dataesr/scientific-paragraphs-categorization huggingface dataset:
Slopes from AirSWOT are -14 and -15 cm/km on July 8, 2017, and August 16 to 17, 2017, respectively. While the longitudinal profiles are similar, the absolute WSE differences between WaSP and AirSWOT are inconsistent (Figures  9B, D ). The median difference between AirSWOT and WaSP was -20 and -78 cm on July 7 to 8, 2017, and August 16 to 17, 2017, respectively. AirSWOT is biased (lower) during both surveys, with an inconsistent absolute bias as August mappings are nearly three times lower than July. Again, it is important to emphasize that, in order to meet accuracy requirements, AirSWOT and SWOT data require downstream spatial averaging. Because of lack of data density, such spatial averaging is not performed, and thus the accuracy of 2017 AirSWOT mappings in terms of SWOT mission requirements is not assessed.
All surveys were successfully executed, which demonstrates the ease of use of WaSP. Furthermore, WaSP deployments were conducted on foot, by pack-raft, canoe, motorized boat, helicopter, and float plane. This highlights the flexibility of the system given different logistical conditions. In total, 63 WaSP WSE surveys were executed, spanning ∼17 • latitude. Resultant GPS/GNSS data have been successfully processed using GINS PPP  (Marty et al. , 2011)  software, and WSEs (Table  3 ) are being used to validate AirSWOT ABoVE mappings  (Fayne et al. , 2020).
ACKNOWLEDGMENTS Equipment support and data services were provided by the GAGE Facility, operated by  UNAVCO, Inc. , with support from the  National Science Foundation  and the  National Aeronautics and Space Administration under NSF  Cooperative Agreement EAR-11261833. Processed AirSWOT radar data collected as part of 2017  NASA  ABoVE campaign are available at: https://doi. org/10. 3334/ORNLDAAC/1646 (Fayne et al. , 2019). AirSWOT color-infrared orthomosaics collected as part of 2017  NASA  ABoVE are available at: https://doi. org/10. 3334/ORNLDAAC/1643 (Kyzivat et al. , 2018). North Saskatchewan River water mask derived from AirSWOT color-infrared data collected as part of 2017  NASA  ABoVE are available at: https://doi. org/10. 3334/ORNLDAAC/1707 (Kyzivat et al. , 2019b). We thank  John Arvesen  of  Cirrus Digital Systems  for assistance with color-infrared digital imagery;  JPL AirSWOT  processing team for radar data processing;  Alex Shapiro of Alaska Land Exploration LLC  for logistical and helicopter support out of Fairbanks, AK,  USA ;  Jim Webster  for floatplane support out of Fairbanks, AK,  USA ;  Jerry Carrol  in Fort Yukon, AK,  USA  for boat transport on the Yukon River;  Heather Bartlett  and  Mark Bertram  of the  Yukon Flats National Wildlife Refuge  for field support;  Dave Olesen Hoarfrost River Huskies Ltd.
OBSERVATIONS AND DATA REDUCTION We have inspected the NICER  (Gendreau et al. 2016 ) light curves from 2020 by eye. Apparent Type I bursts are present in  OBSIDs 3200400106, 3200400111, 3200400114, 3200400121 and 3200400122, corresponding to March 6, 11, 14, 21 and 22. We begin with the calibrated, unfiltered events file from HEASARC (event_cl/ni32004001**_0mpu7_ufa. evt). We use the standard filters 1 to produce good time intervals (GTIs) apart from the undershoot range, which we relax from ≤ 200 s -1 to ≤ 300 s -1 for the first Type I burst and 1 For further information on the filters, see heasarc. gsfc. nasa. gov/lheasoft/ftools/headas/nimaketime. html ≤ 250 s -1 for the second. This is required due to high optical loading due to the relatively low Sun angle. Additionally, to include the peak of the second burst, we relax the offset from the nominal target direction slightly, using 0.
0155 • rather than 0. 015 •. This is a small change from the standard value, so data during this time are unlikely to show significant deviations from the standard calibration. Further, the fourth burst occurs during passage through the South Atlantic Anomaly (SAA) and the overshoot rate reaches close to 5 s -1 , so is removed by standard filtering. We remove these filters in order to show the light curve but note that the spectrum may be affected. We then use nicerclean to produce a clean events list, which we then barycentre to the ICRS reference frame and JPL-DE200 ephemeris. From this, we extract spectra and light curves using xselect. We use NuSTAR  (Harrison et al. 2013 ) OB-SID 90601308002, which overlaps with NICER OBSID 3200400106. We reduce this using the standard nupipeline and nuproducts software, version 1.
