dataesr/scientific-paragraphs-categorization huggingface dataset:
An incomplete uncertainty assessment for model results largely nullifies the utility of the model predictions in the context of understanding the actual physical system under consideration  (Tarasov and Goldstein, 2021). In this study, we provide an overview of a data-qualitycurated Antarctic constraint database intended to characterize the past evolution of the AIS and to evaluate and calibrate ice sheet models. Key features are a quality classification and careful specification of data uncertainties. The variety of data types is presented along with spatial and temporal information. A general overview is provided that discusses the datasystem relationship and observational uncertainties. In addition, we discuss the future inclusion of additional data types, such as the age structure of the ice, and highlight outstanding issues and community challenges.
AntICEconstraints The updated community Antarctic Ice Sheet Evolution observational constraint database version 2 (henceforth referred to as AntICE2) builds on the initial work of  Briggs and Tarasov (2013)  by integrating additional data since the original publication, including new data types. The updated database comprises observations of (1) past grounded-ice and ice shelf extent (paleoEXT), (2) past ice sheet thickness (paleoH), (3) past relative sea level (paleoRSL), (4) borehole temperature profiles (boreTemp), and (5) Global Positioning System (GPS) observations of PD uplift rates (rdotGPS). Figure  1  shows a summary of the data types in the AntICE2 database and their spatial coverage. In addition to these observations, the PD ice sheet geometry (surface elevation, ice thickness, and basal topography; https://nsidc.
org/data/ nsidc-0756/versions/3, last access: 6 July 2023) and surface ice velocities (https://nsidc. org/data/nsidc-0484/versions/2, last access: 6 July 2023) are considered. This major revision of the AntICE database more than quintuples the direct observational constraints from 203 to 1023 (excluding the PD AIS geometry and surface velocity field). The database is open source (https://theghub. org/resources/4884, last access: 6 July 2023) and available in the Supplement. The curation of data within the AntICE2 database was based on design criteria that excluded low-quality, inconsistent, and superfluous data. If the inference of past ice sheet changes is not increased when a data point is considered, then it is excluded to prevent database bloating. The curation criteria were established by the collective authorship of this study.
To calibrate or history match a model  (Tarasov and Goldstein, 2021) , it is necessary to compare model simulations to observations. For such comparison to have meaning, it logically follows that the relationship between each data point and the actual physical system must be specified. The selection of data with a high ratio of signal (measured quantity) to data uncertainty can strongly facilitate the inference process. To calculate a data-model misfit score for a given observation, the observation must include location data (latitude, longitude) and age data determined with a wellestablished dating technique, and it must quantify the relationship between the proxy observation and the characteristic (i. e. the recorded change in the ice sheet) it constrains. There are many sophisticated approaches to perform a meaningful data-model comparison  (Tarasov and Goldstein, 2021) , and there are tools that can assist those wanting an initial, albeit limited, data-model comparison implementation (e.
g. Ely et al. , 2019). For example, past ice thickness inferred from the elevation of an erratic boulder with an age determined by 10 Be cosmogenic-nuclide exposure dating constrains the time when ice sheet thinning caused the ice surface to fall below the altitude of the sample. The paleo-data are categorized by site, where data from nearby samples (typically within < 10 km distance) are clustered together, thereby yielding a time series at a given site (paleoRSL, paleoH). The exact spatial coordinates of the data are taken from the source publication and transcribed into the database. The sites of the paleodata in Figs. 1  and 2  show the average location of all the data clusters near a given site. Each site has a unique four-digit identifier (Fig. 2 ). The first digit represents the data type (paleoH = 1, pale-oEXT = 2, paleoRSL = 9, boreTemp = 5, and rdotGPS = 8), the second digit designates the drainage basin sector (Dronning Maud Land-Enderby Land = 1, Lambert-Amery = 2, Wilkes-Victoria Land = 3, Ross Sea = 4, Amundsen Sea and Bellingshausen Sea = 5, Antarctic Peninsula = 6, and Weddell Sea = 7; sector boundaries are shown in Fig.
