dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. The authors are grateful to the anonymous referee for his/her careful reading and useful comments, which improved the content of this manuscript. ID, VS, JD, MN, and OM acknowledge the  European Union 's  Seventh Framework programme  under grant agreement  337595  ( ERC Starting Grant , "CoSMass"). NB acknowledges the European Union's  Seventh Framework programme  under grant agreement  333654  (CIG, 'AGN feedback'). CL is funded by a  Discovery Early Career Researcher Award  ( DE150100618 ). Parts of this research were conducted by the  Australian Research Council Centre of Excellence for All-sky Astrophysics (CAASTRO) , through project number  CE110001020. MB and PC acknowledge supports from the  PRIN-INAF 2014. MA acknowledges partial support from  FONDECYT  through grant  1140099.
AK acknowledges support by the  Collaborative Research Council 956 , subproject  A1 , funded by the  Deutsche Forschungsgemeinschaft (DFG). This work was partially supported by  NASA  Chandra grant number  GO3-14150C  and  GO3-14150B  (FC, SM). ID is grateful to  B. Magnelli  for useful suggestions and to  Takamitsu Miyaji  for his help with CSTACK.
SiBCASA has fully integrated water, energy, and carbon cycles, and a modified snow model to better simulate permafrost dynamics  74. The soil model separately tracks liquid water, ice, and frozen organic matter at each time step as prognostic variables, accounting for the effects of latent heat  7, 75. SiBCASA separately tracks CO 2 and methane emissions. The model was used to make one of the first estimates of future permafrost degradation and global carbon emissions from thawing permafrost  7. Here we ran multiple projections from 1901 to 2300 starting from the same initial conditions. We spun up the model until the release from permafrost carbon was negligible, ending up with 560 GtC of frozen permafrost carbon in the top three metres of soil  75, 76  by initializing the model with the observed values from the Northern Circumpolar Soil Carbon Dataset version 2 (NCSCDv2)  77.
economic impact function, with planned adaptation restricted to SLR impacts. Climate model data. The complete lists of the CMIP5 and CMIP3 models used in the study are provided in Supplementary Tables  18  and 19. Image processing. The Figures were plotted using Matlab R2018a, IDL (Fig. 5 ) and Palisade Risk 7. 5 (Fig. 6 ). We used Matlab's Savitzky-Golay smoothing for the SAF results from CMIP5 (Fig. 2 ) and Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) interpolation for the time-series results from PAGE-ICE.
Acknowledgements This work is part of the  ICE-ARC  project funded by the  European Union 's  7th Framework Programme , (grant  603887 , contribution  006 ). D. Y. received additional funding from  ERIM ,  Erasmus University Rotterdam , and  Paul Ekins  at the  ISR, University College London. K. S. was funded by  NSF  (grant  1503559 ) and  NASA  (grants  NNX14A154G ,  NNX17AC59A ). E. J. was funded by the  NGEE   Arctic  project supported by the  BER Office of Science at the U. S. DOE. Y. E. was funded by the  NSF  (grant  1900795 ). E. B. was supported by the  UK Met Office Hadley Centre Climate Programme  funded by  BEIS  and DEFRA. Publication of this article was funded by  Lancaster Environment Centre , the  University of South Florida St. Petersburg's Open Access Publication Fund, NSF  (grant  1900795 ) and  NASA  (grants  NNX14A154G ,  NNX17AC59A ).
28 n the contrary, and similar to many other pathogens, the HIV-1 genome is short (9,719 nt for the reference strain HXB2, including 5¢-and 3¢-LTR sequences). Thus, the number of informative characters between full-genome HIV-1 sequences remains limited, and missing data act by reducing the number of shared informative characters disproportionally when any two sequences have missing characters at different alignment positions. In addition, HIV-1 phylogenies from generalized epidemics in sub-Saharan Africa are broad and exhibit many long branches. 12, 29, 30 Indeed, due to the sheer magnitude of generalized HIV-1 epidemics in sub-Saharan Africa, closely related sequences are often not available to break long branches in viral phylogenies,  31  although other factors, including onward transmission months or years after infection, also contribute to the presence of long branches.
