dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018 , Oreggioni et al. 2022). While this difference in approach can explain a large number of inconsistencies identified for EDGAR, some of them are very large, especially for SO2 and PM in the industrial sector. For this particular sector, estimates mostly come from the LPS and E-PRTR databases in EMEP/CAMS-REG, which emissions are mostly based on measurements or facility-level estimates. Such information is not used in EDGAR where estimates are based on fuel consumption and emission factors that are very general and not plant specific. The screening analysis allowed identifying some of the causes behind these differences (e. g. outdated sources and/or emission factors) that need to be improved in EDGAR. EU-wide, spatial inconsistencies mostly occur for the industry and "other" sectors.
https://doi. org/10. 5194/egusphere-2023-1257 Preprint. Discussion started: 28 August 2023 c Author(s) 2023. CC BY 4. 0 License. built from officially reported data provided to CEIP (Centre of Emission Inventory and Projection by the Member States in Europe) and follow the EMEP/EEA guidebook guidelines (EMEP/EEA 2019) to define the annual totals. The emissions are gap-filled with gridded TNO data from Copernicus Atmospheric Monitoring Service (CAMS) and EDGAR. The dataset consists of gridded emissions for SOx, NOx, NMVOC, NH3, CO, PM2. 5, PM10 and PMcoarse at 0. 1° x 0. 1° resolution. More information on the emissions and where to download can be found in the User Guide (https://emep-ctm. readthedocs. io/en/latest/) and in Mareckova et al. , (2017).
Code and data availability. Supporting data and source code are available at: "Philippe Thunis. (2023). Supporting data for the publication "Emission ensemble approach to improve the development of multi-scale emission inventories" [Data set]. Zenodo. https://doi. org/10. 5281/zenodo. 7940402". Author contributions. PT and AC contributed to the study conception and design. Material preparation, data collection and analysis were performed by PT, EP, ADM, JK, MB, LG, KS, and AC. All authors reviewed the manuscript. All authors read and approved the final manuscript. Competing interests. The authors declare that they have no conflict of interest.
We parameters) suggesting negligible fractionation between melt and refreeze episodes of samples or interlaboratory differences between the various analysis campaigns. A manuscript detailing the full isotope dataset is under preparation. 2. 4 Continuous flow analysis of trace chemistry, peroxide, dust and water stable isotopes Sample sticks (34 x 34 mm) for continuous flow analysis (CFA) sectioned from the MBS Main core and the Charlie surface core were shipped frozen to Copenhagen to be analysed via CFA at the Niels Bohr Institute  (Kaufmann et al. , 2008; Bigler et al. , 2011). We used a modified version of the Copenhagen CFA system  (Bigler et al. , 2011; Kjaer et al. , 2022)  during two campaigns, one which analysed the dry-drilled sections of the Main core (4-94m) and the Charlie core (CFA1: November 2018), and a second which analysed the deeper wet drilled section (95-295m) of the Main core (CFA2: November 2019).
The measurement noise as inferred by the integration of the power spectral density of the δ 18 O and the δD signals is 0. 08 and 0. 43 ‰ respectively. The mean accuracy of the record, calculated using a "check" water standard is 0. 03 and 0. 3 ‰ for δ 18 O and δD respectively. A detailed description of the CFA water isotope dataset is in preparation. 2. 5 Inductively coupled plasma sector field mass spectrometry of the halogens sodium, calcium, iodine and bromine Discrete subsamples taken every 25 cm from the Copenhagen CFA campaigns were transported frozen to the Ca' Foscari University (Venice, Italy) and stored at -20 • C. Total Na, Ca, bromine (Br) and iodine (I) concentrations were determined with an Inductively Coupled Plasma Sector Field Mass Spectrometer (ICP-SFMS, Thermo Scientific™ ELEMENT2™) equipped with a cyclonic spray chamber.
