dataesr/scientific-paragraphs-categorization huggingface dataset:
(  11 ) is expected to provide an upper limit for the uncertainty in the absorption Ångström exponents. As a result of the application of Eq. (  11 ) to our dataset, we estimated the root mean square error in both AAE 440/870 and AAE 675/870 as 0. 12. Note that a probable overestimation of the uncertainties in AAEs can result in some biases of the a posteriori estimates of the retrieved properties toward the a priori ones. However, as argued in Sect. 4. 3, such biases are unlikely to be significant in our case. Note also that since possible uncertainties in our a posteriori estimates due to uncertainties in our simulations are addressed through the random variations in the model pa-rameters, the part of the model uncertainties contributing to σ 1-3 is assumed to be negligible in comparison with the observation error.
Validation of the retrieval algorithm Next, we examine the capability of our method to retrieve δBrC, k OA , MAE OA , and the BC / OA ratio from perfectly accurate (ideal) observations of AAEs and SSA by using the corresponding perfect calculations which take into account random variations in several aerosol parameters as discussed in Sect. 2. 1. To this end, we test our algorithm using synthetic data which are intended to best represent the properties of real BB aerosol in Siberia. The synthetic dataset was formed from our simulations included in the look-up table described in Sect. 2. 1. For each data point from our AERONET dataset, we selected a simulation that minimized the mismatch, ε f , between the observations and simulations. The mismatch was defined as follows: ε 2 f = (z o -z m ) T (z o -z m ).
(12) An additional criterion was that the absolute difference between any component k of z o and z m could not exceed the corresponding standard deviation σ k (see Sect. 3). We presumed that observations that do not satisfy this criterion are not sufficiently represented in our simulations. As a result, the set of our synthetic observations includes 96 data points (of 115 data points in the original AERONET dataset). These data points were excluded from the look-up table and were then used to obtain the a posteriori estimates of the unobserved parameters following Eq. (  9 ). The retrievals that can be obtained using Eqs. (  6 )-(  9 ) depend on the standard deviations σ k , which, as noted above, represent both the observation and model errors. Therefore, if the observations are perfect (as we assume), σ k is determined entirely by the model error.
3), presuming that the difference between the root mean square errors for the AAE and SSA partly reflects the distinction between the natural factors contributing to the variability in these parameters in both the observations and the simulations. Second, we scaled these estimates of σ 1-3 with a constant factor (f s ), which was optimized by minimizing the mean square difference between the "true" values of k OA from the synthetic dataset and the corresponding retrievals of k OA. The optimal value of f s was found to be equal to 0. 20. Then the error inherent to our Bayesian algorithm (with a look-up table consisting of 10 6 simulations) can roughly be estimated as a fifth part of the observation uncertainty. Note that the dependence of the errors in our retrievals of k OA and other parameters on f s is found to be rather weak.
Note also that, since we optimized only one parameter (f s ) and our test dataset is relatively large, the performance statistics for our retrieval algorithm could hardly change significantly if our test dataset were split into optimization and validation subsets (as would probably be necessary if a larger number of parameters of the retrieval algorithm were optimized using the same data). Figure  3  demonstrates the relationships between the values of the observed properties of BB aerosol (AAE 440/870 , AAE 675/870 , and SSA 440 ) from the set of synthetic data and the corresponding retrieved values. The agreement between the true and "retrieved" data is nearly perfect, indicating that the approximation error is rather small. (But note that it would be larger if the Monte Carlo simulations were less numerous.
) The capability of our method to retrieve the unobserved properties of BB aerosol (δBrC, k OA , MAE OA , and the BC / OC ratio) from the perfect observations of AAE 440/870 , AAE 675/870 , and SSA 440 is demonstrated in Figs. 4  and 5. Specifically, Fig. 4  shows examples of CPDFs calculated for the four parameters inferred from one point of the synthetic dataset. The synthetic data point originally corresponds to the AERONET observations performed on 26 July 2014. The CPDFs were computed according to Eq. (  10 ) using three different combinations of the constraining observed properties, such as (i) the two AAEs and SSA 440 (standard constraints), (ii) only two AAEs, and (iii) AAE 440/870 combined with SSA 440. The CPDFs calculated from a priori assumptions without using any observational constraints are also shown in Fig.