The consensus sequence of mapped reads was determined by the most frequent read call at each site. To mitigate the effects of low-level contaminant reads, sites with less than 10 mapped reads were classified as undetermined. Consensus sequences were trimmed to the viral genome from HIV-1 gag (p17) to HIV-1 nef. This process yielded consensus se-quences that were each aligned against a de novo reference sequence and a set of standard reference sequences, in this case the Los Alamos HIV-1 sequence compendium 2012. 39 o construct alignments of HIV-1 consensus sequences, insertions in consensus sequences were excised if they were not present in the standard reference sequences. The resulting alignment was uncertain in that gap characters which flank missing data characters could represent a deletion or a missing nucleotide as in ''AC-GT-??-?-ACGT.
Maximum-likelihood tree reconstruction To ensure optimal deployment of existing phylogenetic inference tools, HIV-1 trees were reconstructed with IQ-TREE,  42, 43  PhyML,  44  and RAxML  45  by the respective software developers. To determine best program settings, the 'true' phylogeny, from which the sequences were simulated, was provided for one data set without missing nucleotides to the teams. Trees were also reconstructed with FastTree  46  by the authors of this study. The command line options that were used for HIV-1 tree reconstructions are listed in Supplementary Data; Supplementary Data are available online at www. liebertpub. com/aid). Trees were subsequently dated and rooted with LSD version 0. 3beta. 47.
Assessment of phylogeny reconstructions from sequences with missing data Reconstructed trees were compared to true trees using several distance measures for tree topologies and HIV-1 transmission pairs. The central aim of PANGEA-HIV is to characterize recent transmission dynamics. For this reason, we focused on comparing the topology of phylogenetic clades that corresponded to transmission chains within the simulated regional population. This excluded deep splits in the true and inferred phylogenies from consideration. For each clade with at least four taxa, we calculated the pro-portion of unrooted, labeled subtrees of four taxa whose topologies differed between inferred and true clades (Quartet distance). 48 In addition, we evaluated the Kendall-Colijn distance on the same clades. 49 Tree distances typically scaled with clade size.
PANGEA-HIV next-generation sequences Table  1  characterizes the first 3,985 PANGEA-HIV consensus sequences. Next-generation sequencing data are available through the European Nucleotide Archive (www. ebi. ac. uk/ena/data/view/PRJEB19239) and HIV-1 consensus sequences are available upon request to the PANGEA-HIV steering committee (Supplementary Data). Two thousand eight hundred thirty-three sequences are from 26 communities of the Rakai Community Cohort study, Uganda. 53 Serum samples were obtained from household residents (aged 15-49 years) in three survey rounds between 2011 and 2014 in fisherfolk communities at the shores of Lake Victoria, and predominantly agrarian or trading communities inland. Participants were recruited at central community locations after a community mobilization event.
25, 57 It is not clear to what extent these factors could exacerbate the impact of irregularly distributed missing data on phylogeny reconstruction and subsequent molecular epidemiologic investigations. Conversely, our accuracy measures were not restricted to phylogenetically credible clades due to computational limitations in generating large numbers of replicate trees. Phylogenetically credible clades (that occur in a high proportion of replicate trees) could be considerably more robust to the impact of missing sequence data than our error analyses across all clades suggest. Several previous studies support our finding that irregularly distributed missing data patterns have a substantial excess negative impact on HIV-1 phylogeny reconstruction below a certain sampling level. Missing nucleotide characters can exacerbate long branch attraction artifacts,  33, 34  while increased taxon sampling reduces systematic errors in tree reconstruction.