The mHz QPO has an average frequency of 9. 6 ± 0. 5 mHz and a fractional rms amplitude of 2. 2 ± 0. 2% (0. 5 -10 keV). There is no evidence of the oscillations in the ≈ 600 s of data after the X-ray burst, with a 90% upper limit on the rms amplitude of 1. 2% ruling out that the same strength of oscillation continues. We also found marginal evidence of QPOs in at least three other cases; however the datasets are relatively short ( 500 -700 s), and therefore it is not possible to understand if they are real or the product of red-noise. The upper limits to the QPO amplitude for the time segments prior to the earlier bursts are somewhat lower in fractional amplitude (0. 6, 0. 8, 1. 0 and 0. 8% in chronological order) than for the detected QPO but due to the brighter flux at earlier times, all but the last of these limits are higher in absolute amplitude than the detected QPO.
2007). This is the 7 th NS system that shows this type of QPOs. The fact that we do not detect more episodes of mHz QPOs could either be due to their intrinsic absence or to detection difficulty: the mHz QPOs are not always present in the X-ray light curves (they are state dependent and even in a given state, there is not yet a clear physical trigger for them, see  Altamirano et al. 2008; Mancuso et al. 2019, etc. ) ; in addition, the frequency and amplitude of these QPOs are very low, and therefore to acquire enough QPO cycles and sufficient signal-to-noise, uninterrupted datasets longer than 1000 -1500 s are generally needed to unambiguously detect them.
6-0814; the closer comparison of the flaring state of Swift J1858. 6-0814 with black hole than neutron star systems could be because the magnetic field of its neutron star is low enough to be unimportant in its accretion flow, implying a relatively low magnetic field strength. research has made use of data and software provided by the High Energy Astrophysics Science Archive Research Center (HEASARC), which is a service of the Astrophysics Science Division at NASA/GSFC and the High Energy Astrophysics Division of the Smithsonian Astrophysical Observatory. This research has made use of ISIS functions (ISISscripts) provided by ECAP/Remeis observatory and MIT (http://www. sternwarte. uni-erlangen. de/isis/). a Recurrence times are upper limits as bursts may have occurred between observations.
ACKNOWLEDGEMENTS We thank  Laurens Keek  for helpful discussions and the referee for comments which improved the manuscript. We thank the  NuSTAR  operations team for rapid approval and execution of our Target of Opportunity proposal. D. J. K. B. and D. A. are funded by the  Royal Society. T. G. has been supported in part by the  Scientific and Technological Research Council (T ÜBITAK )  119F082 ,  Royal Society Newton Advanced Fellowship ,  NAF\R2\180592 , and  Turkish Republic, Directorate of Presidential Strategy  and  Budget  project,  2016K121370. J. Hare  acknowledges support from an appointment to the  NASA   Postdoctoral Program  at the  Goddard Space Flight Center , administered by the  Universities Space Research Association  under contract with  NASA. C. M. is supported by an appointment to the  NASA   Postdoctoral Program  at the  Marshall Space Flight Center , administered by the  Universities Space Research Association  under contract with  NASA.
M. O. A. acknowledges support from the  Royal Society  through the  Newton International Fellowship  programme. This work made use of data from the NuSTAR mission, a project led by the  California Institute of Technology , managed by the  Jet Propulsion Laboratory , and funded by the  National Aeronautics and Space Administration. This research has made use of the NuS-TAR Data Analysis Software (NuSTARDAS) jointly developed by the  ASI Science Data Center (ASDC, Italy)  and the  California Institute of Technology (USA). NICER is a mission of  NASA 's  Astrophysics Explorers Program. This.
We present PATICE, a GIS database of Patagonian glacial geomorphology and recalibrated chronological data. PATICE includes 58,823 landforms and 1,669 geochronological ages, and extends from 38°S to 55°S in southern South America. We use these data to generate new empirical reconstructions of the Patagonian Ice Sheet (PIS) and subsequent ice masses and ice-dammed palaeolakes at 35 ka, 30 ka, 25 ka, 20 ka, 15 ka, 13 ka (synchronous with the Antarctic Cold Reversal), 10 ka, 5 ka, 0. 2 ka and 2011 AD. At 35 ka, the PIS covered of 492. 6 x10 3 km 2 , had a sea level equivalent of ~1,496 mm, was 350 km wide and 2090 km long, and was grounded on the Pacific continental shelf edge. Outlet glacier lobes remained topographically confined and the largest generated the suites of subglacial streamlined bedforms characteristic of ice streams.
