dataesr/scientific-paragraphs-categorization huggingface dataset:
(2022) , which is the one we follow as our baseline taxonomy in this paper, lumped eight of the species recognised by  Gill et al. (2022)  while it split one species (Table  1 ). It nevertheless seems likely that the number of lark species will increase as a result of studies that integrate different categories of data  (e. g. , morphological, vocal, genetic and behavioural data). This is particularly true for Africa, which has several lark species with multiple poorly known subspecies. For example, a recent integrative taxonomic study of the African Rufous-naped Lark (Mirafra africana)-Sharpe's Lark (M. sharpii)-Red-winged Lark (M. hypermetra)-Somali Lark (M. somalica)-Ash's Lark (M. ashi) species complex proposed recognition of nine instead of five species  (Alstr€ om et al. , submitted).
DNA sampling and sequencing and SNP calling Our sampling scheme follows the taxonomy of  Clements et al. (2022). We obtained fresh or toepad samples for 38 lark taxa, for which whole genomes were resequenced (see below and Appendix Table  S1 ). Comparable data were obtained for another 21 samples of 17 Mirafra lark taxa from Alstr€ om et al. , and for the outgroup Panurus biarmicus (Short Read Archive accession SRX7050291) from  Sigeman et al. (2020). In addition, 306 sequences from 111 taxa from Alstr€ om et al. (2013a) and various other sources were downloaded from GenBank (Appendix Table  S1 ). In all, our sampling comprises 184 samples of 133 lark taxa and includes all except one of the species currently recognised in Alaudidae (the poorly known Friedmann's Lark Mirafra pulpa). The genetic sampling (Appendix Table  S1 ) varied from one gene (mitochondrial cytochrome b, cytb; 19 species) to >1 million single nucleotide polymorphisms (SNPs) (39 species).
All new sequences have been submitted to GenBank (Appendix Table  S1 ). We called variants from 50 resequenced lark samples and the outgroup Bearded Reedling (Panurus biarmicus) (Appendix Table  S1 ), relative to the Eremophila alpestris reference genome (GCA_009792885. 1; Mason et al. , 2020). We first trimmed Illumina adapters using the software Fastp v. 0. 20. 0  (Chen et al. , 2018)  and evaluated sequence quality using FastQC  (Andrews, 2010). Read mapping and variant calling were performed using the accelerated wrapper tools in the Sentieon toolkit v. 201911  (Freed et al. , 2017). Reads were mapped to the Eremophila alpetris genome using BWA-mem v. 0. 7. 17-r1188  (Li, 2013)  and PCR duplicates removed using the LocusCollector and dedup modules in.
e. patae analysed here. 5 Alstr€ om et al. (submitted); 6 Not recognised, but uncertain with which taxon it is synonymised; *Mirafra pulpa not analysed, but believed to be part of the redefined Mirafra; **Alaudala somalica athensis not analysed, but considered to be conspecific with A. somalica based on morphology. Sentieon. We called variable positions using HaplotypeCaller GATK v. 4. 1 (McKenna et al. , 2010)  and performed joint genotyping of all samples using GenotypeGVCFs (GATK v. 4. 1) by using the Haplotyper and Genotyper modules, respectively, in Sentieon. Finally, we applied the following common GATK filters to SNPs using the VariantFiltration command in GATK v. 4. 1: RPRS < À8, QD < 2. 0, FS > 60. 0, SOR > 3. 0, MQ < 40. 0, and MQRankSum < À12. 5. We additionally set genotypes with a depth outside the range 2-100, and those with a genotype quality <20 to no call using the SelectVariants command in GATK v.
Phylogenetic analyses based on mtDNA and nuclear loci A total of 177 lark sequences were included in the mitochondrial cytb data set (Appendix Table  S1 ). In addition, up to 16 nuclear loci were obtained for 35 taxa (Appendix Table  S1 ). The DNA sequences were trimmed and assembled in MegAlign v. 4. 03 in the DNAstar package (DNAstar Inc. ). The sequences were aligned using ClustalW  (Thompson et al. , 1994)  implemented in Bioedit 7  (Hall, 1999). The gaps generated by the process of multi-sequence alignments were coded as missing data in all analyses. For each gene, the choice of model was determined based on the Bayesian Information Criterion (BIC) as implemented in jModeltest v. 2  (Darriba et al. , 2012). The best-fit models for each dataset are provided in Appendix Table  S2. We used Bayesian inference (BI) to reconstruct a tree from cytb and the 16 nuclear loci for (177 samples), referred to as the 17-locus data.
