dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013). Bedforms are predominantly ice-scoured bedrock there, overlain by glaciolacustrine sediments from Palaeolake Balmaceda, which produced shorelines of up to 610 m asl in the Río Huemules valley. The col today is located at 585 m asl, indicating that lowering through moraine incision may have occurred (Fig. 15 ). This palaeolake was likely extant at 20 ka, in agreement with radiocarbon ages presented by  Miranda et al. (2013)  (unfortunately, insufficient data is available to allow inclusion of these ages in our database), and numerous Ho tephra ages  (Weller et al. , 2015). The lake is likely to have disappeared by 15 ka with the fragmentation of the ice masses and the opening of the drainage route to the Pacific (Fig. 15 ). The spillway that would drain this lake sits at 585 m asl.
2 m  (Hulton et al. , 2002). Hubbard et al. (2005)  used a time-dependent model to investigate the PIS between 45°and 48°S during the Fig. 35. A. Comparison of our 35 ka LGM reconstruction with that of  Coronato and Rabassa et al. (2011). Sea level reconstruction (-150 m at the LGM on the Argentine Shelf) is from geological data in  Guilderson et al. (2000) , symbolised using the GEBCO marine bathymetry dataset. Outlet lobe abbreviations: BV/RG/UE = Bella Vista/Río Gallegos/Ultima Esperanza; Corcovado = Golfo de Corcovado; Ancud = Golfo de Ancud; GCBA = Lago General Carrera/Buenos Aires; CP = Lago Cochrane/Pueyrredón; MSL = Monte San Lorenzo. B. Reconstruction of ice flow pathways and ice divide for the 35 ka ice sheet. Flowlines are reconstructed using mapped geomorphology (glacial lineations, moraines, bathymetric troughs, topography, etc) and the pattern of recession.
Ice divide has low confidence and is assumed to lie over the Andean mountain chain. LGM. These modelling experiments suggested that to reach the LGM moraines bounding Lago GCBA, a lowering of the ELA by ~900 m is required. These modelled experiments suggested that the PIS was highly dynamic, with mean ice thicknesses of 1130 m drained by large ice streams to the western margin and fast-flowing outlet lobes to the east. This model, forced with the Antarctic ice core record of past climate, also recognised a stabilisation during the ACR and rapid collapse after that time, shrinking to near its present-day margins by 11 ka. We use our geomorphological database (including bathymetric troughs, over-deepened lakes and basins, glacial lineations in both rock [roche moutonnées, whalebacks] and sediment [large streamlined drumlins, smaller flutes in upland regions], fjords, trimlines, parabolic valleys and orientation), topographic data, and the pattern of recession revealed by moraines to determine palaeo ice-flow lines (Fig.
Summary and conclusions We present the first 2D ice-sheet scale reconstruction of PIS evolution at 5 kyr intervals, from 35 ka to 2011 AD, with additional timeslices at the ACR (13 ka) and late Holocene (0. 5 -0. 2 ka). The reconstruction is empirically constrained by the PATICE database, a new compilation of published geomorphology (moraines, perched delta terraces, trimlines, bathymetric troughs, palaeochannels, shorelines, glacial lineations, alluvial fans, sandar, cirques) and recalibrated published ages. Our chronological database includes radiocarbon ages, cosmogenic nuclide surface exposure ages ( 10 Be, 26 Al, 36 Cl, 3 He), cosmogenic depth profiles, OSL, lichenometry, dendrochronology, tephrochronology, varve ages and historical documents. Each age is assigned a reliability rating based on well-defined criteria to aid our panice sheet reconstruction.
This new PATICE database thus generates an unprecedented assessment of materials pertinent to southern Patagonian glaciation. At its last maximum, the PIS was 350 km wide, 2091 km long, and covered 492. 6 x 10 3 km 2 , with a sea-level equivalent of 1,496 mm. Our reconstruction envisages an ice sheet 12% larger than previous   9. reconstructions. It was arranged along the spine of the Andean mountain chain, with prominent outlet lobes flowing orthogonally from the ice divide. The western, Pacific margin of the PIS was likely grounded at the continental shelf edge and outlet lobes here calved into deep water. The emergence of the broad Argentinian continental shelf resulted in an enlarged expanse of land east and towards the Atlantic Ocean. Outlet lobes extended onto this continental plain, following existing topography and often with limited interaction between lobes.
