dataesr/scientific-paragraphs-categorization huggingface dataset:
We additionally use previous work on the CBM algorithm (Marmoret et al. , 2022b) as a baseline. All state-of-the-art algorithms use beat-aligned features, except Grill and Schlüter (2015), who use a fixed hop length and Wang et al. (2021), who use downbeataligned features. Results for Foote (2000); McFee and Ellis, (2014a); Serrà et al. (2014) are computed with the MSAF toolbox (Nieto and Bello, 2016), and realigned on downbeats in post-processing. Results for the CNN (Grill and Schlüter, 2015) are extracted from the 2015 MIREX contest. Results for McCallum (2019); Wang et al. (2021); Dataset F 0bar F 0. 5s F 1bar F 3s SALAMI-test 45. 44% 42. 00% 60. 09% 60. 61% RWC Pop 65. 17% 64. 44% 81. 02% 80. 64% Salamon et al. (2021) are copied from the respective articles. Figures 12 and 13 compare the results obtained with the CBM and the Foote-TF algorithms with.
ORFs with unknown functions are cross-hatched. Genbank accession numbers of type members of the viral genera are provided. ORFs were predicted Figure 1. Genomic organization of episomal and endogenous Caulimoviridae. Schematic representation of the genomes of petunia vein-clearing virus (PVCV, type member of genus Petuvirus), Commelina yellow mottle virus (ComYMV, type member of genus Badnavirus), cauliflower mosaic virus (CaMV, type member of genus Caulimovirus), Wendovirus 1 (Wendo1, type member of tentative genus Wendovirus)  [16] , Lotus japonicus A virus (LjapAV, member of tentative genus florendovirus)  [17] , and Oryza sativa B virus components A and B (OsatBV compA&B, a florendovirus with a bipartite genome)  [17]. Light gray boxes mark open reading frames, and colored regions within ORFs are conserved protein domains: blue is the viral MP domain; green is a zinc-finger domain within the coat protein; red is the retropepsin (pepsin-like AP) domain; orange is the reverse transcriptase domain; and yellow is the RNaseH1 domain.
ORFs with unknown functions are cross-hatched. Genbank accession numbers of type members of the viral genera are provided. ORFs were predicted using ORFinder (https://www. ncbi. nlm. nih. gov/orffinder/, accessed on 1 March 2023), and conserved domains were predicted using MOTIF search (https://www. genome. jp/tools/motif/, accessed on 1 March 2023) with Pfam and CDD database. Over the last 10 years, there have been a growing number of studies on ECVs, aided by exponential growth in the number of plant genome sequence resources. This review aims at providing an overview of the current knowledge of the diversity, host range, abundance, and genomic distribution of ECVs. We also discuss their putative functions, integration modes, and the way they inform the long-term evolution of the Caulimoviridae.
Firstly, no matter how accurate the reconstruction of an ancestral viral genome sequence is, the replication of these sequences has not been experimentally verified. However, the same is true for the vast majority of new virus species that are recognized purely on the basis of a novel genome sequence generated by high throughput sequencing without supporting biological information such as evidence of infectivity, virion morphology, mode of transmission, and sometimes even knowledge about the host organism in the case of viruses discovered in environmental samples  [20, 21]. Secondly, for a virus species or genus to be formally recognized, a unique GenBank accession has to be mandatorily provided in the TaxoProp database. A scaffold of the host genome with nucleotide coordinates of the element is not sufficient.
This problem has been overcome for members of the Metaviridae by depositing sequences in the Third-Party Annotation database of the International Nucleotide Sequence Database Collaboration (M. Krupovic, pers. comm. ) Two alternative naming conventions have been suggested for ECVs. Staginnus et al. , 2009  recommended that when a cognate exogenous virus is not known to exist, then the EVEs should simply be labeled using the host plant initials followed by the suffix 'EPRS', whereby EPRS stands for endogenous pararetrovirus sequence. However, this naming convention fails to satisfy two of the most important objectives of a classification scheme: (i) describe how the various EVEs are related to each other and to exogenous viruses, and (ii) provide an ability to predict the essential features of the EVE based on referral to the name.
