estimations of their accuracy), in order to provide a realistic state estimation (and possibly forecast) with error estimates. These are key tools to use, analyze and understand current data, but also to anticipate the use of new observations. Modeling and OSSEs may be used to simulate these new observations, such as SWOT altimetry for example. There are key tools to give an integrated perspective of a system involving many observations of different types and some understanding translated in terms of dynamic equations.
Models and data assimilation provide adequate tools to make consistent use of multi-sensors, and multi-missions datasets. The status of model development is not similar in all disciplines. Efforts must be made to develop modeling for hydrology and the cryosphere. Such efforts must be made in the same consistent manner to improve the Earth observing system. For example, in order to understand upcoming observations from SWOT, what is needed is not only to do comprehensive CAL/VAL and to coordinate the ocean science campaigns, but also interpretation from highresolution modeling and data simulation. With SWOT the resolution is high, but the temporal resolution is poor, so observations might be uncorrelated, implying an immediate need for modeling. It is even true prior to the launches as model outputs can provide some good indications (''simulators") of the data that will be observed, in order to prepare the right approach for exploitation.