Acknowledgements We thank all other researchers who shared or clarified their data with us. We thank  Eñaut Izzagirre  for sharing shapefiles of geomorphology around northern  Cordillera Darwin. Bethan Davies ,  Varyl Thorndycraft  and  Julian Martin  acknowledge additional Patagonian fieldwork funding support from the  Quaternary Research Association. Bethan Davies  and  Varyl Thorndycraft  also acknowledge support from the  Royal Holloway Research Strategy Fund ,  British Society for Geomorphology and Geologists' Association  for this project. Julian Dowdeswell  thanks the  Servicio Hidrográfico y Oceanográfico de la Armada de Chile (SHOA)  for access to their swath-bathymetric data from Chilean fjords. Jacob Bendle  acknowledges  NERC   Doctoral Training Grant   NE/L501803/1. Monika Mendelova  thanks  NERC   Doctoral Training Grant   NE/L002558/1  and the  University of Edinburgh  for her PhD studentship.
2014; Bindi et al. 2014; Derras et al. 2014; Kotha et al. 2016; Kuehn and   Scherbaum  1 3  2016). In addition, several selected GMMs from other regions that were adopted within the EHSM13 have been superseded by subsequent updates  (Cauzzi et al. 2015; Chiou and Youngs 2014; Zhao et al. 2016a, b, c). The second factor is that in the last decade the volume of strong motion data from earthquakes in Europe has grown by an order of magnitude. Systematic efforts to process and harmonise this database of European strong motion records and their associated metadata has culminated in the European Strong Motion (ESM) flatfile  (Lanzano et al. 2019 ) (Fig. 1 ), a crucial archive of more than 20,000 records represented by peak ground motion values, response spectral accelerations and Fourier spectra.
Epistemic uncertainty: insights from new data and new perspectives The approach to characteristic epistemic uncertainty in ground motion models within the ESHM13, and described in detail by  Delavaud et al. (2012) , invokes what shall be referred to hereafter as the "multi-model" approach. For each of the tectonic domains considered within Europe, the ground motion model logic tree was constructed by selecting multiple models from the literature according to an expert panel, then refining and weighting them using metrics describing the fit of each model to existing strong motion data for each tectonic region where possible. This approach was one of the first of its kind to attempt, at a regional scale, a hybrid expert-and data-driven logic tree construction. An obvious starting point for a more comprehensive update of a ground motion logic tree for Europe would therefore be to repeat this process, integrating new ground motion models published since the ESHM13 and the ground motion records from new ESM database.
A more suitable approach to address the questions posed previously is devised by Douglas (2018a), who presents the general structure of a scaled backbone logic tree that explicitly captures the epistemic uncertainties on three critical, and regionally-variable, facets of ground motion: the anelastic attenuation ( ±ΔQ ), the source stress parameter ( ±Δ ) and the statisical uncertainty ( ). In its original presentation, Douglas (2018a) adopts the GMM of  Kotha et al. (2016)  as the scaled backbone, utilising its three regionalisations (Italy, Turkey and "rest of Europe") as representative of the epistemic uncertainty on anelastic attenuation. The stress parameter scaling is based on the residuals of observed ground motions from different regional subsets of the strong motion database (e. g.
Region-dependent source variability ( ıL2L l ) For the source location coefficient a different regionalisation is adopted, this time using one of the area source branches of the SERA seismic source model, namely the "TECTO" branch. The construction of the area source model is beyond the scope of this publication; however, the "TECTO" branch is intended to describe large scale seismic sources representing the major tectonic features across Europe believed to influence the distribution of seismicity. The region-dependent source variability is determined during the regression as a random effect describing the area source in which each earthquake in the database is located. Constraint of the random effect is only possible with two or more events occuring in the source, with the standard error diminishing with increasing seismicity.
