dataesr/scientific-paragraphs-categorization huggingface dataset:
2 x 0. 2° area around the mooring location. Sea Surface Temperature (SST) was recovered from the NOAA database with the same gridding as the data from the NASA's Giovanni program. Salinity, nutrient concentrations (nitrates and phosphates) and carbonate system parameters data were collected from the DYFAMED database (http://www. obsvlfr. fr/dyfBase/index. php). DYFAMED site is located around 200-220km (Figure  1b ) east of the sediment trap location (43 • 25´N, 7 • 52´E), in the Ligurian Sea. From an oceanographic view, its situation is upstream of the NC circulation and can be considered representative of seasonal and interannual variability of biological and water column properties of the open-ocean waters in the northwestern Mediterranean  (Heussner et al. , 2006; Meier et al. , 2014). Alkalinity and total carbon measurements were available for years 1998 to 2000 and mid 2003 to 2005.
12 (Figure  2 ). Note that the weight variations in our dataset are quite considerable, especially for G. truncatulinoides, probably due to the wider size fraction. Diameter does show correlation with SBW: 0. 33<r 2 <0. 5; and shows a non-negligible correlation with MBWdiam: 0. 2<r 2 <033. Our data demonstrates that SBW correlates more strongly with MBWdiam than with MBWarea for the 3 species: 0. 9>0. 48 for G. bulloides, 0. 89>0. 52 for N. incompta and 0. 97>0. 81 for G. truncatulinoides. These values are consistent with previous studies  (Beer et al. , 2010). Differences between SBW and both MBWarea and MBWdiam vary depending on the species: SBW is slightly heavier for G. bulloides, heavier for N. incompta and lighter for G. truncatulinoides. The mean standard deviation for all 3 species is around 8%: 7.
The correlations between SBW and shell area are consistent with previous studies  (Aldridge et al. , 2012; Beer et al. , 2010)  and underscore the importance of isolating the component of variation in foraminifera shell thickness that represents a change in calcification and does not occur as a direct result of change in shell size. Thus, the shell weight was size-normalized after Beer et al. , 2010 by isolating the influence of isometric scaling on wall thickness and calcification density. Area and diameter show a perfect correlation (r 2 = 1) for all 3 species, highlighting a relationship between the different MBWs. Moreover, both MBWarea (Figure  2 ) and MBWdiam, in either the sediment trap data and core-top data, do not correlate with area and diameter (1x10 -5 < r 2 < 0. 33 and 0. 001 < r 2 < 0.
, 2010; Osborne et al. , 2016)  have shown that sea surface warming and carbonate system parameters are the most likely parameters to control calcification on key calcifying phytoplankton species such as the coccolithophore Emiliania huxleyi organisms  (Meier et al. , 2014). However, datasets from sediment traps that cover a wide span of years and in which foraminifera weights have been analyzed are rare  (Kiss et al. , 2021) , therefore it is difficult to place our results in a more global context. Our analysis suggests that the trends observed in the carbonate system (Figure  5  This pH decrease, combined with the carbonate system parameters seem to affect G. bulloides and N. incompta calcification patterns in recent years, however, the degree to which each species react to a change in these parameters differs.
Acknowledgments. Authors would like to thank  Blanca Ausín  for her insight on radiocarbon dating and  Serge Heussner  for the retrieval of the sediment trap collected within the French national MOOSE program supported by  CNRS-INSU  and  ALLENVI. This study was funded by by the  Spanish "Ministerio de Ciencia e Innovación " through a grant number  PRE2019-089091  and through the project  RTI2018-099489-B-100 ;  PID2021-128322NB-I00. https://doi. org/10. 5194/egusphere-2022-711 Preprint. Discussion started: 22 August 2022 c Author(s) 2022. CC BY 4. 0  License.
Ground-motion models (GMMs) are often used to predict the random distribution of spectral accelerations (SAs) at a site due to an earthquake at a distance. In probabilistic seismic hazard and risk assessment, large earthquakes occurring close to a site are considered as critical scenarios. GMMs are expected to perform well for such rare scenarios i. e. , to predict realistic SAs with low prediction uncertainty. However, the datasets used to regress GMMs are usually deficient of data from rare/critical scenarios. The Kotha et al. (  2020 ) GMM developed from the Engineering Strong Motion (ESM) dataset was found to predict decreasing short-period SAs with increasing   ≥  ℎ = 6. 2, and with large within-model uncertainty at near-source distances   ≤ 30. In this study, we analysed and updated the parametrisation of the GMM based on non-parametric and parametric analyses of ESM and the NEar Source Strong motion (NESS) datasets.