31, 70 In settings with sparse sequence sampling, our findings also support previous arguments against ad hoc alignment trimming  71  : we found that missing characters must be highly unevenly distributed across the genome for this trimming to have a net positive impact on phylogeny reconstruction. We suggest evaluating the impact of alignment trimming in simulations before application to real data. Simulation routines for this purpose are available as part of the PANGEA-HIV simulation tool (https://github. com/olli0601/PANGEA. HIV. sim). Even when using NGS without missing characters, a large proportion (25%-35%) of phylogenetically very close pairs of individuals (patristic distance <1% substitutions/site) were not transmission pairs in our simulations. The fact that complete NGS cannot confirm HIV-1 transmission events is primarily a consequence of sparse sampling in our simulations and also of viral evolution within hosts, which can lead to incongruencies between phylogenies and transmission trees regardless of sampling.
Acknowledgments S. G. acknowledges the  Centre for eResearch at the University of Auckland  for general support. Computational results were partly achieved using the  Vienna Scientific Cluster , the NeSI high-performance computing facilities (www. nesi. org. nz), and the  Imperial College High Performance Computing Service  (www3. imperial. ac. uk/ict/ services/hpc). New Zealand's national facilities are provided by the  NZ eScience Infrastructure  and funded jointly by NeSIs collaborator institutions and through the  Ministry of Business ,  Innovation and Employment's Research Infrastructure program. We thank the PANGEA-HIV steering committee (Supplementary Data) and the  PANGEA-HIV/ ICONIC sequence assembly working group  (in particular  Francois Blanquart ,  Dan Frampton , and  Swee Hoe Ong)  for their input, and the PANGEA-HIV steering committee for comments on a previous version of this article.
O. R. , A. L. B. , P. K. , Td. O. , D. P. , C. F. are supported through the  PANGEA-HIV  consortium by the  Bill and Melinda Gates Foundation ; C. W. , C. F. by the  European Research Council  (Advanced Grant  PBDR-339251 ); C. C. by the  Engineering and Physical Sciences Research Council  ( EPSRC EP/K026003/1  and  EP/ I031626/1 ); S. F. by the  Medical Research Council  ( MR/ J013862/1 ); T. Q. by the  Division of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH ; A. S. and A. K. by the  Klaus Tschira Foundation ; J. T. , B. Q. M. , and Av. H. by the  Austrian Science Fund  ( FWF I-2805-B29 ). The  MRC/UVRI  is funded jointly funded by the  UK Medical Research Council (MRC)  and the  UK Department for International Development (DFID)  under the  MRC/DFID  Concordat agreement.
The length of streamflow observations is generally limited to the last 50 years even in data-rich countries like France. It therefore offers too small a sample of extreme low-flow events to properly explore the long-term evolution of their characteristics and associated impacts. To overcome this limit, this work first presents a daily 140year ensemble reconstructed streamflow dataset for a reference network of near-natural catchments in France. This dataset, called SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological dataset), is based on (1) a probabilistic precipitation, temperature, and reference evapotranspiration downscaling of the Twentieth Century Reanalysis over France, called SCOPE Climate, and (2) continuous hydrological modelling using SCOPE Climate as forcings over the whole period.
This work therefore presents beforehand the SCOPE Climate (Spatially COherent Probabilistic Extended Climate) dataset, derived from the statistical downscaling of 20CR, and introduces the SCOPE Hydro (Spatially COherent Probabilistic Extended Hydrological) dataset, the hydrological dataset derived from SCOPE Climate. The last objective of this work is to present some examples of how characteristics of spatiotemporal extreme low-flow events may be analysed and exploited to extract relevant information from the SCOPE Hydro historical reconstructions running from 1871 onwards. The paper is structured as follows. Section 2 introduces the different reanalysis and observation datasets used. Section 3 describes the hydrological modelling step as well as the new spatial and probabilistic definition of extreme lowflow events.
Observed streamflow A set of 662 near-natural catchments from across France was selected for hydrological modelling and study of extreme low-flow events (see Fig. 2 ). These were derived from two existing and partially overlapping datasets. -The French low-flow reference network consisting of 236 gauging stations, selected by  Giuntoli et al. (2013)  and meeting the following criteria: (a) at least 40 years of daily records, (b) the gauging station controls a catchment without direct human influence on river flow, and (c) data quality is suitable for low-flow analysis. -A set of 632 gauging stations selected by  Catalogne et al. (2014)  and meeting the following criteria: (a) at q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q qq q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Corrèze Ubaye Y Lambert [km] KGE( Q) q q q q q q q q q q q q q q q q 0.