2 ), and the last two digits identify the site within each sector (westernmost site = 1, increasing by 1 eastward following the coast). The types of paleo-data along with full references are found Figure  1. Antarctic Ice Sheet Evolution database version 2 (AntICE2) summary plot. The Antarctic basemap was generated using Quantarctica  (Matsuoka et al. , 2021). in Tables S1-S5 in the Supplement. The method by which the data is processed and interpreted is described below.
g. Balco et al. , 2016; Small et al. , 2019). When many samples along a transect across a topographical slope are analysed, one can reconstruct a chronology of paleo-icesheet thinning since the last ice thickness maximum in the region  (Stone et al. , 2003; Ackert et al. , 2007). This is illustrated in Fig. 3 , showing sample elevation histories from different sites during the deglaciation following the Last Glacial Maximum (LGM: ca. 19-23 ka) and in Figs. S1-S102 in the Supplement, showing the entire AntICE2 paleoH dataset. Cosmogenic-nuclide exposure dating on bedrock and erratics is the primary method used to establish the timing of deglaciation of terrestrial sites  (Bentley et al. , 2006 ; John- The basemap shown in (a) was generated using Quantarctica  (Matsuoka et al. , 2021). The surface elevation shown in (b-f) is based on the BedMachine Antarctica version 2 dataset  (Morlighem et al.
, 1999; Stone et al. , 2003; Bentley et al. , 2006; Mackintosh et al. , 2007; Balco et al. , 2016; Johnson et al. , 2017). An informal cosmogenic-nuclide exposure age database (ICE-D) already exists and facilitates accessibility to raw data and derived exposure ages. The ICE-D database (https: //www. ice-d. org/, last access: 6 July 2023) is inclusive and illustrates the conflicting and complex exposure histories in many regions. Quality control and processing of the data are required since many samples suffer from inheritance, and some regions provide an inconsistent record of past ice surface lowering (younger samples being higher than older samples). The deglaciation age is often inferred by the highest and youngest erratic sample  (Bentley et al. , 2006) , with older bedrock samples at a similar elevation being discounted.
Alternatively, a mean age of several samples for a site may be calculated  (Todd et al. , 2010). In the original AntICE database  (Briggs and Tarasov, 2013) , the exposure ages and uncertainties were taken directly from the literature rather than recalibrating the ages for overall consistency, in part because the raw data were often inaccessible. The ICE-D database addresses this issue by using a single, up-to-date method to calculate all cosmogenic-nuclide exposure ages. Exposure ages used in this compilation were calculated using the LSDn scaling method of  Lifton et al. (2014) , as implemented in version 3 of the online exposure age calculator described by  Balco et al. (2008)  and subsequently updated by  Balco (2020). Production rate calibration for 3 He in pyroxene and olivine, 10 Be in quartz, and 26 Al in quartz uses the primary calibration datasets of  Borchers et al.
(2016). Production rate calibration for in situ 14 C is based on measurements of the CRONUS-A quartz standard and the assumption that the concentration in this sample is at production-decay saturation, as described in  Nichols et al. (2019). An altitude uncertainty value of ±10 m is imposed when source publications do not include elevation uncertainty estimates. Whenever information on uncertainties is lacking in the source publication, uncertainty estimates are judged conservatively by relevant expert members of the author team or are derived from other studies using the same data type. The past ice thickness site IDs and locations are shown in Fig. 2  and visualized on a site-by-site basis in Figs. S1-S102 in the Supplement. Samples dated using in-situ-produced radiocarbon were previously not incorporated in the paleo-AIS thickness database.
, 2016; Hein et al. , 2016; Bentley et al. , 2010 Bentley et al. , , 2017;; Johnson et al. , 2019; Nichols et al. , 2019) ; this largely reconciles contradictory reconstructions of the regional post-LGM glacial history based on marine and terrestrial records  (Hillenbrand et al. , 2014). New exposure data from the Transantarctic Mountains along the Ross Sea embayment tell a more complete, albeit only local, post-LGM ice-sheet-thinning history for the mountain chain. During the LGM, the surfaces of outlet glaciers presently draining directly into the Ross Sea reached an elevation of 260 to 550 m above today (e. g. Jones et al. , 2015; Balco et al. , 2019). Of the other outlet glaciers feeding the LGM Ross Ice Shelf and Sheet system, several had an elevation of ∼ 1000 m above today during the LGM  (Spector et al.