INTRODUCTION For engineering purposes, a ground-motion observation at a site from an earthquake is often reported as the peak acceleration response of a 5% damped single-degree-of-freedom (SDOF) oscillator with fundamental period  when excited by the recorded accelerogram at the site. For a variety of SDOF fundamental periods, the vector of spectral accelerations [()] is the so-called response spectrum. Thousands of response spectra collected from several hundreds of earthquakes at several dozens of recording sites are compiled into engineering ground-motion datasets, such as the recent pan-European Engineering Strong-Motion (ESM by  Bindi et al. 2018; Lanzano et al. 2018) , the global Next-Generation-Attenuation West-2 (NGA-W2 by  Ancheta et al. 2014) , the Japanese KiK-net  (Okada et al.
2004 ) ground-motion datasets  (Bahrampouri et al. 2021; Dawood et al. 2016) , and a few others. Such datasets are essential to studying the physical characteristics of () from past earthquakes and predict () of future earthquakes. For this purpose, a common practice has been to regress empirical attenuation relations connecting these observed () to the properties of earthquake ruptures (e. g. moment magnitude   ), the local geological conditions at the effected site (e. g. time-averaged shear-wave velocity   ), and the rupture-to-site distances (e. g. Joyner-Boore distance   ). Such empirical relations are commonly referred to as ground-motion models (GMMs), and are widely used in probabilistic seismic hazard assessments (PSHA). Therefore, the quality and quantity of ground-motion datasets, and the physical and statistical robustness of GMMsespecially beyond the (  ,   ,   ) ranges of the regressed datasetare of utmost importance in PSHA.
The most recent, and the largest yet, pan-European Engineering Strong-Motion dataset (ESM) was meticulously developed for the next update of European Seismic Hazard Map of the region -from ESHM13  (Woessner et al. 2015)  to ESHM20  (Danciu et al. 2021in-preparation). This dataset is 10-fold larger than its predecessor RESORCE  (Akkar et al. 2014b) , and facilitated the development of the  Kotha et al. (2020)  GMM, which superseded its RESORCE based predecessor  Kotha et al. (2016). The new GMM also made use of the latest geological and tectonic regionalisation models (e. g. Basili et al. 2019)  to predict more accurate (e. g. region-, locality-and site-specific) ground-motions in the extremely heterogeneous (active) shallow crustal regions of pan-Europe. Therefore, this regionally-adaptable GMM was adapted into a scaled-backbone GMM logic tree implementation (e.
g. Bommer 2012 and Douglas 2018)  in the new ESHM20 by  Weatherill et al. (2020b). Current ground-motion datasets, including the ESM, are a few thousands of recordings larger than a decade ago. However, a typical PSHA requires ground-motion predictions for rare scenarios (e. g. ≥ 5. 5 &   ≤ 30) that are very sparsely or never sampled before, but are also deemed the most critical for built environments in the region. For instance, only 2% of the ESM data qualifies the hazard critical criterion above. Of course, the other 98% data is quite useful in robustly calibrating the model for its region-, locality-, and sitespecific adaptability. But in regions such as Italy, Turkey, Greece it is perhaps more important to maintain GMM's physical and statistical robustness in the hazard critical scenarios.
Thanks to the timely feedback from the community of seismic hazard and risk modellers involved in ESHM20, we were motivated to evaluate the performance of the  Kotha et al. (2020)  GMM in hazard critical scenarios of Mediterranean active shallow crustal regions. Especially at short distances (  ≤ 20), the short period ( ≤ 0. 2) ground-motions predicted by the GMM for  ≥ 6. 5 are lower than those for 5. 7 ≤   ≤ 6. 2, and similar to those for   ≈ 5. 5. Although such behaviour reflects the modelling choice and is data-driven, it is discordant with the behaviour of other contemporary GMMs at near-source distances (at short periods and for large magnitudes); and more importantly, has a rather high within-model uncertainty due to the lack of calibrating data. To resolve this issue, we propose an essential revision to the model, while also evaluating its behaviour against the recently published Near Source Strong-motion datasets (NESS and NESS2 by  Pacor et al.
2018; Sgobba et al. 2021b ). NESS2 is an update of the NESS dataset. Therefore, we simply refer to them as NESS from hereon. of the sampled   ≥ 5. 5 events are comparable. Although the two datasets are quite complementary, the NESS2 dataset of  Sgobba et al. (2021b)  was not available during the development of  Kotha et al. (2020)  GMM. Even if the smaller NESS dataset of  Pacor et al. (2018)  was available in a limited way, it was decided against integrating it into ESM. The reasons being: 1) Kotha et al. (  2020 ) GMM was intended to be partially non-ergodic, in the sense that; events were localised into tectonic localities to capture systematic differences between earthquake generating sources, and stations were regionalised into attenuating regions to capture differences in apparent anelastic attenuation of ().
