dataesr/scientific-paragraphs-categorization huggingface dataset:
• The marginals of all random-effects groups Δc 3,r = Ɲ(0, 𝜏 𝑐3 ), Δ𝐿2𝐿 𝑙 = Ɲ(0, 𝜏 𝐿2𝐿 ), Δ𝐵 𝑒,𝑙 0 = Ɲ(0, 𝜏 0 ), and Δ𝑆2𝑆 = Ɲ(0, 𝜙 𝑆2𝑆 ) are restricted from being updated. The random-effect values of the levels within each groupsay, the δc 3,r of a region 𝑟 in Δc 3,r = Ɲ(0, 𝜏 𝑐3 ) groupare estimated with respect to their group variance. In doing so, for example, the δc 3,r values of regions common to both datasets are not updated but re-estimated using the new Résif data; and are compared to their ESM based estimates in Supplementary Figures. This has been decided after several trials, but there is no clear reason as to whether they should be or not. The only justification is that, allowing one or more of these parameters to update led to instabilities in regression across the spectral period range.
Kotha et   2022 ) argued that the uncertainty on 𝑏 2 and K20 predictions at 𝑀 𝑊 ≤ 4 is large due to sparse calibration data in ESM, and the errors in 𝑀 𝑊 of small events. Although errors in 𝑀 𝑊 of small French events persist in Résif, the 10000+ records from 𝑀 𝑊 ≤ 4 have worked in favour of reducing the uncertainty in 𝑏 2 values. • 𝑏 3 fixed-effects coefficient controls the scaling of ground-motions at 𝑀 𝑊 > 5. 7. Since the [𝑀 𝑊 , 𝑅 𝐽𝐵 ] range of Résif dataset falls short, 𝑏 3 has remained the same. • 𝑐 1 , 𝑐 2 fixed-effect coefficients controlling the geometric attenuation exhibit only marginal changes in their median values (Fig. 5  and left panel of Fig. 7 ) but have lower uncertainties than in K20. The update has mostly unnoticeable impact on predictions at near-source distances (e. g.
This increase in 𝑒 1 combined with decrease in 𝑏 2 rendered only minor changes in predictions in the range 3. 5 < 𝑀 𝑊 < 4. 5, but led to an increase in GMM median predictions by up to 50% at 𝑀 𝑊 > 5. 7. Since that would be an update difficult to substantiate, the decision to restrain 𝑒 1 from updating was considered more defensible. Similarly, allowing 𝑐 3 to update also increased its values. However, the updated (less negative) 𝑐 3 in combination with ESM based 𝛿𝑐 3,𝑟 values would have suggested anelastic amplification at far-source distances in a few ESM regions. Therefore, not allowing 𝑐 3 to update was considered appropriate. It is important to note again that, the restrains chosen in this study are appropriate for this dataset, but may need revision for other datasets.
Therefore, recognising the need for better refinement on regionalisation of France, this study will be followed-up by an evaluation and redesign of the regionalisation models used in K20. Meanwhile, this update suggests that far-source attenuation in France is on average always weaker/slower than elsewhere in pan-European region covered by ESM, and especially, in comparison to the stronger/faster attenuation inferred for the highly active regions of Central Italy. Consequently, ergodic GMMs developed using ground-motions recorded primarily at stations located in Italy may severely under-predict far-source ground-motions observable in France. 4. 2. 2 Tectonic variability: ∆𝐿2𝐿 𝑙 = Ɲ(0, 𝜏 𝐿2𝐿 ) Fig. 10  shows the 𝛿𝐿2𝐿 𝑙 values of the various tectonic localities estimated using ESM (top panels) and Résif (bottom panels) datasets at spectral periods 𝑇 = 0.