Phylogenomic SNP data analyses For phylogenomic analyses we retained 44 samples of 34 species (including the single outgroup species) and added SNP data for 7 samples of 6 Mirafra taxa (M. somalica, M. ashi, M. hypermetra, M. a. africana, M. a. athi, and M. fasciolata) included in Alstr€ om et al. (submitted), processed with the above pipeline. We filtered the combined SNPs further to require a minimum genotype quality of 20 (-minGQ 20) and full sample coverage, i. e. , none with missing data (-max-missing-count 0) with vcftools v. 0. 1. 17  (Danecek et al. , 2011). Additionally, we excluded SNPs mapped to scaffolds 0. 5 Mbp, in order to confidently being able to select subsets of physically spaced SNPs. The full final SNP dataset comprised 1,247,784 SNPs across 51 samples of 40 species, referred to as the 51-taxon 1.
Coalescence-based species tree analyses We down-sampled the 51-taxon 1. 2M SNP set considerably for analyses with SNAPP v. 1. 5. 1  (Bryant et al. , 2012)  by (  1 ) retaining a single individual per taxon (41 samples, including the single outgroup species) and (  2 ) thinning by physical distance to include SNPs at a minimum distance of 10 Kbp or larger. From this, we randomly selected two non-overlapping subsets of 5,000 SNPs, creating two 41-taxon 5K SNP sets. We ran additional analyses with targeted datasets to resolve the branching sequence of (a) the three primary clades and (b) particularly challenging taxa, for which node support was lower in the above analyses (see Results). From the 51-taxon 1. 2M SNP set, we selected 21 taxa that represented every genus/major lineage (based on the results from the above analyses) and down-sampled SNPs either (1) through thinning by 50 Kbp, resulting in a matrix of 18,812 SNPs (21-taxon 18K SNP set), or (2) through first thinning by 10 Kbp and then selecting 50,000 of the remaining SNPs at random (21-taxon 50K SNP set).
These two datasets addressed the three primary clades and the branching sequence among Calendulauda, Heteromirafra and Mirafra sensu lato. Another dataset specifically addressed the position of Lullula and comprised the seven sampled species within the clade containing Lullula (genera Alauda, Galerida, Lullula, Spizocorys) with Calandrella as outgroup. Thinning by 10 Kbp, we analysed the full matrix of 88,030 variable SNPs (8-taxon 88K SNP set). Node age calibration was implemented as described for the 17-locus analyses (except in Lullula-specific analyses of the 8-taxon 88K SNP set, in which an uninformative tree age of 10 My was set to improve computation efficiency). We specified that branch-specific population sizes should not be evaluated (through linking theta), using the ruby script snapp_prep.
rb (https://github. com/mmatschiner/snapp_prep;  Stange et al. , 2018). We ran the SNAPP analyses in Beast 2. 6. 6  (Bouckaert et al. , 2019)  at different seeds, sampling every 100-500 generations, and optimizing parameters for final runs of typically around 1-2 million generations. We inspected results in Tracer v. 1. 7. 1 (Rambaut et al. , 2018) , ensuring stationarity and sufficient effective samples sizes (all parameters >200), and convergence between replicate runs.
Phylogeny based on single-locus and multilocus data The concatenated 17-locus dataset comprised 177 samples and 14,506 bp, of which 626 sites were variable in mitochondrial cytb and 4,810 sites across the 16 nuclear markers (Appendix Table  S2 ). The tree based on the 17-locus dataset (Fig. 2 ) includes all of the 93 recognised species in the world  (Clements et al. , 2022)  except Mirafra pulpa. It also covers recently proposed species of Eremophila  (Ghorbani et al. , 2020b)  as well as deeply diverged lineages within Mirafra africana and M. hypermetra, which have been proposed to be treated as separate species (Alstr€ om et al. ,). In total, 133 taxa are included, of which 44 (including 17 proposed or currently recognised species) are new compared to the study by  Alstr€ om et al. (2013a).
Phylogeny based on genomic data The main SNAPP species trees, based on two non-overlapping 41taxon 5K SNP datasets and covering representatives of all genera except Pinarocorys, Certhilauda, and Ammomanopsis (Fig. 3A , Appendix Fig. S2 ), are overall well supported by the data (35-36 of 39 nodes, 90-92%, have PP ! 0. 95). The two trees only differ on two points: the position of Mirafra hypermetra in relation to M. somalica/M. ashi/M. africana athi, and the position of Lullula, either as sister to Spizocorys (PP ¼ 0. 73; Fig. 3A ) or to the clade containing Galerida and Alauda (PP ¼ 0. 94; Appendix Fig. S2 ). The two 41-taxon 5K SNP trees generally agree well in topology with the 17-locus tree. However, two incongruences stand out (indicated by red x in Fig. 3A ), although only one of these receives PP ! 0.