Two factors are likely to be 10 -1 10 0 10 1 10 2 R JB (km) 10 -4 10 -3 10 -2 10 -1 10 0 Sa (0. 01 s) (g) Default Backbone -ε • max (τ L2L , σ µ ), c 3 -ε • τ c3 -ε • max (τ L2L , σ µ ), c 3 -ε • max (τ L2L , σ µ ), c 3 + ε • τ c3 0 • max (τ L2L , σ µ ), c 3 -ε • τ c3 0 • max (τ L2L , σ µ ), c 3 0 • max (τ L2L , σ µ ), c 3 + ε • τ c3 +ε • max (τ L2L , σ µ ), c 3 -ε • τ c3 +ε • max (τ L2L , σ µ ), c 3 +ε • max (τ L2L , σ µ ), c 3 + ε • τ c3 Fig. 8  General schematic showing the decay of median ground motion at Sa(0. 01s) with distance, as predicted by the nine-branch "shallow default" logic tree for a M W 6. 0 event and a rock site condition ( V S30 800 m/s) = 1. 732 influential in accounting for this discrepancy. The first is that in comparison to the previous strong motion databases for Europe, particularly that used by  Akkar and Bommer (2010) , the proportion of records from the central and northern Appenine region is significantly increased and therefore skews the centre of the data set toward these conditions, which are generally faster attenuating than the rest of Europe on the whole.
Regional calibration The two regionalisations implied by the L2L l and c 3,r distributions form the basis for the regional adjustment of the logic trees. Whilst there is some evidence for a regional trend in source-region to source-region uncertainty, given the small number of earthquakes available in each source the standard errors on each individual L2L l (T) are substantial. With more time and more earthquakes this uncertainty will be reduced, but even in spite of the large volume of data in the ESM database we do not feel sufficiently confident at present to attempt to regionalise L2L l. Therefore it is believed appropriately conservative that the "stress parameter" term ( max L2L , ) is retained without regionalisation. For the c 3,r (T) term the prospects for regional refinement are encouraging.
A similar situation is encountered in the case of Greece, where many travel paths traverse, or least propagate close to, the Cyclades volcanic belt. The region of the entire data set with the lowest c 3 is in fact the eastern Corinth zone, where high heatflow  (Lucazeau 2019) , shallow source depths and high strain  (Carafa et al. 2015)  combine to produce an environment of particularly fast attenuation even within the broader domain of active shallow crustal seismicity in Europe. Although the calibration of the backbone model in Cluster 3 suggests an influence from volcanism within its dataset, we cannot refer to it as a truly volcanic model in a sense that might be comparable with other such volcanic models in the literature (e. g. Tusa and Langer 2015; Lanzano and Luzi 2019). In our case the records are not exclusively from volcanic earthquakes, meaning that the depth distributions of those events calibrating the attenuation in the cluster are deeper and of typically larger magnitude ( M W > 4.
For running seismic hazard on Eurocode 8 class A rock we assume a measured V S30 condition, whilst for applications to the 2020 European Seismic Risk model, where ground motion should correspond to that at the soil surface in any location, the site condition is mostly inferred from proxy information and therefore an inferred condition is assumed. To determine how the components of aleatory variability have changed in the  Kotha et al. (2020)  model with respect to previous generations, Fig. 14  compares each of the three components with those found in selected preceding models. Bindi et al. (2014)  and  Kotha et al. (2016)  are based on the previous RESORCE European strong motion data set, whilst  Rodriguez-Marek et al. (2013)  and  Tromans et al. (2019)  present or adopt models of 0 and S2S inferred from global databases and consider both heteroskedastic and homoskedastic alternatives.
The model for the measured sites provides values that are similar to those of  Bindi et al. (2014)  and  Rodriguez-Marek et al. (2013). That only the measured-V S30 S2S is only comparable with other models, despite use of a robust regression method and removal of inferred site parameter cases, is actually a reflection of the extent to which the larger number of sites represented in the ESM data set increases the site-tosite variability with respect to preceding studies. As might be expected, when calibrating S2S using only the inferred V S30 the variability increases substantially, especially at shorter periods. Although individual components of the homoskedastic aleatory uncertainty model from  Kotha et al. (2020)  may be comparable to those found in some preceding models, initial calculations of both seismic hazard and risk revealed an unduly large influence from excessive ground motions at moderate-to-large magnitudes in the range of +1 ⋅ T to +3 ⋅ T.