, 2017). Paleo-ice-thickness data adjacent to the Siple Coast and Ross Island, as originally compiled in the AntICE database, showed that the ice sheet surface elevation at the onset of the post-LGM deglaciation ranged from ∼ 1000 to 2000 m above present. This illustrates the regional variability along the Transantarctic Mountains, with greater potential LGM elevation changes recorded further south and with significant variance among the sites likely being related to local topographical features of specific valleys  (Stone et al. , 2003; Todd et al. , 2010; Storey et al. , 2010). The Amundsen Sea drainage sector in West Antarctica has limited outcrops suitable for exposure dating; therefore, the region's past ice thickness is poorly constrained. The original database had a total of five data points constraining the elevation of the ice sheet surface at the LGM and at the start of the Holocene (11.
7 ka) in the hinterland of the Amundsen Sea embayment to be between 45 and 300 m above present  (Ackert et al. , 1999; Johnson et al. , 2008). New cosmogenic exposure ages, totalling 25 quality exposure ages, suggest a pre-Holocene ice elevation upwards of at least 330-560 m above present  (Johnson et al. , 2017 (Johnson et al. , , 2020)). In the original version of the AntICE database, the Antarctic Peninsula lacked any paleo-ice-thickness data. Three new histories are included in our new iteration, and they all consistently report an ice elevation of ∼ 350 m above present early in the last deglaciation  (Johnson et al. , 2019; Bentley et al. , 2011; Balco and Schaefer, 2013; Glasser et al. , 2014). Finally, a new thinning history from the Sør Rondane Mountains in Dronning Maud Land proposes an ice surface lowering of less than 50 m during the last deglaciation  (Suganuma et al.
, 2014). In our study here, we also include previously unpublished exposure data constraining AIS thinning since the last glacial period (Figs. S41, S44, S47, S63 in the Supplement). This includes some newer data of high quality (e. g. 1419, 1422, 1425, and 1506) that are not yet published in peer-reviewed articles but are included in the ICE-D database due to publicaccess requirements of funding agencies.
For this reason, only those marine sediment records that clearly document a position below grounded ice, under an ice shelf, or in (seasonal) open water at a particular time are added to the An-tICE2 database (Figs. 4 and S104-S180 in the Supplement). The paleoEXT database was originally a curated version of the GL retreat ages compiled by  Livingstone et al. (2012). Our new iteration has been updated to include the RAISED consortium compilation  (Bentley et al. , 2014; Hillenbrand et al. , 2014; Anderson et al. , 2014; Mackintosh et al. , 2014; Ó Cofaigh et al. , 2014; Larter et al. , 2014) , and it has also been supplemented by a number of more recent studies  (Bart et al. , 2018). For each marine sediment core, obvious 14 C age outliers or down-core age reversals, if present, were removed in accordance with the source literature.
Converting measured radiocarbon activities to calendar age requires corrections for the variable atmospheric radiocarbon history and for the reservoir age of the ocean. All the past ice extent ages were recalibrated using a consistent marine reservoir correction of 1144 ± 120 yr  (Hall et al. , 2010) with CALIB v8. 1 (CALIB rev. 8; Stuiver and Reimer, 1993)  using the Marine20 calibration curve  (Heaton et al. , 2020). The full information for the marine cores, including expedition ID, sample depth, etc. , is given in the ICE-D marine database (http://marine. antarctica. ice-d. org/, last access: 21 December 2023). Since the original AntICE database, numerous cruises have collected marine sediment cores along transects from near the modern ice shelf front to the continental-shelf edge. The biggest addition of data occurred in the Amundsen Sea sector, where LGM grounded-ice extent and deglacial GL retreat have been reconstructed across Pine Island-Thwaites Trough  (Smith et al.
When uncertainties were provided in the source publications, they were incorporated in the database. When they were lacking, a ±1 m elevation uncertainty was assumed. Moreover, as in  Briggs and Tarasov (2013) , another ±1 m uncertainty is added to allow for present and paleo tidal variations  (Sun et al. , 2022)  when measured uncertainties are less than 2 m. The radiocarbon ages in the database were recalibrated using the CALIB v8. 1 with the IntCal20 (SHCal20), Marine20, or the mixed marine Southern Hemisphere radiocarbon calibration curve depending on the sample type and content of marine material  (Reimer et al. , 2009; Heaton et al. , 2020). The source publications use different marine reservoir corrections depending on the dated material, while our database standardizes the marine reservoir correction to 1144 ± 120 yr  (Hall et al.