Further, the calibration of radiocarbon dating  (Hogg et al. , 2013; Ortlieb et al. , 2011; Stuiver et al. , 2009)  and cosmogenic nuclide dating  (Borchers et al. , 2016; Kaplan et al. , 2011; Lal, 1991; Stone, 2000)  has improved over time, making inter-study comparisons difficult given the varying times at which data were gathered and published. Moreover, research has long highlighted the importance of ice-dammed proglacial palaeolake development in eastern Patagonia during deglaciation (e. g. ,  Bell, 2009; Bendle et al. , 2017b; Caldenius, 1932; García et al. , 2014; Thorndycraft et al. , 2019a; Turner et al. , 2005) , but these have not been included in previous ice sheet reconstructions. While geomorphological data has been compiled previously  (Glasser et al. , 2008) , a geomorphological and chronological database of palaeolake development throughout southern South America has not been attempted.
To date, no attempt has been made to gather these numerous geochronological ages and geomorphological data from across Patagonia into a single database, recalibrate ages using the latest protocols, and assess their reliability. Although LGM ice extent has previously been reconstructed (e. g. Caldenius, 1932; Coronato and Rabassa, 2011; Harrison and Glasser, 2011) , no attempt has been made to calculate ice volume from empirical data, assess confidence in icemarginal positions, or reconstruct the entire ice sheet and its changing palaeolakes through time. This is required as a first step to improving data-calibrated numerical modelling of the PIS. Here, we present an original, pan-ice sheet, empirical reconstruction (PATICE) of the PIS (38°S to 56°S) from 35 ka to 2011 AD (0 ka) in 5000 year increments, with additional reconstructions at periods of significant ice-margin stabilisation (13 ka, coeval with the ACR, and during the Late Holocene, 0.
2 ka). Note that since there is debate about whether the "Little Ice Age" is a global event, in this article we use the term "Late Holocene" for the most recent glacier expansion, dating from 0. 5 -0. 2 ka BP. We do not include pre-35 ka glacial fluctuations of the PIS due to a lack of data across the study area, but note that there is evidence of extensive ice prior to 35 ka in southern Patagonia (cf. García et al. , 2018; Darvill et al. , 2015a). We use volume-area scaling to provide a new estimate of ice volume for each time slice. Our reconstruction compiles existing geomorphological and chronological data, which have been robustly standardised, together with an assessment of the reliability of each age. Our new PATICE database includes 58,823 landforms, 1,669 geochronological ages that constrain ice-free conditions (all recalibrated according to the latest protocols), and spans the breadth of the published literature from 38°S to 56°S centred around the Andes in southernmost South America.
3. A. Mean annual air temperature (°C)  (1970 -2000)  at 30 Arc Seconds resolution, from the WorldClim2 dataset  (Fick and Hijmans, 2017). B. Mean annual precipitation (mm)  (1970 -2000)  at 30 Arc Seconds resolution, from the WorldClim2 dataset. C. Mean annual air windspeed (m s -1 )  (1970 -2000)  at 30 Arc Seconds resolution, from the WorldClim2 dataset. Location of the northerly limit of the mean present-day SWW and Subtropical Front after  Kohfeld et al. (2013). D. Approximate distribution of the SWW and oceanic polar fronts that control Patagonia's climate. The westerlies bring rain and snowfall to the west coast of Patagonia. The Subtropical Front (STF) sits at the northern limit of the westerly wind belt. pollen, charcoal and lake-level records provide evidence for a dry period at some point in the mid-Holocene  (Kilian and Lamy, 2012; Schäbitz et al.
Input data At the scale of the PIS, glacial landforms were mapped from remotely sensed images, particularly satellite imagery (for details on overall methods, see:  Boulton and Clark, 1990; Chandler et al. , 2018; Clark, 1997; Clark et al. , 2012 Clark et al. , , 2018;; Glasser and Jansson, 2008; Jansson and Glasser, 2005). The most commonly used sensors in previous mapping efforts in Patagonia are Landsat 8 OLI and Landsat 7 ETM+ scenes (30 m resolution, 15 m when pan-sharpened), and ASTER (15 m spatial resolution) satellite images, as well as the higher resolution Digital Globe images now widely available in Google Earth Pro and ESRI ArcGIS. Satellite images are commonly overlaid on a Digital Elevation Model (DEM) to provide topographic context, the most commonly used elevation models being the Shuttle Radar Topography Mission (SRTM) DEM, ASTER GDEM (30 m spatial resolution), and the GEBCO 2019 DEM for bathymetric data (The General Bathymetric Chart of the Oceans; GEBCO Compilation Group, 2019).