However, the source dataset was later reported to contain some contaminated samples (http://gigadb. org/dataset/100910, accessed on 1 March 2023); hence, this result needs to be substantiated. Overall, ECVs have been reported in all tracheophyte divisions but not in chlorophytes nor in more basal clades of streptophytes, for which only a limited number of genome and transcriptome sequences are available. Several factors may explain why members of the Caulimoviridae are limited to vascular plants. Plant organs of very primitive plants differ markedly from those of the tracheophytes, especially by the absence of a vascular system, which plays a key role in the infection process  [28]. Tracheophytes and more basal lineages also differ in the structure of their plasmodesmata  [29] , which are essential to the long-distance transport of viral RNAs bound to viral movement proteins such as those encoded by members of Caulimoviridae  [30].
Epigenetics and Silencing The epigenetic status of ECVs has only been addressed in a few angiosperms  [77, 78]. Like most repetitive DNA elements, ECVs are subjected to DNA methylation and histone modifications (i. e. , H3K9me2), which are hallmarks of transcriptionally silent heterochromatin environments  [79]. Furthermore, in silico mining of small RNA (sRNA) datasets and actual sequencing experiments have shown that a heterogenous population of 18-30 nt sRNAs that are homologous to ECVs are present at variable densities in a large range of angiosperms  [22, 31, 68, 69]. This pool of sRNAs is probably composed of subcategories acting in different silencing pathways, including transcriptional gene silencing (TGS) and post-transcriptional gene silencing (PTGS). For instance, the predominant function of plant 24-nt sRNAs is to guide the canonical RNA-directed DNA methylation (RdDM) pathway, while 21-22 nt sRNAs are usually involved either in non-canonical RdDM pathway or in PTGS  [80] [81] [82].
Conclusions Almost 25 years after their discovery, ECVs have proved instrumental for our understanding of the long-term evolution of the Caulimoviridae and the dynamics of plant genomes through horizontal gene transfer. However, we still have only a very fragmented picture of their contribution to plant genomes and their diversity and virtually no indication of their impact on plant biology. Despite their ubiquity and abundance, ECVs remain largely neglected features during plant genome annotation because of a lack of dedicated tools for their proper annotation, hampering the development of paleovirology approaches. CAULIFINDER, a bioinformatics tool, was recently developed for the automated detection and annotation of ECVs in plant genomes  [99] , jointly with databanks that can be used to help with the classification of repetitive elements produced by standard repeat annotation pipelines.
Macroevolution of Caulimoviridae according to Diop et al. [11] (A) and Gong and Han [23] (B; modified from Gong and Han, 2018). Cladograms indicate the main divisions of euphyllophytes. The position of the hypothetical last common ancestor (LCA) of Caulimoviridae is shown by a red dot. The red and blue arrows represent vertical transmission and host swaps, respectively. Graphic representations of plants were retrieved from the Phylopic database (https://www. phylopic. org/, accessed on 1 March 2023). The left cladogram represents the evolutionary relationships between major classes of vascular plants. The upper cladogram represents the phylogeny of the Caulimoviridae based on the work of Diop et al.
Introduction Normalization issues of existing 16 O(n,α) 13 C reaction cross section have generated renewed interest on measuring the (n,α) cross section data. The Nuclear Energy Agency High Priority Nuclear Data Request List demonstrate the interest on accurate 16 O(n,α) crosssection data for practical applications. [1]  Recent experimental data measurements, namely transmission and (n,α), have motivated revising the 16 O resonance region evaluation. Issues with the normalization of the (n, α) cross sections have been investigated and a very dependable measurement has been carried out at the GELINA facility at the Joint Research Center (JRC) Geel from the energy threshold 2. 354 MeV to 9 MeV. Additionally, transmission measurements were done at the nELBE time-of-flight (TOF) facility at Helmholtz-Zentrum Dresden -Rossendorf (HZDR).
Experimental data used in previous evaluations were also considered in the evaluation. The resonance evaluation was performed in the energy range from 0 eV to 6 MeV using the reduced R-matrix Reich-Moore methodology of the computer code SAMMY resulting in a set of resonance parameters (RPs) that describes well the experimental data used in the evaluation. The recent transmission measurements and the (n,α) cross section data are well reproduced. The RPs were converted to the evaluated nuclear data file (ENDF) format using the R-Matrix Limited format option LRF=7 that allows adding information that could not be accommodated in other ENDF representation of RPs. The intent of the paper is to describe the procedures used in the evaluation of the RPs and the use of the RPs in calculations of critical benchmark experiments.
