dataesr/scientific-paragraphs-categorization huggingface dataset:
Eye tracking data were also used as the basic input for enhancing the interaction between the map user and the map interface. GazeGIS platform  (Tateosian et al. , 2017)  is an identical example of how this approach can be implemented. In particular, GazeGIS serves as the gaze-based geographic information system (GIS) which allows the augmentation of text with geographic information. Hence, this platform, although a prototype, may constitute a different way of navigation and distribution of available geographic information. Besides the aforementioned analysis tools and applications, some more generic tools suitable for eye movement analysis have been delivered by cartographic society. For example, EyeMMV toolbox (https://github. com/krasvas/EyeMMV)  (Krassanakis 2013b; Krassanakis et al. 2014 ) is MATLAB analysis toolbox which fully supports the analysis of eye tracking data in typical eye tracking metrics (fixations, saccades, and scanpath based) as well as the development of popular eye tracking visualization methods.
Additionally, the function of this toolbox has been recently extended (LandRate toolbox (https://github. com/krasvas/LandRate)) in order to deliver the automatic eye tracking analysis report in simple steps  (Krassanakis et al. , 2018). At the same time, LandRate toolbox gives the opportunity to calculate the integrated index (LRI) that combines typical eye tracking metrics with weights that can be produced on a basis of expert judgment procedures. Moreover, the 3DgazeR (http://www. eyetracking. upol. cz/3d/) is another tool that supports the visualization of eye tracking data on 3D geovisualizations (e. g. digital elevation models)  (Herman et al. , 2017). Such implementation extends the functionality of typical 2D eye tracking visualization methods in 3D space and may support gaze data visualization during the observation of corresponding types of visual stimuli.
The reported findings can be used directly for designing more efficient and more effective cartographic products. Eye tracking data can also be used for modeling the behavior of the user during the execution of specific map tasks (see e. g. the modeling approach followed by  Dong et al. (2018) ). Additionally, the development and the distribution of commonly available ground truth map-based eye tracking datasets can substantially contribute the implementation of artificial intelligence (e. g. machine and deep learning). Consequently, future maps and map services may be more "smart" and customized to meet users' needs in different viewing conditions.
Brief overview of code upgrade Since the last general publication regarding the FIFRELIN code  [1]  several modifications, improvements, new data handling as well as new capabilities are now available in the code. Most of them will be briefly summarized below: • Basic input data, as described for instance in  [2] , are the pre-neutron mass yields as well as the first and second moments of the gaussian disctribution of the Fission Fragment (FF) kinetic energy as a function of mass. It is now possible to handle a 2D matrix Y(A,TKE) directly from experiments or to use the primary mass, charge and kinetic energy results obtained from the GEF code  [3]. * e-mail: olivier. litaize@cea. fr • Nuclear charge can be sampled from two different parametrizations of Wahl's model  [4, 5]. • Internal conversion coefficients for electron emission are used from the RIPL-3 database at low energy (as usual) but to fill the gap they are now also tabulated from the BrIcc code  [6]  up to 6 MeV as a function of nuclear charge, electron shell and sub-shells as well as multipolarity.
Mixing ratios are also taken into account. • Tabulated nuclear level densities ρ(E, J, π) from microscopic + combinatorial calculations (Hartree-Fock-Bogoliubov HFB formalism) are now available in the code. FIFRELIN can use level densities based on a Skyrme effective interaction (BSk14)  [7]  taken from RIPL-3, as well as level densities based on a Gogny effective interaction (D1M)  [8]. • Microscopic photon strength functions for electric and magnetic dipole transitions (D1M+QRPA)  [9]  are now available, directly taken from the TALYS code  [10]. • A complete refactoring of the ROOT  [11]  package used to analyze FIFRELIN events has been undertaken, increasing its capabilities. • Gamma angular correlations can be accounted for using statistical tensor theory  [12]  in gamma cascades after thermal neutron capture and are in progress for fission calculation considering different alignment hypothesis of the primary fragment spins.
• The EGAF database  [13]  for thermal neutron capture cascades (handling ENSDF database is in progress) can be used for specific applications as described in section 4. If the primary transitions are not fully known from EGAF, a 'mixed calculation route' can be performed with a 'theoretical' FIFRELIN calculation and an 'experimental' EGAF cascade with the appropriate weighting. • A coupling has been done between FIFRELIN and the PHITS+DCHAIN (JAEA) system code  [14, 15]  allowing to deal with delayed components in fission and especially time evolution of decay heat as discussed in section 5.
