dataesr/scientific-paragraphs-categorization huggingface dataset:
Code availability The numerical impact crater formation were made using the iSALE shock physics hydrocode. At present, iSALE is not fully open source. Application for use of iSALE can be made via https://isale-code. github. io/. Any recent stable release can be used to reproduce the data presented. We used the IDL 5. 2 software (L3Harris geospatial https:// www. l3harrisgeospatial. com/Software-Technology/IDL) to run the CraterStats II software available at https://www. geo. fu-berlin. de/en/geol/fachrichtungen/planet/ softwarealgorithm, and the ESRI's ArcGIS 10. 8. 1 software suite (ESRI https://www. esri. com/en-us/arcgis/about-arcgis/overview) and Matlab (https://au. mathworks. com/ products/matlab. html) to produce the maps.
We performed a systematic review with meta-analysis on data from peer-reviewed publications related to the yield advantages of legume-based rotations in 462 studies with 11,768 paired observations across 53 countries (Fig. 1 ). Only field experiments containing side-byside yield comparisons were included in the database (see "Methods" for study selection details). Our analysis allows precise quantification of the change in the subsequent crop yield after legume inclusion for various cropping systems depending on initial crop diversification, N fertilization, or crop types.
Results and discussion Legume pre-crop effect at a global scale Our meta-analysis contains 60 major legume-based cropping systems from field studies and covers 53 countries from 1959 to 2020. The results show that among the main crops following legumes, 91% involved cereals, 6% were rapeseed, 2% were roots and tubers, and <1% were fodder, fiber, and sugar crops. For crop diversity of the initial cropping systems (i. e. , the control), 45% of observations were obtained in monoculture (crop diversity = 1) and 47% in short rotations (crop diversity ≤ 4). This large and detailed dataset provides the basis for profound understanding of the legume pre-crop effects, which cannot be drawn from earlier regional meta-analyses  [11] [12] [13] [14]. We found that the legume pre-crop effect on main crop yield was predominantly positive (73.
Greater legume pre-crop effect at low yield levels and low N inputs Greater legume pre-crop effects were observed at low yield levels and low N inputs (Fig. 4 ). For all main crop species, the yield advantages after legume inclusion declined sharply with increasing initial main crop yield. For the main crop yield above the average yield observed in our database, the yield advantage was nearly constant and negligible after legume inclusion (Fig. 4a ). Moreover, 79% of the observed greater yield increment (>20%) was found at initial yields lower than the average level. Consistent with the overall declining trend of all main crop species, the pre-crop effect on major (wheat, maize, and rice) and secondary (barley) cereals decreased sharply until the yield reached the average level (Supplementary Fig.
Data collection Literature search. We comprehensively searched Web of Science (http://www. webofknowledge. com), Google Scholar (The first 5000 records, http://scholar. google. com), and the China National Knowledge Infrastructure (CNKI; https://www. cnki. net) for studies that examined the yield effect of legume-based cropping systems. The last search was done in October 2020 with the equation: ("crop* rotation*" OR "crop* sequence*" OR "sequential crop*" OR "successive crop*" OR "ley farming" OR "sequence* of plant species" OR "sequence* of crops") AND ("yield*") adapted from Hufnagel et al. (2020)  42. We also checked the reference list within the selected papers after screening, the previous meta-analyses 10-14,16,24,43-45 , and reviews 19,26,46-52 on the subject. The literature search was based on the procedure of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)  53  (Supplementary Fig.
7 ). Study selection. Publications were screened based on the following criteria: (1) the rotation experiment was conducted under field conditions and contained side-by-side comparisons of legume and non-legume pre-crop rotations with the same main crop; (2) subsequent crop yield data were reported or could be calculated; (3) the initial climatic conditions, soil properties, and main crop management practices were the same; (4) location of the experiment was stated. In addition, we considered different experiments to be distinct studies when multiple experiments (usually locations) were reported in one publication. When multiple publications originated from the same experiment (usually a long-term experiment), we coded them as the same study. If a publication reported different 'rotation cycles' of the crop rotation, we included each cycle independently.