95 0. 9 Streamflow data for these catchments were extracted from the French HYDRO database (http://hydro. eaufrance. fr/). Two case study catchments with contrasting regimes and long observational records are selected to exemplify results in Sects. 3. 1 and 5. These are the Corrèze@Brivela-Gaillarde with an oceanic-dominated regime, and the Ubaye@Barcelonnette, with a snowmelt-dominated regime. Figure  A1  in Appendix A shows their daily interannual regimes. Observations have been available since 1 January 1918 for the Corrèze and since 1 January 1904 for the Ubaye.
SCOPE Climate SCOPE Climate is a daily 8 km spatially coherent reconstruction of precipitation, temperature, and reference evapotranspiration fields over France. This dataset is available as a 25-member ensemble of Safran-like daily gridded time series over the period 1 January 1871 to 29 December 2012. SCOPE Climate is obtained through the statistical downscaling of 20CR  (Compo et al. , 2011)  with the SCOPE method (Spatially COherent Probabilistic Extension method), detailed in Appendix B, as an extension of the work by  Caillouet et al. (2016). Validation experiments of SCOPE Climate have been performed with both dependent and independent data at different time steps (not shown). SCOPE Climate presents very little bias in comparison to Safran on their common period. There is a slight overestimation of precipitation in spring (approx.
10 % of Safran precipitation) and an underestimation/overestimation (within 1 • C) in summer/winter mean temperature, respectively. The seasonal variability of both precipitation and temperature is well reconstructed and SCOPE Climate is able to capture the increasing trend in temperature since the 1990s. SCOPE Climate shows an overestimation of reference evapotranspiration, in particular in autumn and winter, as well as an underestimation of its seasonal variability. Catchment-scale data used for the hydrological modelling are computed as for Safran data.
The two CemaNeige parameters are calibrated only for catchments with a snowfall / rainfall ratio over 10 % -representing 187 stations out of 662 -in order to prevent unrealistic values for stations without enough snow episodes. For the remaining 475 stations, the median values of the calibrated CemaNeige parameters, from the 187 snowinfluenced catchments, are adopted to run CemaNeige. Calibrated models are then run with the two meteorological datasets described in Sect. 2. 2 on the 662 stations to obtain -Safran Hydro: 662 daily streamflow series over the 1 August 1958 to 31 July 2014 period using Safran as input; and -SCOPE Hydro: 662 × 25 daily streamflow series over the 1 January 1871 to 29 December 2012 period using SCOPE Climate as input. SCOPE Hydro is the dataset that will be used to derive extreme low-flow events between 1871 and 2012.
The observed interannual variability is well simulated by both Safran Hydro and SCOPE Hydro. Here again, Safran Hydro and observations are generally included in the SCOPE Hydro ensemble range. SCOPE Hydro allows one to fill data gaps for years with missing observations (for example around 1925 for the Corrèze and 1918 for the Ubaye) and to extend our knowledge back until the end of the 19th century.
Extreme low-flow events since 1871 This section provides example results that can be derived from the SCOPE Hydro reconstruction dataset after the definition of extreme low-flow events (Sect. 3. 2. 1) and the ensemble and spatio-temporal matching (Sect. 3. 2. 2). The first part focuses on spatio-temporal events examined only at the local scale for the two catchment case studies. The second part then provides summary results on spatio-temporal events drawn at the scale of France.
Return period in severity for the 25 ensemble members of the 1893 event. Filled grey circles represent stations that did not detect the event. L. Caillouet et al. :  Ensemble reconstruction of spatio-temporal extreme low-flow events matching and the ensemble matching are then discussed in Sects. 6. 2 and 6. 3, respectively. Section 6. 4 touches upon issues that may arise when comparing spatio-temporal events from two datasets. Some limitations of summary statistics derived from the ensemble values are discussed in Sect. 6. 5. Lastly, Sect. 6. 6 discusses additional sources of uncertainties not considered in this study. Uncertainties derived from 20CR have already been discussed in  Caillouet et al. (2016)  and will not be further developed here.