The resulting gamma cascade in coincidence with the positron signal is the signature of an anti-neutrino. The calibration is performed with AmBe sources where (α, n) reaction is used to generate neutrons and a precise cascade is mandatory for deducing systematic uncertainties. Generation of Gd-cascades in GEANT-4 for STEREO simulations was initially performed with a model called GLG4sim, inherited from past neutrino experiments. However this model did not allow for an accurate reproduction of the calibration spectra. A first and major improvement has been obtained by replacing Gd-cascades calculated with GL4sim with the ones calculated with FIFRELIN  [22]. A second improvement has been made possible by considering a mixing of EGAF database for primary transitions from the entry state (S n , J, π) and theoretical FIFRELIN calculations.
Delayed fission observables: focus on decay heat By default, FIFRELIN calculates the whole set of prompt fission observables and a rough estimation of the average delayed neutron multiplicity by simply accounting for the number of neutrons emited when a β -n level is reached after a prompt cascade. Here, the code has been interfaced with the DCHAIN-PHITS  [14]  general purpose radiation transport code  [25]. Indeed, isotopic fission yields can be provided by FIFRELIN to DCHAIN-PHITS. The DCHAIN burn-up functionality of the PHITS transport code calculates the nuclide inventory and decay heat, among other, based on the activation and decay data libraries through the Bateman equation. In this work, total and gamma decay heat from 0. 2 to 10 5 s after a 235 U(n th ,f) burst are shown in Fig. 4  and compared with data compilations from Dickens  [26]  and Tobias  [27].
Before the code upgrade (from FIFRELIN-2018 to FIFRELIN-2021), neutron rich Rb, Sr, Cs and Ba isotopes were systematically overproducted (see Fig. 6  of ref  [25] ). In this work, the nuclear charge distribution from the Wahl's Z p model are modified to account for the revision in Ref. [5] , preneutron mass yields come from  [28]  and finally the RIPL3-2020 release of the nuclear structure database is accounted for, improving the calculated decay heat over the whole time range for the thermal fission of 235 U (FIFRELIN-2021 in Fig. 4 ). All these modifications allow to reduce the overestimation observed with the previous FIFRELIN-2018 calculation, up to roughly 100 s. Fission fragments having longer half-lives are no longer overestimated. For more details, the reader can refer to Ref. [25].
Conclusion Some recent improvements performed in the FIFRE-LIN code have been briefly presented, such as new preneutron data format for FF mass and kinetic energy or nuclear charge models, a new release of the nuclear structure database, interfacing with GEF or PHITS-DCHAIN codes, use of microscopic level densities and photon strength functions as well as γγ angular correlations. Calculations obtained with this new release are in good agreement with experimental data such as VESPA for prompt fission gamma-ray observables, STEREO for gamma cascades after thermal neutron capture and time evolution of the decay heat after 235 U burst. Additional improvements and studies are in progress such as particle correlations in fission or the interfacing with ENSDF database.
Therefore, R. auricomus genomes are large even at the diploid level (diploid genome sizes are estimated to be between 5. 998-6. 174 Gbp, Leitch et al. , 2019). Increasing proportions of repetitive regions have usually been considered responsible for genome size inflation  (Bennetzen, 2005) , which could also be relevant for R. auricomus genomes  (Hörandl et al. , 2009; Hodač et al. , 2018; Karbstein et al. , 2022). However, so far, reference genomes for the species complex, but also for the entire genus Ranunculus, are still missing. Among Ranunculaceae, the only other reference genomes are available from diploid Aquilegia species  (Xie et al. , 2020; Mokhtar et al. , 2023) , but these genomes are much smaller (mean 0. 9 Gbp,  Leitch et al. 2019) , and no polyploid complexes appear in this genus (TROPICOS database vers.
3. 4. 2, http://www. tropicos. org; CCDB database vers. 1. 66, http://ccdb. tau. ac. il/home/,  Rice et al. , 2015). The evolutionary relationships among diploid sexual R. auricomus progenitors have recently been clarified  (Tomasello et al. , 2020; Karbstein et al. 2020b ), as noted above, and initial insights into the allopolyploid origins and genome evolution of derivatives have been obtained  (Karbstein et al. 2021a (Karbstein et al. , 2022)). A Ranunculus reference genome would be a major step forward in improving the reconstruction of the species by facilitating read mapping to orthologous loci, allele phasing, and subgenome assignments; it would be the starting point for sophisticated approaches like reference-guided assemblies (e. g. ,  Eaton and Overcast, 2020) , polyploid phasing used for multilabeled, subgenome species trees (e.