The tectonic localisation and attenuation regionalisation models used therein could not be extended to contain global events. The possibility of simply assigning NESS (or ESM) data to larger political boundaries was decided against because the region-to-region and locality-to-locality random variances estimated from ESM dataset were to be used in constructing the backbone GMM logic tree for ESHM20  (Weatherill et al. 2020b) , and these estimates were intended to capture the diversity of active shallow crustal regions in Mediterranean region alone; and not partially global. 2)  Kotha et al. (2020)  GMM was also required to function as a site-specific GMM for sites with sufficient number of recordings or otherwise, use the site-specific random-effects to develop a large scale siteresponse model for use in European Seismic Risk Map (ESRM20 by  Crowley et al.
2019 and Weatherill et al. 2020a). For this purpose, it was necessary that each site recorded a variety of magnitudes, and eventto-site distances and azimuths. As well that events are recorded by stations with a variety of site conditions. Most stations in NESS dataset have recorded a single large event at a close distance, and most events are recorded by sites located in very similar geological settings. In this regard, as opposed to the crossed and nested nature of ESM dataset, NESS is exclusively nested. In order to avoid any trade-offs between site-specific and event-specific (between-event) random-effects, we chose not to integrate the two datasets. 3) As seen in top-left panel of Fig. 1 , around 30% of NESS recordings show pulse-like characteristics, which are often characterised by higher long-period  and lower short-period  than average, depending on the event-to-site distance and azimuth.
Instead of biasing the (isotropic) GMM median (at short distances and long periods) with pulse-like effects, we chose to reserve NESS datasets for investigation of complex near-fault phenomenon, as done by  Sgobba et al. (2021a). In this study, we discuss the re-parametrization of the  Kotha et al. (2020)  GMM, consequent impact on its within-model and aleatory uncertainties, its limitation in capturing the complex near-source finite-fault phenomenon, and the apparent (or actual) global diversity of   ≥ 5. 5 shallow crustal earthquakes.
g. ESHM13 and ESHM20), and also in the strong-motion datasets (e. g. RESORCE and ESM). In fact, the functional form had only minor changes since  Bindi et al. (2014)  and  Kotha et al. (2016) , despite the 10-fold increase in data since RESORCE. A more detailed description is available in the Kotha et al. (2020) study. Here we reintroduce the functional form through equations (  1 ) through (4): (3) 𝑙𝑛(𝜇) = 𝑒 1 + 𝑓 𝑓 𝑀 = { 𝑏 1. (𝑀 𝑊 -𝑀 ℎ ) +𝑏 2. (𝑀 𝑊 -𝑀 ℎ ) 2 𝑀 𝑊 ≤ 𝑀 ℎ 𝑏 3. (𝑀 𝑊 -𝑀 ℎ ) 𝑀 ℎ < 𝑀 𝑊 (4) The coefficients  1 ,  1 ,  2 ,  3 ,  1 ,  2 ,  3 are the so-called fixed-effect coefficients of a mixed-effect model; while  3, , 2  ,  , 0 , 2  are the random-effects, and  are the residuals. In addition, the ℎ  (pseudodepth, h-parameter or near-source saturation parameter) was a fixed-effect regression coefficient in  Bindi et al.
Fukushima (1996)  has demonstrated eloquently the necessity of this term in a GMM, especially when the Fourier source spectrum is approximated to be an  -2 source model proposed by  Brune (1970). Of course, if  2 should take a positive or negative value depends on magnitude scale. Fukushima (1996)  showed that it should be negative if the choice of magnitude scale is   , and positive if the scale were to be   or. In accordance, both the pan-European  Bindi et al. (2014)  and  Kotha et al. (2016)  estimates of their respective  2 (at all periods) were negative, when regressed over the data from 4 ≤   ≤ 7. 6 events in RESORCE dataset. The Akkar et al. (2014a) model, also derived from the RESORCE dataset, had negative scaling coefficient for their   2 term. Among the NGA-W2 GMMs, the   2014 ), which shares its quadratic form with pan-European models mentioned here, while being derived from the NGA-W2 dataset also has small but positive   2 scaling coefficient.
   is the fixed-effect coefficient capturing the linear decay of () with   , i. e. the geometrical spreading of ground-motion as a spherical wave front. In distance range 10 <   < 80, analytical values of decay rate are about -1. 1 to -1. 3, but can also be strongly region dependent. For example, in the spectral decomposition of this ESM dataset using Generalized Inversion Technique (GIT,  Castro et al. 1990 , Edwards et al. 2008 , and Oth et al. 2011 ],  Bindi and Kotha (2020)  showed that the slopes of non-parametric 1Hz Fourier ground-motion amplitude decay curves at   < 80 are: -1. 36 in Italy, -1. 34 in regions encompassing Balkans and Romania, and -1. 03 in regions covering Aegean and East Anatolian regions. Since the  1 estimates of our model range between from -1. 34 and -1. 12 for ( = 0.