, 2010)  for simplicity and consistency. By providing the uncorrected 14 C ages, uncertainties, and marine reservoir corrections, the relative-sealevel dataset can easily be recalibrated. Moreover, this enables the data to be incorporated within an online database (e. g. ICE-D RSL repository) so that ages can be dynamically recalibrated upon request. The additional RSL data in the AntICE2 database have significantly increased the geographic coverage when compared to the original iteration  (Simms et al. , 2011; Simkins et al. , 2013; Hodgson et al. , 2016; Verleyen et al. , 2017). In Dronning Maud Land, new isolation basin data from Lützow-Holm Bay more robustly constrain past RSL, which is estimated to have fallen by 20 m over the Holocene  (Verleyen et al. , 2017). These sea level index points are generally consistent with the previously published limiting dates  (Miura et al.
Ice core borehole temperatures The original AntICE database lacked constraints in the interior of the ice sheet. To partly remedy this major issue, we incorporate a new powerful data type -the temperature profiles of major Antarctic ice core boreholes (Fig. 6 ). The temperature structure of the ice can be measured by running a temperature logger down the borehole of an ice core  (Cuffey et al. , 2016). Past changes in temperature, ice velocity, and ice thickness will affect the thermal structure of the ice sheet. Therefore, the resulting observations of temperatures through the ice constrain the present and past thermal forcing and ice dynamics. Borehole temperature profiles generally have one of two structures characterized by the depth of the englacial thermal minimum. In the first case, a borehole temperature profile is characterized by minimum temperatures near the ice surface which progressively increase towards the bed  (Engelhardt, 2004a, c; Motoyama, 2007; Lukin and Vasiliev, 2014; Weikusat et al.
The various borehole temperature profiles were measured using different instruments, and some were measured at a time considerably after the ice core had been drilled  (Motoyama, 2007; Lukin and Vasiliev, 2014). With the precision of the used temperature logger rarely reported in a source publication, an uncertainty value of ±0. 1 • C is assumed. The Talos Dome and South Pole borehole temperature profiles do not cover the majority of the entire ice column, minimizing their overall constraint effectiveness capability. Several borehole temperature profiles have been measured along the Siple Coast in the Ross Sea sector  (Engelhardt, 2004a). Although they all share a high degree of correlation, a total of four Siple Coast boreholes were included in the database to maximize both the spatial distribution and the number of prominent ice sheet features sampled.
Present-day uplift vertical land motion Across Antarctica, a Global Positioning System (GPS) network measures the displacement of the solid Earth. GPS measurements, although relatively scarce, can supplement the even-sparser RSL dataset in constraining the isostatic response of the solid Earth to past and present changes in surface AIS load. The vertical deformation rates derived from GPS measurements represent the integrated signal of several processes operating on various timescales. The two primary contributing factors to vertical land motion are the remaining slow viscous response to past ice and water load changes and the near-instantaneous elastic response due to contemporary ice load changes  (Martín-Español et al. , 2016; Sasgen et al. , 2017). GPS observations have previously been implemented to evaluate Antarctic GIA and ice sheet models  (Argus et al.
, 2014; Gomez et al. , 2018; Whitehouse et al. , 2012b; Ivins et al. , 2013). The resulting Antarctic GIA estimates are used in conjunction with satellite-derived remote gravimetry or altimetry data to infer contemporary mass balance changes of the AIS  (Shepherd et al. , 2018). GPS deformation rates first have to be corrected for the elastic response to contemporary ice mass change before they can be inferred to reflect the background viscous response to past ice mass change  (Martín-Español et al. , 2016; Sasgen et al. , 2017). For our database, a key criterion for GPS data evaluation is the constraint value for Antarctic GIA. We therefore divide the dataset into stations that are not influenced significantly by modern ice mass change and stations with a significant contemporary elastic signal (for details, see the discussion section below).