2 with one freshly prepared and one at -80°C (two days) stored libraries, and flow cell no. 3-6 with two at -80°C for 1-2 weeks stored libraries. We used a MinION 101B device, the ONT software  MINKNOW vers. 21. 11. 7, 21. 11. 9, and 22. 03. 5  were installed on a local Linux system, and six R9. 4. 1 flow cells following the manufacturer's instructions for priming and loading. After one or two days of runtime, the old library was exhausted and thus replaced with a second library using the supplied flow cell wash kit (protocol vers. WFC_9120_v1_revB_08Dec2020). Sequencing was run for 3-5 days per flow cell until the majority of pores were inactive (> 99%).
Genomic Data Analysis For Illumina sequence data, we first converted the raw output into *fastq files using SUPERNOVA vers. 2. 1. 1 (10x Genomics, San Francisco, USA;  Marks et al. , 2017; Weisenfeld et al. , 2018)   (Shi et al. , 2019) , and drawn using CPGVIEW  (Liu et al. , 2023). Geneious R11 2023. 1. 2  (Kearse et al. , 2012)  was used to check and edit the annotation results. The final plastome sequence incl. annotations was deposited into GenBank (OP250948). The plastome with annotations is also shown in Figure  1. To investigate sequence differences between R. cassubicifolius and R. repens, we performed progressiveMAUVE  (Darling et al. , 2004)  whole genome alignment analysis in Geneious. In addition, mapping TEG off-target reads to reference plastomes from sampled or herbarium specimens is a common strategy for constructing plastome phylogenies in evolutionary studies  (Folk et al.
, 2015; Tomasello et al. , 2020). Therefore, we followed the procedures described in the study by  Karbstein et al. (2022)  to examine the effect of read mapping and lengths of the resulting alignments using the distantly related plastome of R. repens, the R. cassubicifolius plastome assembled here as a closely related reference, and 87 di-and polyploid, sexual and apomictic R. auricomus TEG samples (SRA BioProject PRJNA628081;  Tomasello et al. , 2020; Karbstein et al. , 2022). The mitochondrial genome. To assemble the mitogenome, we used GETORGANELLE with the same settings as described above, except that we specified "embplant_mt" and as seed the closest available mitochondriome (Hepatica maxima, GenBank accession number NC_053368. 1;  Park and Park, 2020). The estimated average embplant_mt base coverage was 92.
, 2023)  and CPGView  (Liu et al. , 2023)  with default settings. For visual identification of repetitive regions, the BLASTN webserver tool  (Zhang et al. , 2000; Camacho et al. , 2009)  was used to compare the R. cassubicifolius mitogenome against itself (e-value cutoff of 1 × 10 -6 , word size of 16; following  Park and Park (2020). Geneious was used to inspect and edit the annotation results. The resulting *gb file was converted into a feature table file for GenBank submission using the GB2SEQUIN tool  (Lehwark and Greiner, 2019)  within the CHLOROBOX webserver. The final annotated mitochondrial genome is illustrated in Figure  2. The mitochondrial raw data and the annotated mitochondrial genome will be made available upon publication (SRA: PRJNA831351, and BankIt: 2722616, respectively).
, 2008). Using the update function, UTRs were added and gene models in disagreement with RNA data were fixed (partly manually, as recommended). Functional genome annotation was then done with INTERPROSCAN  (Jones et al. , 2014) , EGGNOG-MAPPER  (Cantalapiedra et al. , 2021) , and ANTISMASH  (Blin et al. , 2021). The article will be updated soon with annotation results, and the annotated genome will be deposited in NCBI under Bioproject PRJNA831351 upon publication.
Results and Discussion More than 610 million, 150 bp raw Illumina reads were generated leading to 91 Gbp and genome coverage of ca. 28x (mean base accuracy > 99. 9%) assuming a haploid genome size of 3. 3 Gbp (see Introduction). In addition, the final ONT long-read dataset contains 50. 9 Gbp and 4. 26 million reads, yielding a coverage of 16x (mean base accuracy of 97%) on an estimated haploid genome size of 3. 3 Gbp. The plastid genome. Based on short Illumina reads, we retained a circular plastid genome with 156,233 bp in two different states caused by the differing orientations of the inverted repeat (IR) regions ("flip-flop";  Stein et al. , 1986; Walker et al. , 2015). Results were visually inspected using Bandage vers. 0. 9. 0  (Wick et al. , 2015). The plastome shows a typical tetrapartide structure of a small single-copy region  (SSC, length: 19, 826   Interestingly, the plastome of R.