(2021)  (left panel of Fig. 4 ) to regionalise (or localise) ground-motion data and quantify source related attenuation characteristics in the ESM dataset. In those studies, the 𝜏 𝐿2𝐿 took larger valuesindicating a greater regional variabilityat short spectral periods (𝑇 < 1𝑠) and high frequencies (𝑓 > 1𝐻𝑧). However, random-effects and residual analyses showed that the ∆𝐿2𝐿 𝑙 = Ɲ(0, 𝜏 𝐿2𝐿 ) were poorly correlated to any available physical parameters at that time. For instance, ∆𝐿2𝐿 𝑙 showed no correlation with  Brune (1970)  stress-drop estimated by  Bindi and Kotha (2020)  of events originating the tectonic localities. ∆𝐿2𝐿 𝑙 showed some negative correlation to Activity Index (𝐴𝐼𝑥) of  Chen et al. (2018) , which in-turn is a fuzzy combination of 1Hz coda Q, crustal shear-wave velocity at 175km, and seismic moment rate density parameters.
(2021)could in fact be related to intrinsic absorption quality-factor 𝑄 𝑖 /𝑄 𝑚 at the earthquake source location and the Earth's crust immediately around its hypocentre. Therefore, in this study, the tectonic localities random-effects group is hypothesised to be capturing the at-source attenuation characteristics. Based on this hypothesis, evidently, the earthquakes originating in the Brittany region with 𝛿𝐿2𝐿 𝑙 > 0 suffered weaker at-source attenuation by virtue of the region's lower intrinsic absorption as indicated by its higher 𝑄 𝑖 /𝑄 𝑚. Supplementary to However, it appears that the scatter of 𝛿𝐵 𝑒,𝑙 0 of 𝑀 𝑊 < 4 Résif events is rather narrow, suggesting that the between-event variability of such small events is in fact lower than that of 4 < 𝑀 𝑊 < 5. 7 events in ESM dataset. This observation is counter to the general consensus on heteroskedastic 𝑀 𝑊 -dependent betweenevent variability 𝜏 0 (𝑀 𝑊 ) (e.
Supplementing Fig. 11 , Fig. S3  compares 𝛿𝐵 𝑒,𝑙 0 of the events identified by Laurendeau, Clément and Scotti (2022) as common to both datasets. While majority of events have similar 𝛿𝐵 𝑒,𝑙 0 values inferred from the two datasets, the events with largest differences in their 𝑀 𝑊 sourced from EMEC in ESM and Si-HEX in Résif datasets also show the largest differences in their 𝛿𝐵 𝑒,𝑙 0 values. In addition, there appears also an inverse relation, wherein ESM inferred 𝛿𝐵 𝑒,𝑙 0 values are larger than Résif inferred 𝛿𝐵 𝑒,𝑙 0 when ESM assigned 𝑀 𝑊 are lower than Résif assigned 𝑀 𝑊although not strictly. Laurendeau and Kotha (2023)  analysed in detail the impact of 𝑀 𝑊 homogenisation/unification on the between-event variability of  Kotha, Bindi and Cotton (2022)  Fourier GMM derived from ESM, and discussed the sources of such 𝑀 𝑊 and 𝛿𝐵 𝑒,𝑙 0 discrepancies.
Fig. 13  visualises the 𝑀 𝑊 -𝑅 𝐽𝐵 dependence of Résif record-to-record variability 𝜙 0,𝑀,𝑅 at 𝑇 = 0. 01, 0. 1, 1𝑠. Here, 𝜙 0,𝑀,𝑅 is the standard-deviation of residuals in the 900 evenly spaced 𝑀 𝑊 -𝑅 𝐽𝐵 bins defined solely for visualisation and discussion purpose. The numerous 𝑀 𝑊 -𝑅 𝐽𝐵 bins with grey colour are those where 𝜙 0,𝑀,𝑅 is equal to the generic 𝜙 0 for that specific period. Moderate deviations of 𝜙 0,𝑀,𝑅 from 𝜙 0 appear at 30 < 𝑅 𝐽𝐵 < 100𝑘𝑚, where the Résif dataset is densely sampled. Stronger deviations appear at 0 < 𝑅 𝐽𝐵 < 30𝑘𝑚, where the dataset is poorly sampled. However, at this resolution of 𝑀 𝑊 -𝑅 𝐽𝐵 binning, the deviations are rather irregular between adjacent cells, with steep changes in 𝜙 0,𝑀,𝑅 ; plus, choosing larger 𝑀 𝑊 -𝑅 𝐽𝐵 bins show similar trends with no additional insights.