, 2008; Dowdeswell et al. , 2016c; Dowdeswell and Vásquez, 2013; Fernández et al. , 2017; Lastras and Dowdeswell, 2016). In addition to the multibeam echo-sounder data on seafloor morphology described and interpreted in these papers (horizontal resolution usually a few tens of metres), we used the GEBCO 2019 15 Second Arc Grid (GEBCO Compilation Group, 2019) to investigate the geomorphology of the continental shelf edge around Patagonia. The resolution of the GEBCO 2019 DEM is too coarse to map many types of submarine glacial landforms, but allows mapping of major glacial troughs and the possible suggestion of some moraines. Published bathymetric mapping is included in our compilation  (Dowdeswell et al. , 2016a (Dowdeswell et al. , , 2016b (Dowdeswell et al. , , 2016c)). Our PATICE compilation also includes an updated version of the 2011 AD inventory of 617 glaciers mapped by  Davies and Glasser, 2012.
, 2016). Each of the methods used, and the corrections and calibrations we apply, is discussed in detail in the Supplementary Methods. Compiling published ages from the literature (census date August 2019) gives us greater confidence in reconstructing glacial chronology than considering different studies in isolation. Here, we use a compilation of ages that provides insights into the lateral and vertical extents of the PIS and the later ice fields the PIS disintegrates into (Tables  1  and 2 ). Our compilation builds on earlier efforts to produce databases of ages  (Coronato and Rabassa, 2011; Hall et al. , 2017; Martínez et al. , 2011; Mendelova et al. , 2017; Rabassa et al. , 2011). These databases were cross-checked, and additional published literature was scoured for data pertaining to the geographical position of ages, which were further cross-checked in ArcGIS using the maps and datasets provided.
In some instances, errors were found in original papers and where possible these were rectified via communication with the original authors. In a few rare cases, errors in ages or geographical location meant that published ages were excluded from the dataset. Compiled ages are recalibrated according to the methodology set out in the Supplementary Methods. Cosmogenic nuclide 10 Be exposure ages use the  Kaplan et al. (2011)  production rate. Throughout this study, ages are given as before 1950 AD, except for lichenometry and dendrochronology ages, which are given in calendar years AD. Ages are presented with the age formats shown in Table  1. All final ages were mapped in ArcGIS as ESRI point shapefiles (*. shp), projected to UTM Zone 18S (datum: WGS 84). Each individual age contains attribute information including the publication reference,    4 ).
Consistency and quality control of published ages We assign all ages in the PATICE database a quality control rating to aid interpretation over the scale of an entire ice sheet and to enable the identification of poorly resolved ages (Fig. 4 ). This approach is pragmatic given the resolution of isochrones produced (5 kyr), the size of the area analysed, and the scale and quantity of ages compiled. This rating scheme should not be taken to indicate that the ages or associated studies are perceived to be of low, medium or high quality themselves, and we strongly urge readers to consult original studies when using our compilation to investigate a particular area or higherresolution time-step. Principally, the reliability index reflects the availability of data needed to recalculate ages from the original publication, whether samples are outliers or are part of a group that replicates the same age, and whether they are in situ with well constrained geomorphic or stratigraphic context.
Following the approach used by recent studies that encourage model-data comparisons (e. g. Hughes et al. , 2016; Small et al. , 2017; Ely et al. , 2019) , ages are assigned a reliability index rating of 1, 2 or 3, based on the criteria in Table  3. Decisions are made by the lead and coauthors at the time of writing. These ages are symbolised with green, amber or red circles respectively in the figures in this article. 1. All criteria are met and the age is considered reliable (green); 2. Most of the criteria are met and the age is probably reliable (amber); 3. No criteria met or the age is an outlier; the age is considered unreliable (red). Stern (1990) , in  Marden and Clapperton (1995)  1    (Lowe and Walker, 2015). Within calibration range of SHCal13 / Marine13 dataset  (Hogg et al. , 2013; Reimer et al.
Published ages tend to occur in clusters around the more accessible parts of the palaeo ice-margin. In addition, some data reduction is necessary to make the most useful palaeo ice-sheet reconstructions. However, in some places (e. g. eastern Northern Patagonian Icefield, Torres del Paine), there is enough dating resolution to separate out time-slices at 15, 13 and 10 ka, which allows us to generate a specific ice advance coeval with the ACR. Another additional time-slice through the Late Holocene uses published data  (Davies and Glasser, 2012; Glasser et al. , 2011a; Meier et al. , 2018)  and new geomorphic mapping to constrain ice extent at 0. 5 -0. 2 ka. In drawing the isochrones, context was used to assess whether ages related directly to an ice margin (e. g. , 10 Be ages on a moraine), or whether they denoted a minimum age for ice-free conditions (e.