2. 4  and 36 of the International Code of Zoological Nomenclature (ICZN, 1999)) Type genus (by indication): Alauda Linnaeus, 1758 Diagnosis: the taxa in this subfamily form a well-supported primary clade in multiple analyses of independent phylogenetic datasets (Fig. 1  in  Alstr€ om et al. , 2013a, Figs. 2 and 3 in the present study), specifically diagnosed by a fixed 1-bp adenine insertion between positions 14 and 15 of Myoglobin (MB) intron 2 in the Zebra Finch reference genome bTae-Gut1 v1 (accession GCA_003957565. 2); fixed thymine alleles at positions 89 and 434 (other taxa fixed for purines); fixed adenine allele (other taxa fixed for guanine) at position 414 counting from the start of exon 5 of ODC (transcript ODC1-201) in the Zebra Finch reference genome; fixed pyrimidine alleles at position 521 (other taxa fixed for purines).
Included taxa: Alaudinae comprises the Saharo-Arabian genus Eremalauda, the sub-Saharan genus Spizocorys, the Palearctic genera Lullula, Chersophilus and Melanocorypha, and five widespread genera with a predominantly Palearctic distribution: Galerida, Calandrella, Alaudala, Alauda, and Eremophila (all species included in Fig. 2 ). (2) Certhilaudinae Le Maout, 1852 (available under Article 11. 7. 2) Type genus (by indication): Certhilauda Swainson, 1827 Diagnosis: the taxa in this subfamily form a well-supported primary clade in multiple analyses of independent phylogenetic datasets (Fig. 1  in  Alstr€ om et al. , 2013a, Figs. 2 and 3 in the present study), specifically diagnosed by a fixed guanine allele (adenine in all other taxa) at position 19 and a fixed cytosine allele (guanine in all other taxa; site not sampled in Pinarocorys) at position 665 in Myoglobin; reference and positions as declared above for Alaudinae.
Included taxa: Certhilaudinae comprises the sub-Saharan genera Ammomanopsis, Chersomanes, Certhilauda, and Pinarocorys, the predominantly Saharo-Arabian genera Alaemon, Ramphocoris, and Ammomanes, and the Afro-Oriental genus Eremopterix (all species included in Fig. 2 ). (3) Mirafrinae  Bianchi, 1905 (available under Article 12. 2. 4)  Type genus (by indication): Mirafra Horsfield, 1821 Diagnosis: the taxa in this subfamily form a well-supported primary clade in multiple analyses of independent phylogenetic datasets (Fig. 1  in Alstr€ om et al. , 2013a, Figs. 2  and 3  in the present study), specifically diagnosed by a fixed guanine allele (adenine in all other taxa) at position 544 in Myoglobin and a fixed adenine allele or deletion (the latter only in Corypha hypermetra; all other taxa fixed for guanine) at position 534 of ODC; reference and positions as declared above for Alaudinae.
Conclusions Our study infers a baseline phylogeny of the species-rich lark family based on multilocus sequence data combined with up to 1. 2 million SNPs. It confirms the previously most densely sampled study, which revealed an unusually poor correspondence between phenotypes and phylogenetic relationships. We define three primary clades as subfamilies and balance the taxonomy of the family by splitting the large genus Mirafra into four genera, with divergence times similar to those between other lark genera. Two species are moved from Mirafra to Calendulauda, and several subspecies in a Corypha (formerly Mirafra) species complex that were suggested to be raised to species rank in a different study are here supported as separate species. This updated phylogeny and taxonomy of Alaudidae will serve as a foundation for future studies of various aspects of lark evolution and natural history, including addressing the few remaining phylogenetic conflicts uncovered here.