DISCUSSION The parametric K20 ground-motion models were developed from the pan-European ESM dataset, quantifying various repeatable physical phenomena as mixed-effects. These models were intended to be evaluated, updated and adapted to new regions as and when new ground-motion datasets become available. Regression of a dataset is essentially a condensation of knowledge it provides into an interaction of a few predictable physical parameters. This condensed knowledge can be used as prior information that can be validated and updated with new data as likelihoods. Such an approach to ground-motion prediction is more aptly handled via a Bayesian framework. In this work the first step was to recast the regression of ESM into K20 GMMs in a Bayesian framework; in order to validate the knowledge gained from regression of ESM against Résif ground-motion data, and update as necessary.
The Bayesian linear mixed-effects regression of ESM dataset yielded a version of K20 that can be used interchangeably with the published version. While the fixed-effects components of the two versions produce identical median ground-motion predictions, the random-effects and residual components (standard-deviations) are not identical. The published random-effects estimates were from a computationally intense robust regression that iteratively down-weight outlier data, but were not replicable with current capabilities of the INLA algorithmalthough this may change in the near future. For the time being, the marginal distributions of mixed-effects inferred from ESM dataset were used as priors to update the K20 with Résif ground-motion data. In process, a few major assumptions had to be made. There are events, stations, and associated ground-motion records common to both datasets.
These were few in number, and were retained in the update process. The 𝑀 𝑊 of events in ESM and Résif datasets are sourced from different catalogues, and based on different selection criteria. It is unreasonable to expect that newer datasets will share the same preferred 𝑀 𝑊 selection criteria as ESM. Therefore, 𝑀 𝑊 reported in Résif dataset were used as are. Among the planned activities is a unification/homogenisation of 𝑀 𝑊 across datasets, and advance this study into Fourier domain as well. ESM dataset regression considered only the surface strong-motion sensors, while all Résif stations were used, irrespective of their installation conditions, to enhance the spatial coverage. The resulting sitespecific random-effects can be investigated if this selection criteria can be justified. In this study, there is no such detailed analysis because nothing remarkably peculiar was noticed.
However, the outcomes from this study can be used to develop a more application-ready version of the GMM; with revised decisions on which sites should be retained in, for example, developing a companion empirical site-response model for France. Multiple trial regressions were made with various restrains on the mixed-effects, allowing them to be updated or not. Ultimately, a rather restrained approach was chosen wherein some of the fixed-effects known to be strongly correlated are locked to their prior values, allowing the others to absorb the possible differences in physical process across the two datasets. The first important outcome, in this approach, is a large correction to the quadratic scaling of ground-motions with 𝑀 𝑊. While the model inferred from ESM suggested a counter-intuitive scaling towards small 𝑀 𝑊 , the Résif update rectified it to a more analytically agreeable scaling.
ESM dataset had barely enough data to quantify these wellknown regional variabilities in attenuation within France. Résif dataset has tremendously increased the capability of K20 to make partially non-ergodic region-specific ground-motions in France; and not-tomention, site-specific predictions now possible at several dozen new locations all over France. The maps showing spatial variabilities of far-source and at-source attenuation are interesting but offer little insight on the underlying physical processes. Particularly, the attenuation regionalisation model used to quantify far-source attenuation variabilities are in urgent need of refinement. The model used to quantify at-source attenuation variabilities are at-times consistent with crustal tomography maps available for France. This means that, the hypothesis on at-source attenuation may hold-but further investigation is necessary.