Data Availability. The authors declare that basic data supporting the findings are available within the manuscript and Supporting Information. Raw sequencing data and the assembled genomes are deposited on the National Center for Biotechnology Information (NCBI) database under the BioProject accession numbers: PRJNA826743 for Illumina raw reads (https://www. ncbi. nlm. nih. gov/bioproject?LinkName=biosample_bioproject&from_uid=2758 2367), PRJNA831351 for filtered Nanopore reads, filtered mitochondrial Illumina and Nanopore reads, and the assembled and annotated genome (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA831351/), OP250948 for the assembled and annotated plastome (https://www. ncbi. nlm. nih. gov/search/all/?term=OP250948), OP2722616 for the assembled and annotated mitochondrial genome.
The read quality of all datasets was checked using FASTQC (reports on FigShare). and BLC2FASTQ vers. 2. 20 (Illumina, San Diego, USA), resulting in 51 Gb raw *fastq files. Read quality was assessed by FASTQC vers. 0. 11. 4 (reports on FigShare; Andrews, 2010). We performed polyX-tail and adapter trimming, and quality filtering of Illumina raw reads, using ATRIA vers. 3. 1. 2 (Chuan et al. , 2021) with default settings, and BBMAP vers. 37. 66 ('repair. sh'; sourceforge. net/projects/bbmap/) to reorder filtered paired-end reads. The plastid genome. The R. cassubicifolius plastome was assembled using Illumina short-read data and the software GETORGANELLE vers. 1. 7. 5. 3 (Jin et al. , 2020;  as     recommended by Freudenthal et al. , 2020) , specifying the "embplant_pt" database, k-mer size range between 45 and 121, use of all Illumina reads, and as seed the Ranunculus repens plastome (154,247 bp, GenBank accession number NC_036976; Dann et al.
; (ii) using moderate-coverage Illumina short-reads and lowcoverage ONT long-reads, we performed analysis with SPADES (incl. the option of untrusted SUPERNOVA contigs for gap filling), MASURCA vers. 4. 1. 0 (Zimin et al. , 2017) , MUCHSALSA/LazyB vers. 0. 0. 2 (Gatter et al. , 2021) , and WENGAN (M) vers. 0. 2 (Di Genova et al. , 2021) ; (iii) using low-coverage ONT long-reads, we performed analyses with Paetzold et al. , 2022) were downloaded from NCBI BioProject PRJNA870679 and quality checked with FASTQC vers. 0. 11. 4. genome annotation. We thus annotated the assembled genome from FLYE+Polishing (3. 45 Gbp) with FUNANNOTATE pipeline vers. 1. 8. 15 (https://github. com/nextgenusfs/funannotate; Palmer and Stajich, 2022). First, the assembly was cleaned (only contigs >1 kbp, and duplicated contigs; -14 bp removed) and sorted by size using MINIMAP2 (Li, 2018), and simple repeats were masked using TANTAN ('-s arabidopsis'; Frith, 2011) as they easily align to each other, which causes false homology predictions and which in turn negatively impact gene annotation.
Approximately 6. 33% (218 CANU vers. 2. 1. 1 (Koren et al. , 2017)   and FLYE vers. 2. 9. 1 (Kolmogorov et al. , 2019)   (first: CANU corrected reads with the option '--nano-hq' as recommended, and second ONT raw reads with the option '--nano-raw' without polishing) using ONT long-reads followed by the optimized polishing strategy of Dmitriev et al. (2021) , i. e. , two-times via filtered ONT reads using RACON vers. 1. 4. 3 (Vaser et al. , 2017)   and MEDAKA vers. 0. 7. 1 (https://github. com/nanoporetech/medaka), and once using Illumina reads via the POLCA function (Zimin and Salzberg, 2020)   within the MASURCA pipeline. Statistics for genome assemblies were calculated using QUAST vers. 5. 0. 1 (Gurevich et al. , 2013) , and summarized in Table 1 (reports on FigShare). Genomic completeness in terms of expected single-copy genes inherent to land plants was estimated by 'Benchmarking Universal Single-copy Orthologs' (BUSCO vers.
5. 0. 0; Waterhouse et al. , 2018; Manni et al. , 2021)   with the 'embryophyta_odb10' vers. 2020-09-10 dataset containing 60 species and 1375 core genes, and shown in Figure 3. The assembly with the highest completeness, largest contig size, and best BUSCO scores (Table 1 , Fig. 3 , reports on FigShare), was selected for Mbp) of the genome was masked. Then, five recently published diploid R. auricomus transcriptomes from flower buds (R. cassubicifolius, R. notabilis, and their F 2 hybrids;.