1 ng g -1 (Na), 3. 6 ng g -1 (Ca), 0. 05 ng g -1 (Br) and 0. 002 ng g -1 (I). The repeated samples showed a variability of 2. 1% (Na), 4. 5% (Ca), 3. 1% (Br) and 7. 7% (I) with respect to the original measurement. The paleoenvironmental value of this dataset from the MBS site is under investigation, however for the purposes of this study, we present the annual mean halogen concentrations at MBS compared to those from the Law Dome ice core. The MBS ice cores contain numerous stratigraphic features, particularly thin crusts. These crusts are qualitatively similar to bubble free layers identified elsewhere in Antarctica and Greenland, and they are distinct from melt layers in appearance by being thin (1-2 mm thick) and sharply defined rather than thick and ragged in appearance. In the WAIS Divide ice cores, such bubble free layers have been determined to be unrelated to melt events via noble gas analysis  (Orsi et al.
785 metres, which is 18. 5 cm different from the field measured depth of 294. 6 metres (i. e. a 0. 003% error). This suggests that despite the discrepancies between field and laboratory measurements, the ultimate depth of the core was measured accurately and individual core discrepancies will have limited impact on sample-depth registration. For the CFA analysis of trace impurities and water isotopes, a process of scaling and shift factors was derived to account for length discrepancies. This was necessary as the length of the CFA analysis sticks sent to the University of Copenhagen were sometimes shorter or longer (by up to 0. 5 cm) than the lengths of the original cores, depending on their orientation to any slanted end cuts in the whole core. The scaling and shift factors will be described in detail as the CFA trace chemistry and water isotope datasets are developed and published.
The MBS fluoride record presented here is an interim dataset, with some missing sections due to variability resulting from the very low concentrations present at MBS and possible losses in the firn section during storage. We present fluoride seasonal cycles covering time periods of adequate sample fidelity from the MBS Main and Charlie cores (Figure  5 ). These show that fluoride has a minimum in early summer (November/December) and an autumn maximum (March/April/May). Note that the satellite era fluoride seasonal cycle was derived without reference to fluoride as an indicator of an annual horizon, as the satellite era age scales were developed in  Crockart et al. (2021)  prior to examination of the fluoride record. Table  3. Proposed volcanic dates and depths from the MBS ice core. The MBS record has been synchronised to WAIS Divide  (Sigl et al.
Concentrations are shown in the units used during layer counting (µEq L -1 ). From top to bottom of each row are seasonal cycles for the Main core non-satellite era  (872-1978 CE) , the Main core satellite era  (1978 ( -2008 CE) CE)  and the Charlie surface core  (1966 ( -2017 CE) CE). For fluoride, seasonal cycles were calculated over  962-1798 CE (top), 1978 -2008 (middle) and 1966 -2012 (Charlie)  due to an incomplete dataset available to this study. The black line in each panel represents the mean value for that month for the time period selected, assuming uniform accumulation. The coloured upper and lower bands are the standard error of the mean. For orientation, vertical orange dotted lines indicate the annual horizon date of 1 January. For detailed analysis of water stable isotope ratios, their relationship to seasonality and the influence of biases due to moisture intrusions, see  Jackson et al.
A final key difference between MBS and Law Dome is the clear seasonality of fluoride concentrations at MBS, however this difference relies on the development of a robust fluoride dataset from Law Dome to substantiate differences between the two sites. During layer counting, we observed periods of multiple years where annual horizons changed from consistently easy to discern to periods with unclear horizons (see Appendix B1). These periods may be related to years with either lower than average snowfall accumulation, or higher than average episodic accumulation, resulting in an unclear seasonal cycle. Our approach using interim age scales allows the estimation of uncertainty between volcanic tie points as a result of uncertain or unidentified annual layers in the layer-counted only MBS HK (Figure  6 ).
However, we think this error has some mitigating factors. Firstly, the greatest 'loss' of annual horizons (25 y) is accrued from the top of the core to the first major tie at Tambora (1816), a rate of 13 years per century. This suggests this was either a period of 'training' for the researcher who produced MBS HK , or, the higher number of samples per annual layer in the firn section led to more (rather than less) issues in identifying individual annual horizons. A second reason for the greater error in the upper 200 years is the issues with fluoride analyses in the top 80 metres of the MBS Main record (see methods). This meant fluoride was unavailable to assist with dating for this section. After this upper section, the error rate per century drops markedly to 3-6 years per century. We hypothesise that if a new MBS ice core was retrieved and a complete fluoride dataset available, the accumulated knowledge of the inherent idiosyncrasies of the site from this study would result in a much smaller error between the layer-counted effort and the final volcanic tie constrained chronology.