The effect of this parameter is more pronounced at   > 80, and is known to be strongly region dependent. We often find that  1 and  3 fixed-effects are inversely correlated because both capture the decay of ground-motion with distance; similar to correlation of  () and. However, since we have used a dataset of recordings with 0 ≤   ≤ 545 , the negative correlation between  1 and  3 is reasonably low at all periods. Inverse correlation of  1 and  3 is lower at short periods because anelastic attenuation effects high frequency ground-motions more than low frequency groundmotions. While at longer periods,  3 values become close to zero and slightly more inversely correlated to  1. For instance, a few GMMs force  3 = 0 at  ≥ 1. But as long as they are non-positiveindicating an unphysical exponential increase with distancewe are not too concerned about revising this part of the model.
Essentially, we mean to say that all the fixed-effects parameters of our model are necessary, and most behave as expected. The only exception being  3 , which in a way is also the most critical parameter controlling the GMM predictions for large magnitudes at short distances, and therefore the hazard and risk assessments. In the following sections, without changing the mixed-effects configuration of the GMM, and using the exact same dataset used in  Kotha et al. (2020) , we will choose a variety of  ℎ values to understand how the  3 estimates vary; and of course, its impact on median, aleatory variability, and within-model uncertainty of the GMM.
APPROACH The published fixed-effect coefficients of  Kotha et al. (2020)  GMM were estimates using  ℎ = 6. 2, based on the non-parametric trends of () scaling with. Here we repeat the regressions and estimate the fixed-and random-effects for the choices of  ℎ = 5. 5, 5. 7,6. 2,6. 5. While the  Kotha et al. (2020)  GMM was derived using a robust linear mixed-effects regression algorithm (robustlmm by  Koller 2016) , in this exercise involving several trials, we use a less computationally intense ordinary least-square mixed-effects regression algorithm (lme4 by  Bates et al. 2014 ). Both packages are developed for use with R software  (Team 2013). We have already verified (in  Kotha et al. 2020 ) that both the algorithms yield nearly identical estimates of fixed-effect coefficients. In fact, even the fixed-effect variance-covariance matrices differ ever so slightly between robust and ordinary least-square estimatesat least in this exercise.
We consider the apparent oversaturation towards larger magnitudes as indefensible because there seems to be no clear physical explanation as to why predictions for an M7 event would be lower than M6. 5. Despite, several independent datasets we have reviewedfor example, those used in the development of  Cauzzi et al. (2015)  and  Boore et al. (2021) , near-source datasets of  Pacor et al. (2018)  and  Sgobba et al. (2021b) , etc. showed recorded short-period  of   ≥ 7 to be lower than smaller magnitude events. In fact,  Cauzzi et al. (2015)  provided an explicit adjustment to the magnitude scaling of their GMM if one prefers to ignore the apparently data-driven oversaturation at higher magnitudes. Even the recently published dataset of synthetic near-source ground-motions BB-SPEEDset by  Paolucci et al.
(2021)  also shows an on-average lower short-period  for   ≥ 7. Boore et al. (2014)  refer to the simulations of  Schmedes and Archuleta (2008)  to support the apparent oversaturation of short-periods , which was avoided in their earlier  Boore and Atkinson (2008)  GMM. Here we mean to say that, oversaturation is apparent in several datasets, but the reasons as to why could be from: 1) very poor sampling at near-source distances from the rare large magnitude events, 2) finiteness of large ruptures and location of recording stations relative to the largest asperity, i. e. stations with   ,   = 0 but   ,  ℎ ≠ 0, 3) location of recording stations in the areas receiving lower shear-wave radiation energy (due to anisotropy) or directivity effects (e. g. Dujardin et al. 2018; Kotha et al. 2019) , 4) regional variability of large ruptures due to tectonic diversity, i.
e. stress loading and release rates, maturity of fault systems, etc. , as reported in  Radiguet et al. (2009)  and  Chounet et al. (2018). Of all these hypothesis, the most favourable one could be that large characteristic ruptures of   ≥ 7 are likely to occur on the same areas of the fault plane with lower fault friction, i. e. the mature sections of the fault plane, and thereby releasing lower amount of high frequency shearwave energy than smaller ruptures occurring in the less mature sections. In addition, rupture directivity and Doppler's effect could have caused a stacking of high-frequency pulses into a single low frequency pulse-like ground-motions, leading to higher low frequency ground-motions and lower high frequency ground-motions. These effects need to be investigated thoroughly using the recently published near-source strong motion datasets (e.