When different publications included the same data, we recorded the data only once. In total, 462 studies reported in 476 papers published between 1959 and 2020 were included in this meta-analysis (Supplementary note). Data extraction. For each study, we extracted the means, the number of replications, and indicators of precision of the effect-sizes (standard error or confidence intervals). Grain yield and moisture recorded from each crop were converted to kilograms per hectare and adjusted to moisture contents of 15. 5% (maize), 15% (barley), 14% (wheat, rice, and millet), 12% (sorghum and rapeseed). We standardized the yield for all main crops in the non-legume cropping systems (observed values minus mean and divided by one standard deviation) for further analysis. In addition to the response variable, our database also included site characteristics like location, climate, and soil quality, and management information like crop species and cultivation practices, which we used to explain the variation in lnRR (see Supplementary Table  3 ).
In cases where data were only presented in figures, values were extracted using the GetData Graph Digitizer (http://getdatagraphdigitizer. com/). In a few instances where yield data were only reported as a percentage change relative to other treatments, we assumed absolute yield values for the reference treatment and calculated the natural log of the response ratio  54. The digital latitude and longitude of each site were recorded or derived by the location of the nearest city or the experimental station at which the study took place (Supplementary dataset 1). Topographical conditions, i. e. , altitude, climate variables including mean annual temperature (MAT), mean annual precipitation (MAP), and annual aridity index (AI, mean annual precipitation divided by potential evapotranspiration) were collected from studies or extracted from the WorldClim database 55 using site geographic location (i.
e. , latitude and longitude). Soil physicochemical properties including initial pH in water, soil organic carbon (SOC), and total N concent (STN) were collected from studies or extracted from the HWSD database  56  using latitude and longitude coordinates. Soil texture was classified into 11 textural classes and further grouped into three categories based on the USDA textural classes of soils  57. In a few cases (<1%) where only large geographical areas were stated in publications, climate and soil variables were not estimated. A major objective of our study was to test the rotation effect of legume pre-crops on non-legume main crop yield. To do this, we identified features for both (control and treatment) cropping systems, including preceding legume crop species, legume crop purpose, subsequent crop species, crop types, number of crops per year, and crop diversity.
Variable importance. To identify the potentially relevant moderators in predicting the yield benefit of legume-based cropping system, a meta-regression model was fitted with 21 modifiers (Supplementary Table  3 ) as predictors using the R package metaforest  65. The approach is adapted from the machine-learning algorithm "random forest" by incorporating the weight of each experimental observation to the bootstrap sampling. Thus, metaforest is robust to overfitting and considers non-linear relationships between moderators and the response variable, and their interactions  65, 66. We ranked the individual input factors according to their relative importance for predicting yield. Variable importance values were calculated based on the Gini impurity index that captures the variable importance for estimating the value of the target variable (here; the yield of the main crop) across all of the trees that make up the forest.
If the variable is useful, it tends to split mixed labeled nodes into pure single class nodes  67. Exploratory moderator analysis was conducted based on the following steps. First, variable pre-selection was applied in metaforest using the preselect function. A recursive algorithm was used with 10,000 iterations and 100 replications. Variables with consistent negative importance were dropped using the preselect_vars function in metaforest. Variables with positive importance were retained for nextmodel optimization. Second, tuning parameters of metaforest were optimized using the train function from the caret package  68. The optimal model was selected based on 10-fold cross-validation with minimized root-mean-square error (RMSE). The metaforest models were fitted with different numbers of moderators by removing the variables containing missing values.
This led to the selection of the following parameters for the total dataset with 18 moderators: uniform weighting metaforest, four candidate moderators available at each split, six as minimum node size. The estimated explained variance in out-of-bootstrap (R 2 oob ) cases and during cross-validation (R 2 cv ) ranged from 0. 462 to 0. 505 and 0. 459 to 0. 505, respectively, indicating that the optimized metaforest model had good predictive performance (Supplementary Table  5 ). The relative importance of included moderators in each dataset is shown in Fig. 2b  and Supplementary Fig. 1. These results indicated that the importance of subsequent crop residue management, pre-crop residue management, and irrigation were relatively low in determining the legume pre-crop effect on yield. Therefore, the total dataset (11,768 paired observations) with 18 predictors was used in the following meta-analysis.