The 𝛿𝐵 𝑒,𝑙 0 (𝑇 = 0𝑠) versus 𝑀 𝑊 plots under trial 1 and trial 2 are quite similar; with the scatter at 𝑀 𝑊 < 4 being as large as at 4 < 𝑀 𝑊 < 5. 5. The 𝑀 𝑊dependent heteroskedastic models either of K20 or ESHM20 can be used as is with this update. The large 𝛿𝐵 𝑒,𝑙 0 (𝑇 = 0𝑠) versus 𝑀 𝑊 in trial 1 and 2 may be indicative of the well-known uncertainties and regional variabilities in 𝑀 𝑊 estimation procedures of Résif events. At the time of this study, there was no remedy for this issue. A similar analysis in Fourier domain may offer more insights on trial 2. The usable Résif ground-motion data with good signal-to-noise ratio falls rapidly at 𝑇 > 0. 3𝑠. The sudden changes in the dataset composition may have caused convergence issues, leading to the instabilities in random-effects standard-deviations around 𝑇 = 1𝑠; otherwise, trial 2 would be as acceptable as the preferred trial 3 in this studyif not more.
Restraining all the random-effects variances to their ESM inferred priors led to trial 3, which clearly shows the largest 'left-over' residual variabilities 𝛷 0 at all spectral periods. The running hypothesis, in this study, is that the additional Résif ground-motion variability is absorbed into residual variability 𝛷 0 , suggesting a high record-to-record variability for a few events and stations. The bottom panels of Fig. 16  show 𝛷 0,𝑀,𝑅 across the three trials at 𝑇 = 0𝑠 , which is essentially the 𝑀 𝑊 -𝑅 𝐽𝐵 dependent residual variability of 𝑃𝐺𝐴𝑠 in Résif dataset. The redistribution of residual ground-motion variabilityfrom trial 1 to 3appears to have accumulated in 30 < 𝑅 𝐽𝐵 < 100𝑘𝑚 range across the entire 𝑀 𝑊 range. Further investigation showed that the record-to-record variabilities are systematically larger for events located in the complex tectonic environment of the Alps mountain ranges, and for stations located in the Pyrenees mountain ranges.
There are a few options to reduce or remodel the residual variability 𝛷 0 , in order to mitigate its impact as a component of aleatory variability (𝜎) in PSHA. But these options can be explored in a more application-oriented manner; similar to the implementation of K20 GMMs in ESHM20. This study presents the K20 GMMs in a Bayesian framework, with a more formalised updating procedure. New ground-motion datasets are steadily becoming more available following the demonstration of impact of GMM uncertainties in ESHM20, especially for low-moderate seismicity regions of pan-Europe. These new datasets can serve to extend the non-ergodic application of K20 GMMs to newer regions; but more significantly, in validating existing ground-motion models themselves. It has often been the case that, numerous new GMMs supersede earlier models derived from older, more limited datasets.
4. 2. 1 21 Fig. 9 shows the 𝛿𝑐 3,𝑟 values of the various far-source attenuation regions estimated using ESM (top panels) and Résif (bottom panels) datasets at spectral periods 𝑇 = 0. 01, 0. 1, 1𝑠 (left-to-right). Clearly, the regions newly populated with Résif data (right panel of Fig. 4) now have region-specific 𝛿𝑐 3,𝑟 values; which were to be otherwise assigned the pan-European average 𝑐 3 values with 𝛿𝑐 3,𝑟 = 0 in equation (3).
FTIR The total columns and volume mixing ratio profiles of trace gases such as CO are retrieved with high accuracy and precision with ground-based Fourier Transform Infrared (FTIR) spectrometers  (Clerbaux et al. , 2008; Vigouroux et al. , 2015; Zhou et al. , 2019). In the present study, we use the FTIR observations performed at Lauder and Reunion sites in the framework   2018 ) and  Wunch et al. (2015). A brief description is given hereafter. The CO measurements from FTIR at Lauder have been made since the early 1990s. The measurements are made using a Bruker high-resolution spectrometer over a wide spectral range (around 600-4500 cm -1 ). The CO dataset used in this study is the same as that used by  Bègue et al. , (2021)  and  Kloss et al. , (2019). Details on the spectral measurements, CO retrieval strategy and derived CO column abundances can be found within these references.