Despite the addition of several events with M W ≤ 6 in the ESM database, the constraint of aleatory uncertainty at large magnitudes remains poor in comparison to other global data sets such as NGA West 2. Many studies of ground motion variability, as well as preceding ground motion models, have shown evidence of heteroskedastic variability in both the 0 and 0 terms (e. g  Youngs et al. 1995; Rodriguez-Marek et al. 2013; Al Atik 2015) , resulting in reduces aleatory variability for larger magnitudes at short-periods. To assess whether there is evidence of heteroskedasticity in the random-effects residuals of the  Kotha et al. (2020) , Fig. 15  shows the trends in the source-region corrected between-event residual ( B 0 el ) with respect to magnitude, and the site corrected within-event residual ( 0 ) with respect to magnitude and distance.
Each residual is colour scaled according to their weight in the robust linear Fig. 14  Comparison of components of aleatory uncertainty of the current model those found in previous models: 0 (left), (centre) and S2S (right). Labelled models: Bi2014  (Bindi et al. 2014 ), Ko2016  (Kotha et al. 2016 ), Ko2020  (Kotha et al. 2020 ), Tr2019 (Tromans et al. 2019 ) and RM2013 (Rodriguez-Marek et al. 2013)  mixed-effects model. Results are shown here only for Sa(0. 1s) , but a more detailed set of comparisons for multiple periods of ground motion can be seen in Electronic Supplement 2. In the case of B 0 el , the homoskedastic 0 of Kotha et al. (  2020 ) is compared against the heteroskedastic models from  Abrahamson et al. (2014)  and the "global" model Al  Atik (2015). The latter are both calibrated using the NGA West 2 database, which contains a substantially greater number of large ( M W ≥ 6 ) events than ESM.
(2020)  in the hazard and risk applications. For 0 , Fig. 15  suggests a decrease in 0 with magnitude but no significant trend with respect to distance. As the few large events in the ESM database are generally well recorded, we believe this trend to be robust, suggesting that a heteroskedastic 0 may too be more appropriate. Here, however, whilst the "global" model of Al  Atik (2015)  seems to agree well with the observed variance at larger magnitudes, the original 0 model of  Kotha et al. (2020)  agrees better with the observed distribution of the data in the 4. 0 ≤ M W ≤ 5. 5 range. Rather than adopting the 0 model of Al  Atik (2015)  without adjustment, we instead fit their magnitude-dependent heteroskedastic model to the binned 0 distribution for M W > 6 and retain the Kotha et al. (2020) 0 values for M W ≤ 5.
The complete shallow crustal logic tree For shallow crustal seismicity in regions analogous to those sampled within the ESM database, it has been possible not only to define a scaled backbone ground motion model logic tree to capture uncertainty in regional stress scaling and anelastic attenuation, but also to tune it in order to model large scale regional variations. For the Mediterranean region and much of western and southern Europe, the epistemic uncertainty is represented by the shallow crustal ground motion logic tree shown in Fig. 16. In effect, this tree contains six sets of nine branches, one set for the default backbone (shown above the dashed line in Fig. 16 ) to be applied where no ESM data are available, and one for each of the five clusters or subregions implied from the exploration of the residual attenuation random effects c 3,r.
As such, we believe this is a more appropriate depiction of the epistemic uncertainty than that of previous models. It must be re-iterated, however, that this does not represent the complete ground motion logic tree for Europe, nor even for all types of earthquakes found in shallow crust. The key caveat here is that even with the default backbone there is a critical assumption that it represents the broadest epistemic uncertainty in residual attenuation and source location implied by the extent of the ESM data set, and can therefore be considered representative only in regions that are, from a ground motion perspective, similar to those sampled by the ESM data. This would exclude both the non-shallow seismic regions and the stable shield, which will be addressed in subsequent publications.
Whilst the actual calibrations and weights of the scaled backbone itself are clearly relevant for the purposes of the PSHA calculations, what may be of most value to the broader community within this approach is the construction of a general framework through which new strong motion data can inform, refine and reduce the epistemic uncertainty. In the approach adopted here, the ESM data has steered the calibration of the logic tree via the constraint of region-to-region variability in both source and attenuation, and then using this same information permitted the centre, body and range of the GMM logic tree to be tuned according to the regionalisation. Not only does this approach provide scope for future data to continue to refine the models, both spatially and in terms of the calibrations of the backbone, it also introduces some necessary inertia into the process such that one may not necessarily expect large changes in the distribution following a well-recorded event or sequence based on the characteristics of that event(s).