EVALUATION WITH NESS DATASET So far in this study, we have re-derived and revised the  Kotha et al. (2020)  GMM using the ESM dataset. The revised model is a clear improvement with regards to explaining the scaling of () with   of pan-European Fig. 8  Between-event random-effects versus   of the events in ESM dataset at T = 0. 01, 0. 1,1, 2 when  ℎ = 5. 5, 5. 7,6. 2,6. 5 in the four  ℎ -trial GMMs shallow crustal events; most of which originate in Italy, Turkey, and Greece. However, it still remains to be seen how this revision performs against a complementary dataset with high quality ground-motion recordings in nearsource distances of large earthquakes from across the globe. For this purpose, we make use of the recently published Near Source Strong-motion datasets (NESS1 by  Pacor et al.
DATASET NESS dataset is an impressive collection of 1189 high-quality ground-motion recordings from global large magnitude events with 5. 5 ≤   ≤ 8. 1 recorded in near-source distances 0 ≤   ≤ 138. Along with various ground-motion intensity measures of engineering interest, NESS also provides advanced event metadata (e. g. centroid moment tensor solutions, fault geometry, stress-drop, etc. ) and distance metrics (e. g. nucleation depth, fault parallel and perpendicular distances, etc. ). These metrics are quite useful in exploring the near-source finite fault effects, such as directivity, shear-wave radiation pattern, and pulse-like ground-motions as in  Sgobba et al. (2021a). Moreover, the NESS2 dataset is compiled using the same processing methodologies as those of ESM; and hence, complement each other seamlessly.
We refer the readers to  Pacor et al. (2018)  and  Sgobba et al. (2021b)  for elaboration on the dataset. Fig. 1  compares the   -  distribution of data between NESS and ESM datasets. It is evident that NESS has much more data from hazard critical scenarios. However, we note that there are around 177 records, and 39 events common to both the datasets. Of the 34 recordings exclusive to NESS but from events present also in ESM, 15 are from the M6. 5 Norcia, Italy earthquake of 30 th October, 2016; 10 from the M7. 6 Izmit, Turkey earthquake of 17 th August, 1999; and one each from 9 other events. In this study, we chose to discard all NESS records or events that are already present in ESM; amounting to 211 recordings. In addition, 8 recordings associated to the M8. 1 Iquique, Chile earthquake of 1 st April, 2014 have also been removed because it might have been wrongly classified in NESS as shallow crustal event while being an interface event.
RANDOM-EFFECTS AND RESIDUALS To evaluate the performance of the  Kotha et al. (2020)  GMM and its revision presented here, we perform a randomeffect and residual analyses of the NESS dataset. Instead of the customary plots showing GMM fixed-effects (median) prediction against observations, we choose to present and analyse the trends of random-effects and residuals against predictor variables   and. For this purpose, we perform an ordinary least-square randomeffect decomposition as shown in equation below: 𝑙𝑛(𝑂𝑏𝑠 𝑒,𝑠 ) -𝑙𝑛(𝜇) = 𝑜𝑓𝑓𝑠𝑒𝑡(0) + 𝛿𝐵 𝑒 + 𝛿𝑊 𝑒,𝑠 (5) In Eq. (  5 ), ( , ) are the natural-log of () in the NESS dataset recorded for event  by site , and () is the fixed-effects GMM prediction for that event at that event-to-site distance (in   ). Since there are two GMMs, () will also be differentone with  ℎ = 5.
This means that, all the site-effects are redistributed among betweenevent   and within-event residuals  ,. Now that there are two GMMs we are evaluatingrevised GMM with  ℎ = 5. 7 and original GMM with  ℎ = 6. 2against the NESS datasets, we have two sets of   and  , for , , and ( = 0. 01 -8). Similarly, for the ESM dataset, we have two sets of  , 0 and. Note again that the  , 0 and  are filtered for tectonic locality-, attenuation region-, and site-specific effects, while   and  , are not. Therefore, although these are not technically the same quantities, they are still comparable. Fig. 9  plots the between-event random-effects against   of the   ≥ 5 events in ESM and NESS dataset, estimated with respect to the GMMs with  ℎ = 5. 7, 6. 2. Of course, the localized non-parametric trend (loess by Jacoby 2000) always stays close to zero for the ESM data because the models were calibrated on it.