Acknowledgements We thank  Isamu Morino  and his staff for their effort in establishing and maintaining the sunsky radiometer at Lauder. Data used in this publication were obtained from NDACC network and are available through its website (http://www. ndacc. org/). The work of  S. Khaykin  has been supported by the  Agence Nationale de la Recherche PyroStrat  project ( 21-CE01-335 0007-01 ). IASI was developed and built under the responsibility of the " Centre National d'Etudes Spatiales" (CNES, France)  and flown on board the MetOp satellites as part of the  EUMETSAT Polar System. The authors thank the AERIS infrastructure (https://iasi. aeris-data. fr/) for providing access to the IASI data, the  National Aeronautical and Space Administration (NASA)  for providing CALIOP data, MLS and MODIS fire products.
We would especially like to thank the staff of the team working on the lidar systems at the Maïdo observatory. The  French Research Infrastructure ACTRIS-FR ,  CNES  and the  EU  project  REALISTIC  ( GA 101086690 ) are also acknowledged for their support in the upgrade and operation of the Maïdo observatory lidars. Lauder observations are funded by the  New Zealand Government 's  Strategic Science Investment   Fund (SSIF) , administered by the  Ministry of Business, Innovation and Employment (MBIE). The TCCON site at Réunion Island has been operated by the  Royal Belgian Institute for Space Aeronomy  with financial support since 2014 by the  EU  project  ICOS-Inwire , the ministerial decree for  ICOS  ( FR/35/IC1  to  FR/35/C6 ),  ESFRI-FED ICOS-BE  project and local activities supported by  LACy/UMR8105  and by  OSU-R/UMS3365 -Université de La Réunion.
Data availability The data used for this study are available and open access by request to scientist mentioned or through the link hereafter: Lidar measurements (tetsu@mri-jma. go. jp, nelson. begue@univreunion. fr), radiometer measurements (ben. liley@niwa. co. nz), FTIR measurements from TCCON network (mahesh. sha@aeronomie. be); Lauder FTIR data available on the NDACC public access database (https://www-air. larc. nasa. gov/missions/ndacc/data. html); The satellite observations and emission inventory used are available on-line from the sources as stated in the manuscript.
2. 1 1 Aerosols data sets https://doi. org/10. 5194/egusphere-2023-1946 Preprint. Discussion started: 31 August 2023 c Author(s) 2023. CC BY 4. 0 License. The aerosol datasets used in this study resulted mainly from two ground-based observations sites from the Network for the Detection of Atmospheric Composition Change (NDACC, www. ndacc. org) network as well as a suite of spaceborne sensors products.
No cation exchange was performed. Diffracted beam CuKa 1+2 radiation was used (40 kV, 40 mA). Relative humidity was not controlled during data acquisition. Less than 2 lm clay size fractions from representative samples were extracted by sedimentation and treated with calcium saturation to permit a better identification of the type of swelling mixed-layer minerals present in the sample. A part of the clay fraction of the selected samples was prepared for randomly oriented powder mounts in order to determine the (0 6 0) spacing which is diagnostic for recognition of the di-, di-tri or trioctahedral minerals. Representative samples were selected for scanning electron microscope (SEM) observations, which were made on small freshly fractured bulk rock samples and on polished thin sections that were previously coated with carbon.
Acknowledgements We are grateful to  Ariel Testi  for his collaboration during fieldwork. The research was supported by  European Commission  through its  Erasmus Mundus Internship program , which allowed ALR to conduct the clay mineral analysis at the  IC2MP (Université de Poitiers) , FONCyT (PICT No. 2010-2608),  CONICET  (PIP No. 6043, PIP No. 1083, and PIP No. 330),  Universidad Nacional del Comahue  ( PIP No. 04/I167 ),  Universidad Nacional del Sur (SECyT  through its  Internship program in Research  and  PGI 24/H144 ). The authors also acknowledge financial support from the  European Union (ERDF)  and  Région Nouvelle Aquitaine. We thank the two anonymous reviewers and Associate Editor  Prof. Manikyamba  for their constructive reviews and helpful suggestions and Editorial  Advisor M.