Indeed  Stafford (2019)  proposes a more formal framework for such an approach using Bayesian updating, to which the present logic tree seems ideally suited. This would form an obvious avenue for future research, setting in place an operational process for regular updating of hazard models as new data feed into them. Although the development of the calibrations and their regional adjustments has been based upon a single extensive database of ground motion observations, adjustments can also be made without necessarily needing to re-run the regression or to have strong motion data feed into this common database, desirable though that might be. Where sufficient data are collected locally and not necessarily integrated directly into the ESM, these can still be used to identify regional differences in the models by virtue of systematic trends in the inter-and intra-event residuals with respect to either the "central" or the regionalised models.
Coastal boulder deposits (CBD), transported by waves at elevations above sea level and substantial distances inland, are markers for marine incursions. Whether they are tsunami or storm deposits can be difficult to determine, but this is of critical importance because of the role that CBD play in coastal hazard analysis. Equations from seminal work by  Nott (1997) , here referred to as the Nott Approach, are commonly employed to calculate nominal wave heights from boulder masses as a means to discriminate between emplacement mechanisms. Systematic review shows that this approach is based on assumptions that are not securely founded and that direct relationships cannot be established between boulder measurements and wave heights. A test using an unprecedented dataset of boulders moved by storm waves (with associated sea-state data) shows a lack of agreement between calculations and actual wave heights.
These examples demonstrate the extent to which tsunami interpretations of CBD have become normalized, and they testify to the widespread acceptance of this methodology, which we will demonstrate is flawed. In this study, we show that the Nott Approach equations are invalid. We explain the embedded flaws in the hydrodynamic reasoning, and-using the  Cox et al. (2018a)  database of boulders on the Aran Islands moved by recent storms-demonstrate that the equations return wave height values that are unrealistic and contradicted by data. This study provides the first largescale field-validated analysis of the Nott Approach, showing the extent to which these equations misrepresent relationships between boulder movements and storm wave conditions. The Nott equations cannot hindcast storm waves, and they cannot be used to differentiate between storm waves and tsunami.
THE TEST CASE: BOULDERS MOVED BY STORMS IN WINTER 2013-2014 To illustrate the ineffectiveness of the Nott Approach, we apply a series of wave-height equations (Nott's original, and several derivatives) to boulders on Ireland's Aran Islands (Figure  2 ) that were moved by storm waves in winter 2013-2014  (Cox et al. , 2018a; Cox, 2019). Aran Islands' Coastal Boulder Deposits CBD on the Aran Islands (Figure  1 ) are well-documented, and the reader is referred to the literature for details on their geomorphology (e. g. ,  Williams and Hall, 2004; Hall et al. , 2010; Cox et al. , 2012 Cox et al. , , 2018b)). Boulder transport was recorded using before-and-after photography (Figure  3 ), at surveyed sites for which elevation and inland distance were also measured. The dataset (Supplementary Table  1 ) includes more than a thousand boulders, ranging in mass from 0.
Estimating Wave Heights Required to Move Aran Island Boulders Using the Nott Approach We calculate nominal wave heights for the Aran Islands dataset  (Cox et al. , 2018a)  using two equations: the original Nott (2003b) version, and a widely-used modification based on the work of  Nandasena et al. (2011b). Other widely cited Nott-Approach equations-(e. g. ,  Pignatelli et al. , 2009; Barbano et al. , 2010; Benner et al. , 2010; Lorang, 2011; Engel and May, 2012) produce similar or in some cases greater values; therefore, our discussion and the conclusions we draw apply equally to all versions of the Nott Approach. Nott (2003b)  provides equation variants for three different pre-transport settings (submerged, subaerial, and jointbounded), but we consider only the subaerial case because the Aran Islands boulders were already sitting on the supratidal platform prior to the 2013-2014 storms  (Cox et al.
The original Nott equations return wave heights up to 45 m, whereas those from the Nandasena-based equations are generally significantly lower. that winter (Figure  6 , Supplementary Table  1 ). But it is not that simple. H max is an estimate, not a measurement, and it is statistically rare. Waves that large would be few, if they occurred at all. It takes only one big wave to move a big boulder; but because boulders were moved at multiple locations strung all along the Aran Islands coastline (as well as at other sites on the mainland not included in this dataset:  Cox et al. , 2018a) , the transport events cannot simply have been the work of one or two outsize waves; multiple waves must have been involved in the transport events. And the likelihood that they could all have been H max is highly unlikely.