The plastome contains 85 (78 unique) protein-coding genes, 37 (30 unique) tRNAs, and 8 (4 unique) rRNAs (duplicated ones in IRs). We also observed 35 SSR, 32 long tandem repeat, and 34 dispersed repeat motifs. In comparison with the closely related, recently published plastome of R. sceleratus (sister section to sect. Auricomus; Genbank accession number ON755204, Kim et al. , 2023)  and the more distantly related plastome of R. repens (different subgenus within Ranunculaceae), the plastome of R. cassubicifolius shows a maximum number of unique protein-coding genes (78/77/78), tRNAs (30/28/29), and rRNAs (4/3/4). The plastome size of R. cassubicifolius was slightly smaller compared to R. sceleratus (+96 bp, 156,329 bp), but smaller compared to R. repens (-1,986 bp; 154,247 bp), mainly due to differences in repeat motives in the SSC (19,826/18,434 bp) and LSC (85,629/84,224 bp) bp) and a large single-copy region (LSC, length: 85,629 bp) separated by two IR regions (length: 2 x 25,389 bp).
The floodplain scheme is validated based on the river discharge at the outflow of the Pantanal which represents the hydrological cycle over the basin, the temporal evolution of the water mass over the region assessed by the anomaly of total water storage in the Gravity Recovery And Climate Experiment (GRACE), and the temporal evaluation of the flooded areas compared to the Global Inundation Extent from Multi-Satellites version 2 (GIEMS-2) dataset. The hydrological cycle is satisfactorily simulated; however, the base flow may be underestimated. The temporal evolution of the flooded area is coherent with the observations, although the size of the area is underestimated in comparison to GIEMS-2. The presence of floodplains increases the soil moisture up to 50 % and decreases average temperature by 3 • C and by 6 • C during the dry season.
S1  in the Supplement). Section 2 describes the floodplain scheme as implemented in the river routing scheme of ORCHIDEE and the different equations ruling the exchange of water. The validation methodology and the observational datasets used are discussed in Sect. 3. Sections 4 and 5 then present the validation of the scheme. First it is performed on the variables directly linked with the river routing scheme (discharge, flooded area, volume of water in the routing reservoirs). Secondly, the impact of the floodplain scheme on the land surface states and the land-atmosphere fluxes are evaluated. The assessment and analysis of the impact of the floodplain scheme is performed based on simulations at different resolutions using a 0. 5 • atmospheric forcing and a 20 km atmospheric forcing. The final section presents the discussion and conclusion.
This volume is then converted into a flooded fraction f i based on the known potential flooded fraction for this HTU, f max,i. f max,i is obtained from the Global Lakes and Wetlands Database (GLWD; WWF, 2004); see Sect. 2. 5 for more details. The potential flooded surface for an HTU i is S fmax,i = S i •f max,i. We consider that an HTU i is a floodplain if S fmax,i > 0. The actual flooded fraction and the flooded surface are denoted respectively f i ∈ [0, f max,i ] and S f,i ∈ [0, S fmax,i ]. A more detailed description will be found in Sect. 2. 4. The floodplain scheme does not include divergent flows or groundwater lateral flow. Also, it does not include vegetation reduction due to water logging along floodplains.
Spatial description of the floodplains The Global Lakes and Wetlands Database (GLWD; WWF, 2004) available at a 1 km resolution has been interpolated to the HDEM used to define a mask of potentially flooded areas based on the following categories: (1) freshwater marsh, floodplains; (2) reservoir; and (3) pan, brackish saline wetland. Therefore, the floodplain mask is available for each pixel of the hydrological data. These data allow for calculating a potentially flooded area for each HTU during the routing construction. The floodplains in GLWD cover all of the Pantanal region. Before using the floodplain scheme over other large floodplains, it is necessary to assess the relevance of the spatial extent of the categories considered floodplains in GLWD. In the case of the inadequacy of the GLWD representation over the region, other datasets can be used to define the potentially flooded area.
There is a large uncertainty in the description of wetlands due to the difficulty of perfectly evaluating the flooded areas from satellite products, and there are also large uncertainties concerning the categorisation. Despite this uncertainty, GLWD is combining different types of products to obtain this categorisation. The review of other wetland descriptions in  Hu et al. (2017)  does not seem to show a product that would be preferable to GLWD. In this study, the GLWD dataset has not been modified, but the categories in the GLWD dataset related to floodplains may be further changed in other studies to adjust the floodplain mask.