Through the above analyses, we found that the legume pre-crop effect was predominantly explained by the N fertilizer rate and crop diversity over a broad range of soil, climate, and management factors. Legume pre-crop effect on main crop yield was thus modeled as follows: ln RR = β 0 + β 1 Á N + β 2 Á D + β 3 Á N × D + π study + ε ð4Þ where N and D are N fertilizer rate applied on main crops and crop diversity, respectively. β i , π study , and ε are the mean effects, the random estimation error associated with the study, and sampling error, respectively. We conducted the analysis using the restricted maximum likelihood estimation with the lme4 package  69. Because most studies contributed more than one response ratio, the potential nonindependency of studies was accounted for by including 'study' as a random factor.
Our analysis indicated that many of our models violated the assumption of normality based on the Shapiro-Wilk test on model residuals. We therefore bootstrapped the fitted coefficients by 1000 iterations  64. Legume pre-crops and the following main crops formed 170 cropping sequences; for ease of interpretation, only the first 60 major cropping sequences with number of observations >20 are shown (Fig. 5 ). We analyzed the potential for publication bias to influence our results using Fail-Safe N Analysis  70. The fail-safe number (Rosenberg's Nfs) for the database is 2,304,804, which is >39-fold above the threshold of 58,840 (5 × n + 10), which is considered the threshold for robust mean effect size. The funnel plot was sufficiently symmetrical (Supplementary Fig. 8 ). Thus, we did not find significant publication bias that might bias our results.
The yield effects of legume pre-crops were considered significant if the 95% confidence interval did not overlap with zero. The mean effect sizes between groups were significantly different if their 95% CIs did not overlap with each other. To ease interpretation, the lnRR was finally back-transformed to generate the percentage change of rotation effect on yield, with percentage change = [exp (lnRR) -1] × 100. All statistical analyses were performed in R4. 0. 3  71. soils-portal/data-hub/soil-maps-and-databases/harmonized-worldsoil-database-v12/en/). 1.
Acknowledgements This study was funded by the  National Natural Science Foundation of China  ( 32101850 , H. D. Z. ; 32172125, Z. H. Z. ), the  Young Elite Scientists Sponsorship Program  by  CAST  ( 2020QNRC001 , H. D. Z. ), the  Joint Funds of the National Natural Science Foundation of China  ( U21A20218 , Z. H. Z. ) and the earmarked fund for China Agriculture Research System ( CARS-07-B-5 , Z. H. Z. ). Contributions from  Dr. Ji Chen  are funded by  H2020 Marie Skłodowska-Curie Actions  (No. 839806 ),  Aarhus University Research Foundation  ( AUFF-E-2019-7-1 ),  Danish Independent Research Foundation  ( 1127-00015B ), and  Nordic Committee of Agriculture and Food Research. We thank the authors whose work is included in this meta-analysis. We also thank  Beibei Xin  and  Zhen Qin  for their assistance on high-performance computing and the  High-performance Computing Platform of China Agricultural University.
Notes -Appendopyricularia resembles genera in the Pyri cularia complex  (Klaubauf et al. 2014) , in that it has solitary, pigmented conidiophores that terminate in denticulate conidiogenous cells. It is distinct in that the conidia are hyaline, fusoid to fusoid-ellipsoid, and have a characteristic flexuous central apical appendage, with conidia arranged in an apical circle, curved upwards, bowl-like, when viewed under a dissecting microscope. Based on a megablast search of NCBIs GenBank nucleotide database, the closest hits using the ITS sequence of CPC 41278 had highest similarity to Phialemoniopsis curvata (strain EXF-8700, GenBank KP035004. 1; Identities = 389/462 (84 %), 29 gaps (6 %)), Phialemoniopsis limonesiae (strain CBS 146752, GenBank NR_172355. 1; Identities = 388/461 (84 %), 27 gaps (5 %)) and Thyridium curvatum (strain UTHSC R-3448, Gen-Bank HE599292.