Experimental data Resolved resonance evaluation of 16 O cross section using the code SAMMY  [2]  in the energy range from thermal to 6 MeV has been reported in Reference  [3]  which addresses concerns with regard thermal capture and scattering cross section data, coherent scattering data and present an attempt to quantify the normalization issue in connection to the (n,α) cross section. Two sets of resonance parameters, named low and high, from which the calculated (n,α) cross sections differed by ~30 %. From these sets of resonance parameters benchmark calculations of systems sensitive to the (n,α) cross section were carried out. Improvements on the keff, in comparison with results of (n,α) cross section evaluation available in the ENDF/B-VII. 1 are observed with a better performance to the set of resonances named low.
Benchmark results The 16 O evaluation presented in this paper has been used for benchmark calculations of systems sensitive to the 16 O cross section data. Criticality calculations, keff, were performed using the MORET 5 Monte Carlo transport code  [17]  and various nuclear data libraries (JEFF-3. Only results for the JEFF-4T1 library are reported in this paper. The impact of the new oxygen evaluation was tested by including the new oxygen evaluation in the JEFF-4T1 library. The characteristics of the benchmark cases along with the keff results are reported in Table  3. Figure  2  also plots the keff results versus the energy corresponding to average lethargy of neutrons causing fission (EALF). The experimental uncertainty at the 1s level is indicated using red dotted lines. The comparison of JEFF-4T1 results with those where oxygen is replaced by the old evaluation in JEFF-4T1 library indicates an improvement on the average keff values.
Both theoretical  (Domínguez-García et al. , 2019; Kéfi et al. , 2013)  and empirical studies  (Eby et al. , 2017; Majumder et al. , 2019)  have suggested that both these key aspects of resilience tend to decline as ecosystems approach points at which they become vulnerable to shifts in function or structure  (Capdevila et al. , 2021; Hodgson et al. , 2015; Ingrisch & Bahn, 2018; Weinans et al. , 2021). We measured time series autocorrelation as a proxy of recovery  (Forzieri et al. , 2022; Smith et al. , 2022)  and sensitivity as a proxy of resistance  (Seddon et al. , 2016). To do this, we used time series vegetation data of the MODIS Enhanced Vegetation Index (EVI) and climate data from 2000 to 2020. The difference in the two metrics was calculated between the second half of this time frame  (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019) (2020) , relative to the first half  (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010)  in order to identify which regions are experiencing shifts in resilience.
Time-series data We extracted the data from different spatial sources using the sites described previously. Monthly time series from 2000 to 2020 were used. EVI and pixel reliability was extracted from the MOD13C2 product  (Didan & Huete, 2015) , downloaded from the LAADS database, at a 0. 05° (approximately 5 km) resolution. Cloud-fraction was extracted from NASA Earth Observations, as a monthly composite of the MOD06 product (MODIS Atmosphere Science Team, 2015), at a 0. 1° (approximately 10 km) resolution. Monthly Minimum and Maximum temperature, as well as Actual Evapotranspiration (AET, derived using a one-dimensional soil water balance model), and Potential Evapotranspiration (PET, obtained through ASCE Penman-Monteith method), were extracted from the TerraClimate database  (Abatzoglou et al.
, 2018) , at a 0. 05° resolution. The TerraClimate database is a monthly interpolation from WorldClim and the Climatic Research Unit, with data available up to the end of December 2020. EVI pixels were included in the analysis when the quality was "good", or "marginal" (citation). Our temperature variable was obtained by averaging each monthly minimum and maximum temperature. Water-availability was estimated as the monthly ratio of AET over PET  (Seddon et al. , 2016). Cloudfraction is a monthly proportion of cloud-coverage for each pixel, and was our cloudiness index. Our study focuses on the period where all data were available, which was from March 2000 to December 2020 (both included). Data were then sampled at each monitored site.