Application to the AERONET data In this section, we discuss the application of our method to the original AERONET data. Figure  7  shows a comparison of the original values of the observed properties of BB aerosol with the corresponding calculations processed with Eq. (  9 ). First of all, it should be noted that these calculations tacitly assume that the observations can be closely reproduced by some (or, ideally many) samples of the model data from the generated look-up table (see Sect. 2. 1). How-  ever, this proves to be not always possible (due to, e. g. , unusual composition of the observed aerosol). To exclude such situations, we used a criterion similar to one applied to the synthetic data. Namely, we required that the absolute difference between any component k of the observation vector (z o ) and its model counterpart (z m ) should not exceed the corresponding standard deviation σ k (estimated as discussed in Sect.
8a ), and the similar differences for k OA , MAE OA , and the BC / OA ratio are also rather small (compared to the confidence intervals). These facts indicate that the biases in our a posteriori estimates of the unobserved characteristics due to random uncertainties in the AERONET data are not very significant. Our estimates of the BC / OA ratio are, in most cases, also higher than the alternative estimates (see Fig. 9b ), which can be because the relationships by  Pokhrel et al. (2016)  are obtained for fresh aerosol and can therefore underestimate SSA for aged aerosol (with the same BC / OA ratio) if the aging results in BrC losses. Nonetheless, there is a rather high correlation (r > 0. 68) between the two kinds of estimates, indicating that the variability in both kinds of estimates is driven by the same factors.
Overall, we find the different estimates of δBrC and BC / OA to be in reasonable agreement and consider this comparison as evidence for the robustness of our estimates. Note that both alternative methods show inferior performances when applied to the synthetic dataset used above in comparison with our algorithm. Finally, we examined some relationships between the variables retrieved with our method (see Fig. 10 ). Specifically, Fig. 10a  reveals a close relationship between the OA IRI (k OA ) and δBrC. Similarly to the dependence of δBrC on AAE 440/870 (see Fig. 8a ), this relationship indicates that variations in the OA IRI are a key factor determining the variability in the BrC absorption of Siberian BB aerosol. Not surprisingly, δBrC is also affected by variations in the BC / OA ratio, depending inversely on it (see Fig.
It should be noted, however, that the current application is limited by observations of dense BB plumes (having AOD at 550 nm larger than 0. 8), and so our estimates may not be representative of BB aerosol in highly diluted (and strongly aged) plumes. Overcoming this limitation is challenging and would require good knowledge of the optical properties of the background aerosol as well as the use of the potentially less accurate Level 1. 5 AERONET data instead of the quality-assured Level 2. 0 data (which are not representative of low-AOD conditions). Future developments of our method may also include (1) an enhancement of the number of the observed characteristics considered the components of the observation vector in the Bayesian analysis, (2) accounting for a possibility of external mixing of BC and OA within the Mie theory calculations, and (3) improving the representation of the absorption properties of coarse particles.
The potential of the suggested Bayesian approach to the investigation of BrC absorption should be explored further using observations in other regions of the world. Code availability. OPTSIM software is available at https://www. lmd. polytechnique. fr/optsim/ (last access: 6 May 2021,  Menut, 2021). Data availability. The AERONET data for the Tomsk_22 site are available from https://aeronet. gsfc. nasa. gov/cgi-bin/data_display_ inv_v3?site=Tomsk_22  (Holben, 2021). The AERONET data for the Yakutsk site are available from https://aeronet. gsfc. nasa. gov/ cgi-bin/data_display_inv_v3?site=Yakutsk  (Holben et al. , 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-14-6647-2021-supplement. Author contributions. IBK designed the study and the method to analyze optical observations, contributed to the analysis of AERONET data, and prepared the paper.