Acknowledgments We would like to thank the  British Antarctic Survey (BAS)  and  Alfred Wegener Institute (AWI)  staff for their field and logistical support at  Halley Station  and  Kohnen Station , respectively. Technical support for ice core measurements at BAS were provided by  Rebecca Tuckwell ,  Lisa Hauge ,  Julius Rix ,  Catriona Sinclair ,  Emily Ludlow , and  Shaun Miller. The ISOL-ICE ice core project was funded by a  Natural Environment Research Council (NERC)   Standard Grant  ( NE/N011813/1 ) to M. M. F. V. H. L. W. was supported by the  NERC Standard   Grant , and a  Rutherford Foundation Postdoctoral Fellowship  ( RTF-VUW1801-PD ) and  Rutherford Discovery Fellowship  ( RDF-VUW2203 ) administrated by the  Royal Society Te Apārangi. K. R. C. acknowledges support from the  Royal Society Te Apārangi Marsden Fund  ( MFP-VUW2010 ).
1 is an emission inventory developed as part of the Copernicus Atmosphere Monitoring Service (CAMS) to support European scale air quality modelling  (Kuenen et al. 2022 ). The inventory builds on the officially reported emission data to EMEP in the year 2020, which are complemented by other sources where reported data are not available or deemed of insufficient quality. The data are spatially distributed consistently across the entire domain at a resolution of 0. 05x0. 1 degrees (lat-lon). The spatial distribution takes into account specific point source emissions as reported in the European Pollutant Release and Transfer Register (EPTR2022) to correctly represent point source emissions to the extent possible. The emissions are provided in GNFR format. The emission dataset is used in support of the CAMS regional modelling activities, but is also publicly available to support air quality assessment at European level.
Input data In this section, we use the local inventory for Poland and compare it to the Europe wide ensemble. The Central Emission Database (CED) is a local emission inventory designed for Polish national air quality modelling. The CED is based on source location and provides accurate resolution-free data, which can be gridded depending on the requested target resolution for different computational grid configurations over Poland (typically 2. 5 km over the entire country and 0. 5 km for agglomeration zones). The majority of data is processed with respect to its exact geographical localisation. The intention behind CED is to include documented emission sources in Poland. Since the inventory is fairly new (the first version was ready in 2019), priority was given to the most critical sectors, like residential combustion (described in detail in  Gawuc et al.
, 2021)  and road transport. The road transport data presented in this paper (topical for 2019) was based on traffic models for the major roads in the country. Emissions on minor roads were distributed using the residue values taken from subtracting emission on major roads from the national totals. Current methodology (topical for 2022) is based on smartphone car navigation app which provides GPS data on road traffic and annual average car speed. One of the essential components of CED is the "National database on greenhouse gases and other substances emission" (so-called national database -NB). NB consists of information on installations and sources' location responsible for emission into the atmosphere. NB has similarities to E-PRTR, but unlike it, it covers all emission sources regardless of type, power or production level.
Moreover, spatial patterns of the increments calculated in the assimilation procedure let to identify and improve the assumptions behind CED. The database is updated every year and there is a continuous attempt to improve emission estimates both -for total load and spatial distribution of sources. Modelling results helped to identify missing sources (e. g. resuspension, underestimated agriculture sector, domestic water heating). All sectors in CED are constantly improved using the best available activity data. The comparison between CED and ensemble data is performed on 14 cities, 5 sectors and 6 pollutants, leading to 420 emission ratios being tested. Among these 420 available data, 84 only remain after the relevance test (𝛾𝛾 𝑡𝑡 > 0. 5). These 84 [p,s] points serve as basis to identify inconsistencies (𝛽𝛽 𝑡𝑡 > 2).