A total of 67 GPS stations constrain the isostatic adjustment of the land bedrock for the period 2009 to 2014, with a total of 15 GPS stations being assigned to a high-quality tier (as discussed in Sect. 3. 1. 5). Alongside the GPS uplift rates, we provide the elasticresponse-corrected vertical rates from  Martín-Español et al. (2016). Both the GPS and elastic-corrected datasets come associated with their own explicit and implicit uncertainties. In compiling the AntICE2 GPS dataset, we selected sites that are hardly impacted by contemporary mass balance changes (negligible elastic signal). The accuracy of the elastic-corrected high-quality subset of GPS data is dependent on the validity of the inferred contemporary ice load changes and the resulting elastic signal.
Data uncertainty structure The uncertainties in the database are explicitly stated as 1σ and 2σ intervals. Some of the observational data in the database exhibit two-way or one-way bounds. It follows that some proxy data and their uncertainties represent just an upper-or lower-bound constraint (one-way bounds). Twoway Gaussian uncertainties are affiliated with the PD obser-   (Morlighem et al. , 2020)  and (ef) MEaSUREs ice surface velocity over 2005-2017 and its associated uncertainties  (Mouginot et al. , 2019). vations (ice sheet geometry and surface velocities), the GPS observations, and borehole temperature measurements. The paleoH and paleoExt data are also represented by two-way symmetric uncertainties around the mean. Some of the pa-leoExt data constrain exclusively the onset of open-marine conditions, rendering them a one-way constraint.
There are several one-way constraints in the RSL database as well, such as those that are limiting minimum or maximum RSL inferences (molluscs, penguin remains). The details are specified in the database itself and were previously discussed in greater detail in  Briggs and Tarasov (2013). These observations are converted to nominal two-way non-symmetric constraints by assigning an exceedingly small or large uncertainty bound to the unspecified region of the probability distribution. This adoption of a Gaussian observational error model facilitates ice history scoring. However, the validity of a Gaussian error model for all the types of data in our database awaits future testing.
Discussion The implementation of a database for geophysical model calibration has a number of requirements to ensure utility. In large part this boils down to clear specification of the relationship between the data and the real-world system under consideration. As such, the database curation process and all data uncertainties need to be clearly specified. From the perspective of modellers, a careful evaluation of internal and ex- ternal model limitations is necessary to produce meaningful data-model comparisons. In this discussion, some considerations are explicitly stated when it comes to the aforementioned challenges. For much of the last glacial cycle, there are very few to no paleo-observations that directly constrain the configuration of the ice sheet (Fig. 8 ). The paleo-data (paleoEXT, paleoH, paleoRSL) have a mean age of 9.
In the case of cosmogenic-nuclide data, exposure ages that clearly suffer from inheritance are not assigned to a quality tier. Moreover, some data are excluded from tier assignment based on physical impossibility -e. g. exposure age data require that younger ages must be at a lower elevation than older ages for a given site. In the following subsections, we describe the tier assignment process which involves evaluating the datasets with respect to strict criteria that assign the datasets to their respective tiers. Further refinements are conducted based on expert assessment and outlier identification. This is performed on a data type basis with the aim of minimizing subjectivity in quality assessment and data selection. There are a few criteria which are consistent across data types: prioritizing data with a high signal-to-noise ratio beyond a chosen data type threshold; valuing data in datasparse regions; outlier identification and exclusion when substantiated by broadly consistent, dense data clusters (cluster density assessments are data type dependent); and superfluous data exclusion.
PaleoH data curation The past ice thickness dataset requires additional considerations when assessing data tiers. Some paleoH sites have few exposure ages that constrain the elevation history. In these instances, we rely on the discretion of the original study to assess the quality of the data point which is available through ICE-D. At a given site, an assessment is conducted that identifies the highest-quality exposure ages (e. g. 10 Be, 26 Al, 3 He, and 14 C) bracketing the elevation history and sorts the data into tiers (Figs. S1-S102 in the Supplement). A high data density cluster of young exposure ages that form the expertassessed 2σ bounds on elevation history is identified (Fig. 3 ). This assessment considers the occurrence of inheritance and post-deglacial shielding. By evaluating paired isotope exposure ages and applying first principles (along a sample transect, older ages should always be obtained from samples collected at higher altitudes), many data points can be excluded, and an expert-assessed 2σ elevation history based on data density can be identified.