Acknowledgements We are indebted to  Mark Adams  and  Hein van Grouw  and the  Natural History Museum, Tring, UK , for access to specimens and multiple toepad samples for DNA; to  Sharon Birks  and the  University of Washington Burke Museum ,  David Willard  and the  Field Museum, Chicago ,  Sylke Frahnert  and the  Museum Für Naturkunde, Berlin ,  Nigel Collar  and  Claire Spottiswoode  for DNA samples; to  Nick Horrocks  for managing samples from Kenya;  Robert Dowsett  and  David Donsker  for comments on nomenclature; to the  National Swedish Research Council  (grants No. 2015(grants No. -04402 ,  2019-04486)-04486 ), the  Carl Trygger Foundation  ( CTS 20:6 ), the  Jornvall Foundation , and  Julian Francis  for financial support; and to the  National Genomics Infrastructure  in Stockholm funded by  Science for Life Laboratory , the  Knut and Alice Wallenberg Foundation ; the  Swedish Research Council , and the  Swedish National Infrastructure for Computing (SNIC)/Uppsala Multidisciplinary Center for Advanced Computational Science  for assistance with massively parallel sequencing and access to the UPPMAX computational infrastructure, and  SciLifeLab  for next-generation DNA sequencing; and to the  Research/Scientific Computing  teams at  The James Hutton Institute and NIAB  for providing computational resources and technical support for the "UK's  Crop Diversity Bioinformatics HPC " ( BBSRC  grant  BB/S019669/1 ).
As morphological trait, we estimated adult body size, approximated by measuring hind tibia length postmortem with a stereomicroscope (S APO Stereo Zoom Microscope, 215 Leica Microsystems, Wetzlar, Germany) and a 12. 6 x magnification (precision: ± 0. 01 mm). Alate (winged) aphids (7. 5% of the dataset) were discarded for fitness assays, because wing polymorphism occurs alongside profound physiological and morphological changes, resulting in contrasting life histories  ( Braendle et al. , 2006 ; Zera and Denno, 1997 ). With the exception of immature survival rates (lethal effects of heat stress), all traits were measured for individuals successfully surviving until the adult stage and reproduction.
Immature survival rate was compared between the different groups with a GLM assuming a binomial error structure and a logit link function. Total longevity was analyzed as a survival probability function of time since birth within a Cox proportional hazard regression (package ' survival ',  Therneau, 2021 ). Both immature development time and hind tibia length displayed heteroskedastic variances and were processed through GLMs assuming a Gamma error structure and an inverse link function. Lifetime fecundity data met both the homoskedasticity and normality criteria, allowing to run a linear model with analysis of variance (ANOVA). Differences among groups underlying interactive effects were teased out with post hoc tests based on contrasts among estimated marginal means (package ' lsmeans ',  Lenth and Lenth, 2018 ).
Acknowledgments We are grateful to  Prof. Nancy Moran  ( University of Texas at Austin ) for providing the aphid lines used in the experiments, and to  Dr. Jean-Christophe Simon  and  Prof. Yannick Outreman  ( INRAE  and  Institut Agro Rennes-Angers)  for their valuable help in checking clonal lines and infection status. We thank  Benoît Geelhand de Merxem  for supplying plant material during the experiments, and the other members of the laboratory for stimulating scientific discussions.
However, it is possible to implement other configurations (e. g. with different class types and hierarchies) within the CryoGrid community model (see  Sect. 4. 6 for a discussion of such possibilities). The "RUN_INFO" class is the only mandatory class type, which represents the uppermost level of any class hierarchy. For the multi-physics simulations described in this work, the second level in the class hierarchy uses the class type "TILE" (Fig. 1 ). The purpose of a TILE class is to perform a classic one-dimensional model simulation over a predefined time period with a predefined forcing dataset (as in e. g. CryoGrid 2 and 3). RUN_INFO classes, on the other hand, organize the model simulations. Depending on which RUN_INFO class is selected, they can for example launch only a single TILE class, or several TILE classes (e.
2. 2. 9). Interactions between stratigraphy classes are realized by interaction classes (Fig. 2 ) that compute fluxes across the boundaries between pairs of stratigraphy classes. Thus, compatibility of two stratigraphy classes (i. e. if they can border each other in the CryoGrid stratigraphy) can be ensured by providing a dedicated interaction class. Note that the compatibility depends on the order of the two classes in the stratigraphy. In particular, interaction classes compute fluxes across boundaries between stratigraphy classes which are required for computing time derivatives of state variables in the prognostic step of the time integration. For example, if two stratigraphy classes have temperature as state variables and the two modules are connected through heat conduction, the interaction class computes the conductive heat flux between the adjacent grid cells of the classes.
This work was supported by  Nunataryuk  ( EU  grant agreement no. 773421 ),  Permafrost4Life (Research Council of Norway , grant no. 301639 ),  ESA Permafrost_CCI  (https://climate. esa. int/en/projects/permafrost/),  PCCH-Arctic (Research Council of Norway , grant no. 320769 ),  ClimaLand  ( EEA/EU  collaboration grant between  Norway  and  Romania 2014-2021 , project code  RO-NO-2019-0415 , contract no. 30/2020 ). Jan Nitzbon ,  Simone M. Stuenzi , and  Moritz Langer  are supported through a grant by the  Federal Ministry of Education and Research (BMBF) of Germany  (No. 01LN1709A ,  Research Group PermaRisk ). Brian Groenke  acknowledges the support of the  Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS). Louise S. Schmidt  was funded by the  Research Council of Norway  through the  Nansen Legacy  project ( NFR-276730 ).