NAG developed computer codes for the Bayesian analysis and conducted simulations with the OPTSIM software. MVP contributed to the preparation of the AERONET dataset. MB and MOA contributed to the discussion of the results and the preparation of the paper. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. The authors acknowledge the free use of the AERONET data available from https://aeronet. gsfc. nasa. gov. Financial support. The development of the BrC estimation method was supported by the  Russian Science Foundation  (grant no. 19-77-20109 ). The analysis of the retrieved optical properties of BB aerosol was supported by the  CNRS   International Emerging Actions program  no. 304365  (project  MERSI ) and the  Russian Foundation for Basic Research  (project no. 21-55-15009 ). Review statement. This paper was edited by  Alexander Kokhanovsky  and reviewed by two anonymous referees.
The role of endophytes in plant health is, however, more complicated than it first seems. Despite their ubiquity, the endophytic lifestyle remains somewhat of a mystery. While there are aforementioned examples of mutualistic endophytes found only to benefit the plant host, other fungi described as endophytes are found to be latent pathogens or decomposers (saprotrophs)  (Slippers and Wingfield, 2007; Promputtha et al. , 2010; Delaye et al. , 2013; Nelson et al. , 2020). This concept-that the term endophyte represents a range of functional roles within the plant host-has been referred to as the endophytic continuum  (Schulz and Boyle, 2005). Of the 366 species classified as endophytes in the FUNGuild database as of December 2020: 140 (∼38%) are also classified as plant pathogens; 16 as saprotrophs (∼4%); and 17 (∼5%) as other various guilds  (Nguyen et al.
Up to 12 colonies were randomly picked and amplified in secondary PCR with the above cycle conditions and primers. After examination with gel electrophoresis (1% agarose in TE buffer), all PCR products were purified using ExoSAP-IT (USB, Hudson, OH, United States) and both strands sequenced using the same primers as for amplification. Sequence reactions were carried out in 5 µl volumes-consisting of 1 µl 5× sequencing buffer, 2. 05 µl ddH 2 0, 1 µl primer (1. 25 µM), 0. 5 µl purified PCR product and 0. 45 µl BigDye Terminator v3. 1 (Applied Biosystems, Foster City, CA, United States)-and run on an ABI 3730 DNA Analyzer (Applied Biosystems, Foster City, CA, United States). Sequences were manually edited with contiguous alignments using Geneious R7 v7. 1. 5 (Biomatters, New Zealand). Sequences from 642 endophytes (235 cultures, 280 direct sequences, and 127 clones) have been deposited in GenBank under accession numbers MW298868-MW299510.
(2019). Singletons-OTUs comprising one sequence-were not discarded, as is common practise to reduce artefacts when using NGS datasets, because each sequence originated from Sanger sequencing of an individual seed extraction, and so was assumed to be "real. " Preliminary identification of OTUs was made via a local BLASTn v2. 6. 0 search  (Camacho et al. , 2009)  against the UNITE v8. 2 database, release 04. 04. 2020  (Abarenkov et al. , 2020). Taxonomic identification of OTUs was inferred from the top UNITE hit, guided by  Vu et al. (2019) : ≥99% similarity for the same species; ≥98 similarity for the same genus; ≥96 similarity for the same family; ≥94 similarity for the same order; ≥92 similarity for the same class; and <92% similarity for the same phylum. Similarity-based identification was corroborated with a phylogenetic approach via the Tree-Based Alignment Selector (T-BAS) toolkit v2.
Sampling Effort and Community Analysis For the purpose of these analyses, Musa subspecies and varieties were grouped under the same species. Sampling effort was assessed by producing species accumulation curves of the number of OTUs for the number of Musa accessions using the rarefaction method in the specaccum function from the R package vegan v2. 5-6  (Oksanen et al. , 2019). This was done including and excluding singleton OTUs for all Musa accessions (n = 45) as well as distinguishing between the three best sampled species-M. acuminata (n = 12), M. balbisiana (n = 16), and Musa itinerans (n = 14). The impact of detection method-culturing, direct sequencing or cloning-on species recovery was quantified with analysis of similarity (ANOSIM)  (Clarke, 1993)  using the vegan anosim function following confirmation that data dispersion was even using the vegan betadisper function.