1; Identities = 387/462 (84 %), 29 gaps (6 %)). The ITS sequences of CPC 41278 and 42171 differ by one substitution and two gaps caused by two separate single nucleotide repeat strings (484/487 nt (99 %), including two gaps). Closest hits using the LSU sequence of CPC 41278 are Barbatosphaeria varioseptata (strain CBS 137797, GenBank NG_058674. 1; Identities = 776 /816 (95 %), five gaps (0 %)), Paradiplococcium singulare (strain CBS 126091, GenBank NG_066271. 1; Identities = 775/815 (95 %), three gaps (0 %)) and Fluminicola striata (strain MFLUCC 18-0990, GenBank MW287770. 1; Identities = 767/ 808 (95 %), five gaps (0 %)). The LSU sequences of CPC 41278 and 42171 differ by one gap caused by an extra C in a single nucleotide repeat string (659 /660 nt (99 %), including one gap). Closest hits using the actA sequence of CPC 41278 did not reveal any significant hits, only a very distant association with Xylariales.
2011). Based on a megablast search of NCBIs GenBank nucleotide database, the closest hits using the ITS sequence had highest similarity to 'Neocosmospora striata / Fusarium bataticola' (strain CBS 105. 77, GenBank LR583781. 1; Identities = 528/535 (99 %), one gap (0 %)), Myrmecridium schulzeri (strain CBS 188. 96, GenBank EU041772. 1; Identities = 508/518 (98 %), three gaps (0 %)) and Myrmecridium sambuci (strain CBS 148444, GenBank NR_175209. 1; Identities = 523/534  (98 %) , two gaps (0 %)). Closest hits using the LSU sequence are Myrmecridium phragmiticola (strain CPC 36367, GenBank NG_074444. 1; Identities = 776/779 (99 %), no gaps), Myrme cridium schulzeri (strain CBS 188. 96, GenBank EU041829. 1; Identities = 775/779 (99 %), no gaps) and Myrmecridium spartii (strain CPC 24953, GenBank KR611902.
1; Identities = 773/779  (99 %) , no gaps). Consensus phylogram (50 % majority rule) obtained from the maximum likelihood analysis with IQ-TREE v. 2. 1. 3 (Minh et al. 2020)  of the Myrmecridium phragmitigenum ITS nucleotide alignment. Bootstrap support values (> 69 % are shown; only values > 94 % are significant) from 5 000 ultrafast  (Hoang et al. 2018 ) bootstrap replicates are shown at the nodes. Culture collection numbers and GenBank accession numbers (superscript) are indicated for all species. The tree was rooted to Rubellisphaeria abscondita (culture CBS 132078; GenBank NR_154424. 1) and the novelty described here is highlighted with a coloured block and bold font. Sequences from material with a type status are indicated in bold font. Alignment statistics: 21 strains including the outgroup; 565 characters including alignment gaps analysed: 250 distinct patterns, 135 parsimony-informative, 70 singleton sites, 360 constant sites.
2007 , Braun et al. 2015). Based on a blastn search of NCBIs GenBank nucleotide database, the closest hits using the ITS sequence had highest similarity to Proliferophorum thailandicum (strain MFLUCC 17-0293, GenBank MK028344. 1; Identities = 352/422 (83 %), 18 gaps (4 %)), Plagiosphaera immersa (strain D270, GenBank MN727889. 1; Identities = 178/209 (85 %), two gaps (0 %)) and Pseudopyricularia higginsii (strain 09/2007/1470, GenBank KM484875. 1; Identities = 183 /216 (85 %), eight gaps (3 %)). Closest hits using the LSU sequence are Proliferophorum thai landicum (strain MFLUCC 17-0293, GenBank MK028343. 1; Identities = 802 /848 (95 %), ten gaps (1 %)), Ophiostoma saponiodorum (strain CBS 128125, GenBank MH877992. 1; Identities = 741/ 845 (88 %), 19 gaps (2 %)) and Ophiostoma pallidulum (strain VPRI 43846, GenBank MW046139.