In this paper, firstly an architecture for ANM scheme is designed considering Li-ion BESSs as one of the FERs in an existing smart grid pilot (Sundom Smart Grid, SSG) in Vaasa, Finland. Further, Li-ion BESS controllers are designed to be adaptive in nature to include its aging characteristics, i. e. tracking the changing peak power as the aging parameter, when utilised for ANM operation in the power grid. Peak power capability of the Li-ion nickel-manganese-cobalt (NMC) chemistry-based battery cell has been calculated with the experimental data gathered from accelerated aging tests performed in the laboratory. Impact of such aging aware and adaptive Li-ion BESS controllers on the flexibility services provision for power system operators needs will be analysed by means of real-time simulation studies in an existing SSG pilot.
(  10 ) and  (11). Similarly, charge PPs were calculated with the help of Eqs. (  12 ) and (  13 ), which is represented by Fig. 10. A script was written in Matlab/ Simulink interface for this purpose. The calculated PP values were then stored in a two-dimensional lookup table whose output will be controlled by SOC and aging parameters and then integrated to the BMS control block. R dis = ΔV dis ΔI dis = V t1 -V t10 I t1 -I t10 ( 10 ) PP Dis = V min *(OCV -V min ) R dis (11) R cha = ΔV cha ΔI cha = V t1 -V t10 I t1 -I t10 ( 12 ) PP cha = V min *(OCV -V maz ) R cha (13).
Two RC combinations are suggested for modelling Li-ion battery cell, so that the dynamic behaviour is modelled as R 1 , C 1 , R 2 and C 2. The model parameters (OCV, R i , R 1 , C 1 , R 2 and C 2 ) are obtained by HPPC tests  [37] , i. e. the HPPC tests were run at different SOC's (from 0 % to 100 % at 10 % intervals) and at different aging intervals (i. e. at 0, 500, 800, 1200 and 1600 cycles of operation). The battery cell cycle aging test was performed at 1C charge/discharge rate and 25 • C  [37]. A    Matlab script was developed to calculate the measurements of SOEC parameters from HPPC test voltage response (Fig. 8 ) repeated for all the HPPC curves (i. e at all the aging intervals).
The physically-based, spatially-distributed hydrometeorological model SASER, which is based on the SURFEX LSM, is used to model the hydrological cycle in several domains in Spain and southern France. In this study, the modeled streamflows are validated in a domain centered on the Pyrenees mountain range and which includes all the surrounding river basins, including the Ebro and the Adour-Garonne, with a spatial resolution of 2. 5 km. Low flows were found to be poorly simulated by the model. We present an improvement of the SASER modeling chain, which introduces a conceptual reservoir, to enhance the representation of the slow component (drainage) in the hydrological response. The reservoir introduces two new empirical parameters. First, the parameters of the conceptual reservoir model were determined on a catchment-by-catchment basis, calibrating against daily observed data from 53 hydrological stations representing near-natural conditions (local calibration).
Streamflow data The observational streamflow database used was gathered by the EFA210/16 PIRAGUA project  (Zabaleta et al. , 2022). It consists of daily streamflow records from the different river basin authorities that manage the water in the study area. For our analysis, we considered a final database comprising 392 gauging stations that encompasses the temporal period between September 1979 and August 2014 (35 years), of these, 104 were selected as natural and near-natural gauging stations analyzing the data and metadata, as indicate the Fig. 1a  with black circles. The selection was carried out following the criteria below: 1. Only stations with at least 20 years of data within the analysis period were considered. 2. Data gaps of less than 10 % of the total record length (these gaps were not filled) were allowed.
Description of SASER model SASER (SAfran-Surfex-Eaudysee-Rapid) is a distributed and physically-based modeling chain consisting of a meteorological forcing, a LSM, and a routing scheme. Système d'Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN)  (Durand et al. , 1993)  is a meteorological analysis system that produces the meteorological gridded forcing dataset using an optimal interpolation algorithm which exploits the output of a meteorological model as first guess, and in-situ observations. SAFRAN provides hourly meteorological data which is then ingested by the LSM. The SAFRAN dataset used here is PIRAGUA_atmos_analysis (Quintana-Seguí & Le Cointe, 2022), which was created within the EFA210/16 PIRAGUA project and corresponds to a union of the French  (Quintana-Seguí et al.