Interpolation of isochrones New and published glacial geomorphological mapping were used to reconstruct ice-sheet limits (Fig. 4 , Fig. 5 ). We interpolated between isochrones using these geomorphological and topographical data to produce the maps of past ice-sheet extent. Reconstructions of ice limits were conducted 'blind': relying on geomorphological, chronological and topographical data, but without comparison to established LGM reconstructions. We assume that prominent moraines indicate a period when the ice margin stabilised and, therefore, in the absence of dating control, that moraines adjacent to dated ice limits or a similar distance from the ice divide have a similar age. Where no moraines were visible from previously published mapping or from new analysis of satellite imagery, ice limits in immediately adjacent valleys with the same orientation were assumed to have reached a similar position and elevation, and assigned a low confidence rating.
Palaeolakes We map the extent of ice-dammed palaeolakes at each timeslice. The extent of the palaeolakes is constrained by published geomorphology and chronology  (Bell, 2009; Bendle et al. , 2017b; Caldenius, 1932; García et al. , 2014; García et al. , 2019; Glasser et al. , 2016b; Hein et al. , 2010; Horta et al. , 2019; Martin et al. , 2019; Martinod et al. , 2016; Thorndycraft et al. , 2019a; Turner et al. , 2005). The ASTER GDEM dataset was used to interpolate lake levels between mapped shorelines or other geomorphological datapoints. Differential isostasy is not taken into account in these reconstructions, apart from around the Northern Patagonian Icefield  (Thorndycraft et al. , 2019a). In other cases, glacial lakes are reconstructed following analysis of the DEM where our reconstruction indicates that current drainage routes are blocked.
Relative sea level record We use the relative sea level datapoints in Table  5  throughout our reconstruction figures, where we alter the GEBCO (GEBCO Compilation Group, 2019; www. gebco. net/) marine bathymetry dataset to reflect relative sea level (Table  5 ). We use published data from the Argentinian Atlantic coast  (Guilderson et al. , 2000; Peltier and Drummond, 2002 ; Table  5  Regional relative sea levels used in the PATICE reconstruction. Schellmann and Radtke, 2010;  Isla, 2013). There is a dearth of data for the Pacific margin during the LGM, Late Glacial and Holocene, and sea level change in the northern part of the study area may be overestimated in our reconstruction. Sediments on the Argentine continental shelf in southern Patagonia suggest a local relative sea level lowering of 150 m at the LGM  (Guilderson et al.
Glaciomarine landsystem The 15 arc-second GEBCO dataset (GEBCO Compilation Group, 2019) permits a relatively low-resolution visualisation of the wider Patagonian continental shelf. The western, Pacific continental shelf is characterised by 12 relatively deep cross-shelf troughs up to 300 km long between 42°and 55°S (Fig. 9A ). Troughs are not apparent on the corresponding Atlantic continental shelf, which is typically <100 m deep, gradually deepening to 200 m on the outer shelf before the sharp break in slope at the continental shelf edge. In contrast, the Pacific coast troughs emanate from the narrow, deep, inter-island fjords that characterise the western Patagonian coastline (Fig. 9A ). The inner fjords between the islands reach depths of more than 1000 m below sea level and are 3 to 10 km wide.
PATICE Reconstruction (35 -0 ka) This section provides brief summaries of the geomorphological and chronological data that underpin our ice sheet reconstruction, followed by the results of our 5 ka reconstructions from 35 ka to present. For simplicity, we have sub-divided the former PIS into six key sectors spanning the full latitudinal range of the study area from 38°S to 56°S (Fig. 10 ). From north to south, we refer to these as: Chilean Lake District (38°-42°S), Isla de Chiloé and Archipielago de los Chonos and the adjacent mainland (42°-46°S), Northern Patagonian Icefield sector (46°-48°S), Southern Patagonian Icefield sector (48°-52°S), Gran Campo Nevado sector (52°-53°S) and Cordillera Darwin sector (53°-56°S).
7. Key landforms associated with the lowland land-terminating glacial landsystem. A: Latero-frontal moraines at the eastern margin of Lago GCBA. B: outwash fan. Centre: Geomorphological map showing key land-terminating landforms, such as outwash plains, moraines and meltwater channels. C: Detail of meltwater channels. D: Detail of moraines and meltwater channels. Location of photographs is shown on the central map. database  (Andersen et al. , 1999; Denton et al. , 1999; Moreno et al. , 2015). Other ages are excluded as they are not as relevant to our reconstruction. Many of these radiocarbon ages are given a reliability rating (see Section 3. 3) of 2 rather than 3 as they date bulk material, have δ 13 C values outside of the ideal range, or have an unclear stratigraphic or geomorphic context, or are hard to locate.