Conclusions Age-by-depth scales and preliminary results from the new Mount Brown South (MBS) ice cores from East Antarctica are presented. We used a four step approach to develop chronologies for the 295 metre 'Main' core, and three 20-25 metre surface cores, via layer counting of seasonally varying species and alignment to known volcanic horizons. The MBS 'Main' ice core    VF acknowledges support from Agence Nationale de la Recherche, projects ANR-20-CE01-0013 (ARCA), ANR-14-CE01-0001 (ASUMA).
Acknowledgements. We thank  Jason Roberts ,  Sharon Labudda  and  Bloo Campbell  for essential field work contributions, and  Adam Treverrow  for co-designing the ILCS laboratory and planer set-up. Financial Support This study was supported by the  Australian Government's Antarctic Science Collaboration Initiative  ( ASCI000002 ) through funding to the  Australian Antarctic Program Partnership. Logistics and analytical funding was provided by an  Australian Antarctic.
). Science grant (AAS 4414), the Australian Antarctic Division, the Carlsberg Foundation and a European Union Horizon 2020 research and innovation grant (TiPES, H2020 grant no. 820970). This study contributes to an Australian Research Council (ARC) Discovery Project (DP220100606) to TRV and NJA. NJA was supported by an ARC Future Fellowship (FT160102059), and NJA and SLJ were supported by the ARC Special Research Initiative Australian Centre of Excellence in Antarctic Science (SR200100008) and the Centre of Excellence for Climate Extremes (CE170100023). ASC received support from Polar Knowledge Canada. VG acknowledges support from the Villum 535 Foundation (project numbers 00022995, 00028061) and the Danish Independent Research Fund (DFF grant ID: 10. 46540/2032-00228B).
We unravel the various atmospheric processes and impacts interacting with one another during this compound event. This is done by combining numerous different datasets and expertise to provide a detailed analysis/overview of the March 2022 East Antarctic heatwave and place this event in context with other extreme Antarctic climate events observed. We describe: the sub-tropical origins and meteorological drivers that led to the intense AR (Section 3), the scale of the temperature records (Section 4), and the event's implications on the present and future Antarctic climate (Section 5), concluding with a discussion on the various compounding elements of this extreme event and the challenges of calculating a return time in an environment which large temperature variability and sparse observations (Section 6).
Data and methods a. Large-scale circulation, precipitation, melt, and moisture products Synoptic meteorological conditions leading to the AR during the heatwave event and the climatological context were analyzed with data from the two reanalyses ERA5  (Hersbach et al. 2020 ) and MERRA-2  (Gelaro et al. 2017). First, we used ERA5, produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), at a global 0. 25° resolution and a horizontal resolution of approximately 30 km with 137 levels. Variables including 500 hPa geopotential height (Z500), 2 m air temperature, precipitation, humidity, and horizontal wind were downloaded for 0000 UTC with daily resolution for the period 1979-2022, as well as at finer time resolution during the heatwave event. Investigation of tropical deep convection was however performed using the NOAA interpolated outgoing longwave radiation (OLR) dataset  (Liebmann and Smith 1996) , at 2.
91° C and Tmean anomaly = +38. 30° C). These results indicate that anthropogenic warming does not seem to significantly increase the likelihood of extreme temperature anomalies like those observed during March 2022, but further analysis with a larger range of models is needed to confirm this although models struggle to capture the anthropogenic warming signal over Antarctica  (Casado et al. 2023). These results, using a dataset of 700 years for historical period and 350 years for future period, confirm the exceptional nature of the March 22 event, even when climate change is taken into account. Further analysis with a larger number of ensemble members or models would be needed for a better quantification.
J. D. W. and V. F. acknowledge support from the  French Agence Nationale de la Recherche  projects  ANR-20-CE01-0013  (ARCA). C. Amory  and  C. Kittel  thanks the Computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS ). M. A. Lazzara  and  D. Mikolajczyk  are grateful for support from the  NSF Office of Polar Programs  under grants # 1924730 ,  1951720 , and  1951603. We also wish to thank  Bella Onsi  for her assistance. This project received grant funding from the  Australian Government  as part of the  Antarctic Science Collaboration Initiative program. I. J. Smith  and  G. H. Leonard  were supported by grant  MFP-UOO1825  from the  Marsden Fund Accepted  for publication in Journal of Climate.