1; Identities = 741/845 (88 %), 19 gaps (2 %)). No significant hits were obtained when the rpb1 sequence was used in blastn and megablast searches. Closest hits using the tef1 (second part) sequence had distant similarity to Phaeoacremonium sphinc trophorum (strain MFLUCC 11-0629, GenBank KU940202. 1; Identities = 481/514 (94 %), no gaps), Phaeoacremonium mini mum (strain AFTOL-ID 924, GenBank DQ471083. 2; Identities = 476/514 (93 %), no gaps) and Codinaea amazonensis (strain MUCL 41171, GenBank OL653996. 1; Identities = 472 /512 (92 %), no gaps). Etymology. Name refers to the fact that it is related to the genus Proli ferophorum. Classification -Diaporthomycetidae incertae sedis, Dia porthomycetidae, Sordariomycetes. Mycelium consisting of pale brown, smooth, branched, septate hyphae. Conidiophores solitary, erect, subcylindrical, olivaceous brown to medium brown, unbranched or branched above, finely verruculose, 1-2-septate.
aemula (conidia cylindrical, 4. 5 -6. 5 × 1. 2 -2. 0 μm) and N. indica (conidia guttiliform to clavate, 3-5 -4. 7 × 1. 8 -2. 2(-3) μm and N. neoexosporioides (conidia subcylindrical, (7-)8 -10(-14) × 2(-2. 5) μm  (Gams et al. 2019 , Crous et al. 2021b) , which differ based on their conidial dimensions. Based on a megablast search of NCBIs GenBank nucleotide database, the closest hits using the ITS sequence had highest similarity to Niesslia aemula (strain CBS 556. 75, GenBank MG827004. 1; Identities = 535/564 (95 %), 15 gaps (2 %)), Niesslia indica (strain CBS 313. 61, GenBank MH858063. 1; Identities = 516/545 (95 %), 17 gaps (3 %)) and Niesslia neo exosporioides (strain CBS 146810, GenBank NR_173014. 1; Identities = 533/563 (95 %), 14 gaps (2 %)). Closest hits using the LSU sequence are Niesslia exosporioides (strain CBS 515.
72 l, GenBank MH872254. 1; Identities = 788 /798 (99 %), no gaps), Niesslia neoexosporioides (strain CBS 146810, Gen-Bank NG_076714. 1; Identities = 791/802 (99 %), one gap (0 %)) and Niesslia aemula (strain CBS 556. 75, GenBank MG826805. 1; Identities = 791/804  (98 %) , no gaps). Closest hits using the actA sequence had highest similarity to Niesslia neoexospori oides (strain CPC 38177, GenBank MW890027. 1; Identities = 519/574 (90 %), 14 gaps (2 %)) and the protein-coding part of the sequences of Coccinonectria pachysandricola (strain CBS 501. 63, GenBank KM231167. 1; Identities = 398 /418 (95 %), no gaps) and Gliocephalotrichum bulbilium (strain CBS 242. 62, GenBank KM231118. 1; Identities = 398 /419 (95 %), no gaps). Closest hits using the tef1 (first part) sequence had highest similarity to Niesslia neoexosporioides (strain CPC 38177, GenBank MW890097.
1; Identities = 284/348 (82 %), 27 gaps (7 %)) and the protein-coding part of the sequences of Alfaria ossiformis (strain CBS 324. 54, GenBank KU846009. 1; Identities = 202/234 (86 %), 9 gaps (3 %)) and Trichoderma appla natum (strain 7781, GenBank KJ634757. 1; Identities = 144/150 (96 %), no gaps). No significant hits were obtained when the tub2 sequence was used in blastn and megablast searches. However, a blast2 comparison against Niesslia beta-tubulin sequences in GenBank revealed highest similarity to Niesslia ilicifolia (strain CBS 459. 74, GenBank MG896293. 1 (unverified sequence); Identities = 244/296 (82 %), 12 gaps (4 %)), Niesslia exilis (strain CBS 389. 70A, GenBank MG896278. 1 (unverified sequence); Identities = 235 /293 (80 %), eight gaps (2 %)) and Monocillium constrictum (strain CBS 407.
On MEA surface sienna to isabelline and diffuse scarlet pigment, reverse bay; on PDA surface fulvous, reverse sienna; on OA surface umber. Typus. NetherlaNds, Drenthe Province, Dwingeloo graveyard, 52. 831376, 6. 365404, on Physcia caesia (Physciaceae), 28 Mar. 2021, J. Boers, HPC 3614 (holotype CBS H-24967, culture ex-type CPC 41464 = CBS 149048, ITS and LSU sequences GenBank ON603773. 1 and ON603793. 1, MycoBank MB 844250). Additional material examined. NetherlaNds, Drenthe Province, Dwingeloo graveyard, 52. 831376, 6. 365404, on Physcia tenella (Physciaceae), 28  Mar. 2021, J. Boers, HPC 3614, CPC 41466, 41467 , ITS and LSU sequences of CPC 41466 GenBank ON603774. 1 and ON603794. 1. Notes -Hudson et al. (2019)  suggested that although the asexual morph of Acrospermum is not known, close relatives include Gonatophragmium, Pseudovirgaria and Radulidium.
On MEA surface and reverse ochreous; on PDA surface and reverse saffron; on OA surface ochreous. Typus. fraNce, Ile de France, Paris, 7 ième arrondissement, in house, from bore dust of Xestobium rufovillosum feeding on Quercus wood (death-watch beetle), Sept. 2020, C. Decock (holotype CBS H-24951 culture ex-type CPC 41118 = CBS 148941 = MUCL 58099, ITS and LSU sequences GenBank ON603775. 1 and ON603795. 1, MycoBank MB 844252). Notes -Neoacrodontium (Amplistromataceae) is phylogenetically distinct from morphologically similar genera such as Acrodontium (Teratosphaeriaceae;  Videira et al. 2016 ) and Xenoacrodontium (Xenoacrodontiaceae;  Crous et al. 2021c). Neoacrodontium gallicum clusters with CBS 349. 55 (unknown country, keratinous substrate, F. Blank, 3 Jan. 1955), which was identified as Acrodontium hydnicola  (De Hoog 1972) , and belongs to the same genus.
Neoacrodontium hydnicola (Peck)  Crous & Decock,  Basionym. Virgaria hydnicola Peck, Ann. Rep. N. Y. State Mus. 42: 128. 1889. Synonyms. Tritirachium hydnicola (Peck) Hughes, Canad. J. Bot. 31: 604. 1953. Acrodontium hydnicola (Peck)  de Hoog, Stud. Mycol. 1: 31. 1972. Based on a megablast search of NCBIs GenBank nucleotide database, the closest hits using the ITS sequence had highest similarity to 'Acrodontium' hydnicola (strain CBS 349. 55, GenBank MH857507. 1; Identities = 515/602 (86 %), 44 gaps (7 %)),  'Acrodontium' simplex (strain CBS 127. 53, GenBank MH857128. 1; Identities = 378/462 (82 %) , 41 gaps (8 %)) and Acidothrix acidophila (strain MH566, GenBank FJ430780. 3; Identities = 341/420 (81 %), 28 gaps (6 %)). Closest hits using the LSU sequence are 'Acrodontium' hydnicola (strain CBS 349.
55, GenBank MH869047. 1; Identities = 806/833 (97 %), two gaps (0 %)), Wallrothiella congregata (strain SMH1760, GenBank FJ532375. 1; Identities = 753/839 (90 %), 16 gaps (1 %)) and Amplistroma erinaceum (strain CBS 134881, Gen-Bank MH877572. 1; Identities = 655/733 (89 %), 17 gaps (2 %)).