The RBG, Kew accession and oil palm plantation accessions (1 locality in Malaysia) were excluded from the following analyses due to low sample size for the habitats and the former being a geographical outlier. Endophyte community composition was explored using non-metric multidimensional scaling (NMDS) implemented in the metaMDS function in vegan. OTU counts were filtered for the eight most common OTUs (abundance greater than 20) for the 33 accessions of M. acuminata, M balbisiana, and M. itinerans and six dimensions were selected for the NMDS using a scree plot (Supplementary Figure  1 ). Habitat information for Musa accessions was interpreted from the collection notes in the MSB's metadata records (Supplementary Table  1 ). To investigate the relationships between community composition and post-storage seed viability (i.
The PERMDISP null hypothesis is that there is no difference in dispersion between groups, and so a significant p-value indicates that dispersion is not consistent. In order to determine which of the habitats had significantly different community composition from the others, pairwise PERMANOVA was performed on both the subset of common taxa used in the NMDS as well as all OTUs including rare taxa. This was done using the pairwise. perm. manova function from the R package RVAideMemoire v0. 9-78  (Hervé, 2020)  with 999 permutations and multiple testing p-value correction using the Benjamini-Hochberg method  (Benjamini and Hochberg, 1995). Difference in diversity-according to Shannon and Simpson diversity indices, both calculated with the vegan diversity function-and abundance of fungi per Musa accession for each habitat was assessed using the TukeyHSD function.
Fusarium Phylogenetic Analysis Given the abundance of Fusarium in our dataset, a genus-specific phylogeny was reconstructed to elucidate the relationships of our Fusarium OTUs with already known species. While the UNITE identification described above recovered many 5. 8S-partial LSU OTUs to apparent species level, it has been shown that the ITS locus is not sufficiently variable for species delimitation within this particular genus  (Geiser et al. , 2004). For this reason, all cultures and direct extractions belonging to the genus, as identified with UNITE and confirmed by T-BAS analyses, were selected for additional amplification of the translation elongation factor 1-α (EF1α) gene using the primers EF1 and EF2  (O'Donnell et al. , 1998). Amplification and sequencing were performed as above, except for PCR cycle conditions, which were informed by da  Silva et al.
(2014). All new sequences were deposited in GenBank (MW319587-MW319636). OTUs based on EF1α sequences were also delimited as above. Representative sequences for each OTU from this study (as provided by USEARCH) were aligned with already published EF1α data and, in addition to EF1α, RNA polymerase II largest (RPB1), and second largest subunit (RPB2) sequences were also taken from the MycoBank website 2. Taxon sampling was guided by  O'Donnell et al. (2013) , with the addition of taxa from the Fusarium oxysporum species complex (FOSC)  (Maryani et al. , 2019)  and Fusarium musae  (Van Hove et al. , 2011)  and Neonectria coccinea and Cylindrocarpon cylindroides were selected as the outgroup (Supplementary Table  2 ). Sequences for each gene were aligned using MUSCLE v3. 8. 425  (Edgar, 2004)  and ambiguous regions were manually delimited and removed in AliView v1.
py script  (Kauff and Lutzoni, 2002, 2003)  run in Python v3. 7. 9 using Biopython v1. 78  (Cock et al. , 2009). Taxa responsible for conflicts were removed. The three loci were concatenated using SequenceMatrix  (Vaidya et al. , 2011)  and partitioned by gene, codon position and EF1α introns for the ML search, performed as above for individual gene trees-see https://github. com/Rowenah/MusaEndophytes for the raw alignment and tree files. Species names were checked in Species Fungorum 3  and the species tree was plotted in R using ggtree v2. 3. 4  (Yu et al. , 2017).
Were the Most Common Genera Not including duplicate clones, 642 sequences (GenBank accession numbers MW298868-MW299510) were clustered into 181 OTUs, of which 125 (69%) were singletons. Species accumulation curves including singleton OTUs were almost linear and with a high gradient, while the curves excluding singletons approached an asymptote, indicating that many rarer OTUs remain to be discovered but a considerable proportion of the most common OTUs were captured (Figure  1B ). Of the 181 OTUs, UNITE and T-BAS classified the vast majority to the Ascomycota (162, 90%), with a few belonging to the Basidiomycota (12, 7%) and the remaining as unclassified Fungi (7, 4%) (Supplementary Data Sheet 1). In almost equal proportion, most of the ascomycete OTUs fell in the classes Dothideomycetes, Eurotiomycetes, and Sordariomycetes (in order of abundance), in the respective orders of Botryosphaeriales, Eurotiales, and Hypocreales (Figure  2 ).