DOI 10. 1175/JCLI-D-23-0175. 1. File generated with AMS Word template 2. 0 Council from government funding, administered by the  Royal Society of New Zealand , and the  Antarctic Science Platform  ( MBIE SSIF Programmes Investment  contract number  ANTA1801 ). C. A. Shields supported by  U. S. Department of Energy, Office of Science, Office of Biological & Environmental Research (BER), Regional and Global Model Analysis (RGMA)   component of the Earth and Environmental System Modeling Program ,  DE-SC0022070 ,  National Science Foundation (NSF )  IA 1947282 , and  NCART NSF Cooperative  Agreement No. 1852977. S. P. Alexander  was supported by the  Australian Antarctic Division 's  Australian Antarctic Science  projects 4387 and 4637. We thank the  Australian Bureau of Meteorology  and the  Australian Antarctic Division  for providing the AWS data from the Australian Antarctic stations.
S. González-Herrero  was supported by the research group  ANTALP (Antarctic, Arctic, Alpine Environments ;  2017-SGR-1102 ) funded by the  Agència de Gestió d'Ajuts Universitaris i de Recerca of the Government of Catalonia. S. -J. Kim  and  T. Choi  were supported by the  Korea Polar Research Institute (KOPRI)  project " Understanding of Antarctic climate and environment and assessments of global influence " (PE23030) funded by the  Ministry of Oceans and Fisheries. D. Udy  was supported by an  Australian Research Training scholarship  and an  Australian Research Council (ARC) Centre of Excellence for Climate Extremes  top-up scholarship. T. Vance  acknowledges support from the  Australian Antarctic Program Partnership  ( ASCI000002 ). D. U and T. V also acknowledge support from  ARC DP220100606.
I. Ollivier  was supported by the project  DEEPICE. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  955750. L. D. Trusel acknowledges support from  NASA   Cryospheric Sciences Program  (Award  80NSSC20K0888 ). N. Wever  was supported by the  Swiss National Science Foundation  ( SNSF  Grant  200020-179130 ). D. Bozkurt  acknowledges support from  ANID-FONDAP-1522A0001  and  COPAS   COASTAL ANID FB210021. File generated with AMS Word template 2. 0 Concordia Research Station. Radiosondes (Vaisala RS92) launched daily at around 1200 UTC at Concordia were used. The radiosondes provide vertical profiles of pressure, temperature, humidity and wind speed and direction through the troposphere and lower stratosphere with a vertical resolution of about 5 m.
2), or in both. In that case the effect can be enhancing (cis x trans, cat. 3) or compensating (cis + trans, cat. 4). No difference in parental expression with cis-and trans-regulatory differences in hybrids suggests a compensatory effect (cat. 5). When no differences are detected in both parents and hybrids, regulation is conserved (cat. 6). Any other combination was classified as ambiguous (cat. 7). The results were similar whether parental expression counts were computed from reads exclusively encompassing the variant position, or from reads encompassing the entire transcripts (=DEG results) (compare Figure  1EFG  and Figure  S1CD ). The majority of the 5573 genes analysed belonged to cat. 6 in both HybSF and HybCF datasets (Figure  1EF , black dots). Allele-specific expression in hybrids revealed that 167 genes in HybSF and 228 genes in HybCF belonged to cat.
1 (cis-regulatory divergence), respectively (Figure  1EF , red dots). Among these, 108 were shared (=2% of the 5573 genes studied; 547 variants on those 108 genes; Figure  1G ). We considered these 108 genes as strong cis-regulatory divergent candidate genes between Pachón cave and surface Astyanax, and of major interest for further analyses (list in Table  S1 ; full raw dataset in Table  S2 ). Very few genes were found in cat. 2 and cat. 3 (0 gene) or cat. 4 (cis + trans; 6 and 12 genes in reciprocal F1 hybrids, including 5 shared), suggesting that trans-regulatory evolution has limited impact on the evolution of cavefish development at tailbud stage (Figure  1G ). We concluded that cis-regulatory changes are the main drivers of variations in early developmental gene expression in the two Astyanax morphs at tailbud stage.
RNA-sequencing and analysis Library preparation and RNA-sequencing was done at the I2BC High-throughput sequencing platform (https://www. i2bc. paris-saclay. fr/spip. php?article399) using an Illumina NextSeq 500 sequencing instrument (paired-end). Analysis was performed on the European Galaxy server (usegalaxy. eu). Reads were mapped onto the Astyanax mexicanus 2. 0 genome (NCBI) with HISAT2 (version 2. 1. 0+galaxy4), after filtering (paired-end single match and Q>20). Reads were counted with htseq-count (version 0. 9. 1) and differential gene expression was determined with Deseq2 (  [47] ; version 2. 11. 40. 6). The full dataset is deposited as Sequence Read Archive (SRA) on NCBI under accession BioProject ID PRJNA848099. R scripts used in analyses below are available at https://github. com/LeclercqJ/Retaux-lab.
Variant calling and filtering Each RNA-seq experimental replicate originated from 2 in vitro fertilizations obtained from 1 female and 3 males. Therefore a maximum of 48 alleles were represented in each condition (4 individuals * 2 alleles * 2 FIV * 3 replicates). Variant calling was performed using FreeBayes (version 1. 3. 1, [48] ). Genes with variant(s) were identified using SnpEff  [49]  with custom Astyanax mexicanus database from SnpEff build and the NCBI GTF file, then filtered on quality >30 with SnpSift (version 4. 3). Reads containing fixed variants in parents were extracted using a custom R script and summed for a single gene in case of multiple variants. Parental expression (P) is the result of the combination of both cis-and trans-regulation. Cis-regulation (H) is estimated via allele specific expression in hybrid (i.
Fixed variant effect prediction Fixed SNPs were extracted from the VCF file using VCF-Bed intersect on Galaxy and variant effects were predicted with SnpEff (canonical transcript only, custom database). Nonsense variants and premature start variants were manually checked (see Results). Missense variants were analysed with a R custom script. Protein sequences were retrieved from NCBI with the rentrez R package and corrected for discrepancy between sequences and annotations (150 variants/75 genes corrected; 2 genes/5 variants removed). For each gene with a missense variant between surface and cave morphs, orthologous sequences from 9 other teleost species with annotated genomes (Danio rerio:  (https://doi. org/10. 1093/molbev/msm088) was used for the reconstruction of ancestral sequences. Finally, for each gene, the ancestral sequence of either cave or surface morph was used as input in MutPred2  [33]  to assign a deleterious score (g-score) to all missense mutations in these two lineages, which can vary between 0 (non-deleterious) and 1 (highly deleterious).
EF, Scatter plots of cis regulatory divergence (y axis; from allelic expression ratios in hybrids) versus parental divergence (x-axis; from DEG in parents), where the different categories of genes are color-coded. E is from HybSF analysis and F is from HybSF analysis (computed from reads exclusively encompassing the variant position). G, Venn diagrams showing the consistence of results obtained from HybCF or HybSF datasets and with the two calculation methods (at the variant position/. snp, or on the whole transcript/. deg).
EF, Scatter plots of cis regulatory divergence (y axis; from allelic expression ratios in hybrids) versus parental divergence (x-axis; from DEG in parents), where the different categories of genes are color-coded. E is from HybSF analysis and F is from HybSF analysis (computed from reads exclusively encompassing the variant position). G, Venn diagrams showing the consistence of results obtained from HybCF or HybSF datasets and with the two calculation methods (at the variant position/. snp, or on the whole transcript/. deg). A,.
We then assessed their mitochondrial (mtDNA) and nuclear genomic diversity. With the use of a Restriction site-associated DNA sequencing (RAD-seq) approach, which generates a reduced but wide representation of genomic diversity, we provided the first multispecies genomewide dataset for the Eliurus genus, aiming at validating and clarifying the Eliurus species tree topology. We performed several phylogenetic inferences and species delimitation tests,. CC-BY-NC-ND 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted October 24, 2022. ; https://doi. org/10. 1101/2022. 10. 21. 513246 doi: bioRxiv preprint 6 taking into account genomic and geographic variability within each taxon, as well as gene tree discordance.