Processing time series for sensitivity index calculation To estimate sensitivity, we used the methodology developed by  Seddon et al. , (2016) , with modifications to increase robustness to outliers and to ensure that sensitivity was comparable across our study period. We generated detrended time series, where seasonal and long-term temporal trends were removed. To do this, we first removed seasonal effects by subtracting from each observation the mean value of its month during the monitored period  (2000-2010 and 2010-2020). Then, to remove any signal of longer-term time trends, we linearly regressed the deseasonalized time series against time. We extracted the residuals from this linear regression model, and then normalised them. This produced a dataset of monthly anomaly scores (Z-scores), which measures how extreme an observation is given its position in the time series.
We performed this procedure separately for every monitored location and variable (EVI, temperature, water availability and cloudiness). Each time series at each site was split into two parts, one time series from 2000 to 2010, and one time series from 2010 to 2020, and the following analysis (aimed at calculating sensitivity and autocorrelation) was performed on each dataset, to obtain two measures per site to be compared (one per decade of available data). Months with mean EVI < 0. 1 and mean temperature < 0°C were considered dormant, and were excluded from the analysis (see  Seddon et al. , 2016). The sensitivity index for each climate variable was calculated using eq. (1), where 𝑣𝑎𝑟(𝐸𝑉𝐼) and 𝑣𝑎𝑟(𝑐𝑙𝑖𝑚) are the corrected variance for EVI and a given climate variable (temperature, water availability and cloudiness), and 𝛽 𝑐𝑙𝑖𝑚 is a weight that measures how closely related are anomalies in EVI and in that climate variable.
Such a process is not suitable to test AR1, as the time series should not be segmented. Nevertheless, our AR1 patterns reflect trends found in  (Forzieri et al. , 2022; Smith et al. , 2022). As we uncovered relationships between change in climatic averages over the period and change in resilience metrics, we used change in temperature from WorldClim in the model, instead of the initial time-series for the other variables. We further tested for statistical artifacts between the resilience metrics and trends in each component of the time series. To do this, we simulated time series of each variable, with a uniform random distribution for EVI, water availability, and cloud-cover, and a linear positive trend in temperature. All values were simulated within bounds comparable to those of the initial dataset.
Estimating drivers of the change in resilience using machine-learning (XGBooster) First, we compared gains/losses in resilience between different biomes and forest types  (Dinerstein et al. , 2017). To identify if each biome tends to gain or lose resilience, we tested the significance of the change in sensitivity and autocorrelation through a Wilcoxon-Mann-Whitney test, as the normality conditions were not respected. To compare changes in resilience over time of different biomes, we performed pairwise permutation tests of both changes in sensitivity and autocorrelation between biomes with 100 permutations. To determine what is driving changes in sensitivity and autocorrelation within or across each biome, we used a machine learning based model: XGBooster, from the "xgboost" package  (Chen & Guestrin, 2016).
a. Data A number of static and dynamic variables were considered as potential drivers of resilience changes. The static drivers are detailed in Table  1  and are related to site-specific characteristics of a given pixel, either historical climate (averaged for the period 1970-2000), vegetation structure, topography, soil, or human influence. We included drivers that have been previously associated with an intrinsic mechanism of ecosystem resilience (vegetation structure, topography, aridity), or can be an external variable affecting these mechanisms (human influence, change in climate). The specific rationale for including each static variable, including references that support this inclusion are depicted in Supplementary Table  1. The dynamic variables used were changes in average temperature, water availability, cloudiness, EVI, and vegetation structure during the studied period  (2000-2010 vs 2010-2020) , as we expect climate, productivity (here, remote-sensed greenness) and land-cover change, to impact resilience.
A decrease in productivity can also be a valuable indicator of land degradation  (Burrell et al. , 2020; Eckert et al. , 2015) , however, it is important to recognize that this relationship is complex and context-dependent  (Easdale et al. , 2018; Yengoh et al. , 2016). The cloudiness, water availability and EVI changes were computed from the time series used to estimate sensitivity. Temperature change was calculated from the WorldClim database  (Fick & Hijmans, 2017). We removed dormant months when calculating the overall average for water availability, cloudiness and EVI, based on the same criteria from the sensitivity analysis for each site (decadal monthly average of temperature < 0°C and EVI < 0. 1). The changes in each of these variables were calculated as the difference between the 2010-2020 (2010-2018 for temperature from WorldClim) decadal mean and that of 2000-2010 derived from monthly means.
Changes in vegetation structure were estimated as changes in land cover between 2001 and 2020 using the MODIS land cover database  (Friedl & Sulla-Menashe, 2019). Land cover types were re-classified into three vegetation structures: Forests, Savannas and Grasslands, and each change from one to another was used as a potential driver in the model. To remove very highly correlated variables, only one of every highly correlated pair of variable (i. e. |ρ| > 0. 7) was kept in the final model. Mean Annual Temperature was correlated to Soil Carbon Content, and Mean Annual Precipitation was correlated to Aridity Index. Temperature has been shown to impact resilience  (Feng et al. , 2021)  and Aridity has been widely used to characterise various ecosystems  (Berdugo et al. , 2020; Tijerín-Triviño et al.
b. Machine-learning model parametrization The functioning of our model is conditioned by hyper-parameters, which determine the way in which the decision tree is constructed. Minimum child weight and Minimum splitting loss both change the degree to which the algorithm can create new nodes. Learning rate, which is how much the algorithm weights each driver, and Maximum depth, changing the size of the tree, for these two, high value can cause overfitting. Subsample size is a ratio of the training data used by the model, sampled once every tree generation, and can prevent overfitting. To estimate the best values for these parameters, a Bayesian optimization was used, from the "ParBayesianOptimization'' package  (Snoek et al. , 2012)  to minimise RMSE on the validation dataset using a cross validation procedure, thus preventing overfitting.
Finally, to add transparency, an interactive interface to explore relationships between SHAP values and variables included in the regressive model, as well as interactions, is available on this link: "https://cfournierdel. shinyapps. io/SHAPValuesFournier/". To test if observed patterns were homogeneous across vegetation structures, the regressive model was also run for 3 separate vegetation types: forests, savannas and grasslands, based on the classification of the MODIS land cover database in 2001. To improve the robustness of the patterns uncovered with SHAP values from the continuous regressive model, we ran a binary classifier using the points identified as losing or gaining sensitivity. To do that, we used a XGBOOST model with a binary logistic regression, and optimized our hyper-parameters following the same Bayesian process described previously.
Thresholds for SHAP Values Visual inspection of SHAP value plots suggested the possible presence of aridity and change in temperature thresholds (i. e. , points in the gradient where there is a sudden change in either slope or intercept of the relationship between the driver and its associated SHAP value). To verify this quantitatively, we used R package "chngpt"  (Fong et al. , 2017)  to identify points at which the relationship between SHAP values and drivers changed abruptly. To reduce the effect of outliers when identifying these thresholds, points were weighted based on Mahalanobis distance to the mean of the overall distribution for aridity or temperature  (Berdugo, Gaitán, et al. , 2022; Mahalanobis, 1936). We ran this analysis on SHAP values for the whole dataset, as well as specifically for forests, savannas and grasslands.
Acknowledgements: GRS acknowledges funding from  DOB Ecology  and from the  Marc R. Benioff Revocable Trust , which, in collaboration with the  World Economic Forum , made this work possible. M. B. is supported by a  Ramón y Cajal  grant from the  Spanish Ministry of Science  ( RYC2021-031797-I ). We wish to thank  Tom Lauber  for his assistance with the analysis.
Phylogenetic inference and major relationships within Hymenoptera Our phylogenetic results are covered in detail in the Supplementary Discussion. In brief, to infer a robust phylogeny for Hymenoptera, we assembled our data set in several arrangements with varying levels of missing data and phylogenetic information content. We first filtered all captured UCE loci for taxon completeness, retaining a 50%, 60%, and 70% complete matrix (nuc-50% = 1118 loci, nuc-60% = 765 loci and nuc-70% = 446 loci) for further analyses. UCE loci were also matched to a reference protein database, and a 50% taxoncomplete set (=324 loci) of protein-coding loci was created, which was further analyzed as nucleotide (prot-NUC) and amino acid (prot-AA) data set. These five matrices (nuc-50%, nuc-60%, nuc-70%, prot-AA, prot-NUC) were then analyzed via partitioned and unpartitioned analyses in a maximum likelihood framework.