Ensemble matching sensitivity Section 3. 2. 3 introduced average events as a way of summarizing local events from all ensemble members in a single deterministic way of applying the spatial matching procedure. Events from individual ensemble members that do not overlap temporally with average events are removed from the final dataset. In Fig. 8 , very short events detected by only a few ensemble members during May 1990 for Station 1 in HER 11 (shown in grey) are for example adequately removed in the final spatio-temporal event definition. However, events detected by 8 ensemble members before July 1989 are also removed -the threshold for deriving an average event being set at 10 for this station -while being detected in the Safran Hydro dataset (in blue). Even if the threshold definition is calibrated against Safran Hydro summary statistics (total duration of events during a given period), it may thus introduce some limited discrepancies between the two datasets.
It has to be noted that this particular discrepancy is only local: Fig. 5e  shows that an event occurred in early 1989 for 12 out of 16 stations in HER 11 in the Safran Hydro dataset, and an event concurrently occurred at 8 stations for average events in the SCOPE Hydro dataset (not shown). The two datasets therefore agree well in the occurrence of an event during this period over a large part of this HER, even if differences may occur locally due to the local sensitivity of ensemble matching choices. Figure  8b  shows another case of local discrepancy between the two datasets for Station 1 in HER 11: the late 1990 event is clearly detected locally by SCOPE Hydro (in red) but not by Safran Hydro (in orange). However, Fig. 5e  shows that Station 1 is the only one (out of 16) in HER 11 where no event is detected during this period in the Safran Hydro dataset.
Moreover, 15 stations in this HER are affected by an event around this period in the SCOPE Hydro dataset (not shown). This shows once again that the two datasets agree well when considering the regional occurrence of low-flow events. Figure  8b  also features a favourable case where the late 1991 event (shown in mauve) is detected by 23 SCOPE Hydro ensemble members as well as by Safran Hydro (in green). The cases discussed above thus perfectly illustrate the compromise reached here to derive a deterministic series of events from an ensemble of series and to then apply the spatial matching procedure.
Comparison of spatio-temporal events from two different datasets The previous section briefly touched upon a comparison of extreme low-flow events derived from the Safran Hydro dataset and the SCOPE Hydro dataset, at the local scale, but also from a larger spatio-temporal perspective. One may then want to make a more formal comparison of spatio-temporal events between the two datasets (and possibly with a spatial matching of events derived from observations, but see comments in Sect. 6. 2. 1). This section discusses further the limitations of automating such comparisons. The simplest approach would consist of identifying locally previously defined spatio-temporal events through a date overlapping procedure. However, this can lead to the identification of multiple spatio-temporal events from one dataset but only one event from the second dataset, calling into question the initial spatial matching done (independently) for each dataset.
Moreover, this approach would identify events only locally and through a temporal dimension, not considering the spatial extent of the event at the scale of France, which would be too restrictive in practice, as shown by the above examples. One response would be to formally identify a spatio-temporal event from one dataset to a spatiotemporal event from another dataset. Some further technical developments would thus have to be set up and tested for comparing any two spatio-temporal extents of extreme lowflow events in both temporal and spatial dimensions. When some temporal overlapping rules may rather easily be set up at the local scale, corresponding rules for allowance in spatial extent would be much harder to formalize due to the irregular location of hydrometric stations (and upstream catchments).
One way forward would be to start the identification based on HER-representative events, which carry less spatio-temporal variability, and report this identification to the local scale. Lastly, one would also need to deal with the ensemble property of the SCOPE Hydro dataset. Again, average events as defined in Sect. 3. 2. 3 may be a promising way forward. Nevertheless, wrongly identifying multiple spatiotemporal events from one dataset to only one event from the second dataset is still a possibility.