A relatively modest sampling effort was required to isolate the majority of common OTUs found across Musa species (Figure  1B ), in agreement with other microfungi community studies (e. g. ,  Paulus et al. , 2006; Tisthammer et al. , 2016; Vaz et al. , 2018) , but the vast majority of OTUs were singleton or lowabundance, a known phenomenon in microbial diversity  (Lynch and Neufeld, 2015; Jia et al. , 2018). A disproportionate number of rare taxa can obstruct community composition visualisation methods such as NMDS, and so low abundance taxa are often filtered out in order to visualise structural patterns (e. g. ,  Miller et al. , 2016; Huang et al. , 2018; U'Ren et al. , 2019). This is distinct from the practise of removing rare/singleton OTUs from high throughput sequencing datasets in case of sequencing artefacts (e.
This is also supported by the fact that the Shannon index, which is sensitive to rare species, found a significant difference in diversity, while the Simpson index, which is sensitive to abundant species, did not  (Morris et al. , 2014). These results come with the caveat that the habitats as defined in this study are interpreted from the MSB seed collection data, which were not recorded with any particular study design in mind, and as such some entries are more complete than others and there can be subjectivity in how to infer habitat from the collection notes. While the extensive metadata attached to natural history collections can be incredibly powerful for studying patterns of biodiversity  (Andrew et al. , 2018 (Andrew et al. , , 2019;; Funk, 2018; Pearce et al. , 2020) , the application of that data must be done with care.
ACKNOWLEDGMENTS We thank the  Malaysian Agricultural Research and Development Institute  and the  Vietnamese Plant Resources Center  for providing seeds to the MSB, in particular  Binti Tahir ,  M. Anuar Rasyidi ,  M. N. ,  Ahmad Syahman, M. D. ,  Mohd Shukri ,  M. A. ,  Suryanti ,  B. ,  Dang Toan Vu ,  Tuong Dang Vu ,  Le Thi Loan , and  Ngo Duc. We thank  Toby Tydeman ,  Khushboo Gurung ,  Emily Kennedy , and  Tom Bance  for help in processing samples. We are grateful to  Jana M. U'Ren  for useful discussion and Bryn T. M. Dentinger for his generosity and inspiration at the outset of the study. Thanks also to the editor and the reviewers  Jana M. U'Ren  and  Asha Janadaree Dissanayake  for their valuable feedback.
FUNDING This work was supported by the  Charles Wolfson Charitable Trust and Pragnell Fund  to EG; a sub-grant from the  University of Queensland  from the  Bill & Melinda Gates Foundation  project " BBTV mitigation: Community management in Nigeria , and screening wild banana progenitors for resistance" ( OPP1130226 ) to SK; and the  Royal Botanic Gardens Kew Pilot Study Fund  awarded to RH, who is a  Natural Environment Research Council  funded Ph. D. student with the  London NERC DTP (NERC Ref :  NE/L002485/1 ). The Royal Botanic Gardens Kew is part funded by  Grant in Aid  from the  UK Department for Environment, Food and Rural Affairs.
lant functional traits have proved useful in identifying life history strategies 1,2 for predicting plant community assembly 3, 4  and for assessing the impact of vegetation composition and diversity on ecosystem functioning  5, 6. Consequently, vegetation models including coupled climate-vegetation models benefit from a better representation of plant trait variation to adequately analyse terrestrial biosphere dynamics under global change  6, 7. Today, in combination with advanced gap-filling techniques 8 , databases of plant traits have sufficient coverage to allow quantitative analyses of plant form and function at the global scale 9. Analysing six fundamental traits, Díaz and colleagues 10 revealed that essential patterns of form and function across the plant kingdom can be captured by two main axes.
Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land-climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits.