dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The empirical ranges of CUE and BPE and age effects on FPE. Under some extreme circumstances, the carbon flux to mycorrhizae and root exudates can constitute as much as 50% of daily assimilation  14  or as much as 30% of annual NPP  15, 16. While in nonstressed conditions BVOCs consume a small fraction (∼5% or less) of annual NPP, under stressed conditions and in hot climates, BVOC emissions can consume 15-50% of annual NPP  17, 18. Thus CUE-if not equal to BPE -, should always be larger than BPE. In our dataset, in those cases where both could be estimated, CUE was larger than the estimated BPE in seven out of thirteen cases (Supplementary Information, Supplementary Table  3 ). In the remaining cases, the estimated CUE was statistically indistinguishable from BPE. This finding suggests that the fraction of these unaccounted organic carbon flows varies substantially among forests.
Statistically fitted values of BPE and CUE ranged between 0. 27 (-0. 04) and 0. 58 (+0. 04). The numbers in parentheses for CUE and BPE reflect our estimates of methodological bias (random intercepts, see Table  1 ). Ninety-two percent of BPE and CUE  values in the dataset lie within the allowable range according to Amthor  19  , reflecting maximum growth with minimum expenditure (0. 65) and minimum growth with maximum maintenance costs (0. 2). The remaining 8% of the data exceed the upper bound given in ref. 19. However, no values below 0. 2 were found, and it seems likely such values cannot be physiologically sustained by plants for long periods  8. They might be encountered in moribund stands, unlikely to be sampled (perhaps an example of survivorship bias, or the desk drawer problem'  20  ).
However, values >0. 65 apparently can (temporarily) occur in young, actively growing forests. Age-or size-related declines in both GPP, NPP and FPE (as CUE or BPE) have been reported in earlier studies  9, 11, 21, 22  but a decline in FPE is shown unequivocally here (slope ∂FPE/∂age = -0. 0004 yr -1 ), based on a larger dataset than previously analysed  [23] [24] [25]  (Fig. 4  and Table  1 ). This decline could have several contributory causes. First, the longer transport pathway for water in taller trees can result in more closed stomata (to avoid xylem cavitation) and therefore reduced GPP  26  , with no corresponding reduction in R a , at least in the short-term  27. Second, larger trees may respire more because of their greater sapwood volume and mass per unit leaf area  8, 28, 29  , leading to increased R a (for the maintenance of living sapwood tissues) and reduced NPP relative to GPP.
'Model' means that a dynamic process-based model was used, but with parameters calibrated and optimized at the site, based on either biometric or micrometeorological measurements. Data selection. The data were obtained from more than 300 peer-reviewed articles (see also ref. 5  ), adding, merging and extending published works worldwide on CUE or BPE  4, 9, 11, 23, 25, 56, 57. Data were extracted from the text, Tables or directly from Figures using the Unix software g3data (version 1. 5. 2, Jonas Frantz). In most studies, NPP, BP and GPP were estimated for the tree stand only. However, GPP estimated from CO 2 flux by micrometeorological methods applies to the entire stand including ground vegetation. We therefore included only those micrometeorological studies where the forest stand was the dominant primary producer.
The database contains 244 records (197 for BPE and 47 for CUE) from >100 forest sites (including planted, managed, recently burned, N-fertilized, irrigated and artificially CO 2 -fertilized forests; Supplementary Information, Supplementary Fig. 3  and online Materials; https://doi. org/10. 5281/zenodo. 3953478), representing 89 different tree species. Globally, 170 records out of the total data are from temperate sites, 51 from boreal, and 23 for tropical sites, corresponding to 79 deciduous broad-leaf (DBF), 14 evergreen broad-leaf (EBF), 132 evergreen needle-leaf (ENF) and 19 mixed-forests records (MX). The majority of the data (∼93%) cover the time-span from 1995 to 2015. We assume that when productivity data came from biometric measurements the reported NPP would have to be considered as BP because 'occult', nonstructural and secondary carbon compounds (e.
g. BVOCs or exudates) are not included. In some cases, multiple datasets from the same site were included, covering different years or published by different authors. We considered only those values where either NPP (or BP) and GPP referred to the same year. From studies where data were available from more than 1 year, mean values across years were calculated. When the same reference for data was found in different papers or collected in different databases, where possible, we used data from the original source. When different authors described the same values for the same site, one single reference (and value) was used (in principle the oldest one). By using only commonly available environmental drivers to analyse the spatial variability in CUE and BPE, we were able to include almost all of the data that we found in the literature.
The occurrence of fire was reported by only a few studies  [59] [60] [61]. These data were included in the database but fire, as an explanatory factor, was not considered due to the small number of samples in which it was reported (n = 6). Data uncertainty. Uncertainties of GPP, NPP and BP data were all computed following the method based on expert judgment as described in Luyssaert et al. 55. First, 'gross' uncertainty in GPP (gC m -2 yr -1 ) was calculated as 500 + 7. 1 × (70-| lat|) gC m -2 yr -1 and gross uncertainties in NPP and BP (gC m -2 yr -1 ) were calculated as 350 + 2. 9 × (70-|lat|). The absolute value of uncertainty thus decreases linearly with increasing latitude for GPP and for NPP and BP, because we assumed that the uncertainty is relative to the magnitude of the flux, which also decreases with increasing |lat|.
e. no reduction of uncertainty for methods used, hence maximum uncertainty) was used. The absolute uncertainties on CUE (δCUE) and BPE (δBPE) were considered as the weighted means  62  by error propagation of each single variable (δNPP or δBP and δGPP) as follows: δCUE ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi δNPP GPP 2 þ δGPP NPP GPP 2 2 s ð4Þ and similarly for δBPE, by substituting NPP with BP and CUE with BPE. Data and model selection. The CUE and BPE data were combined into a single variable, as sites for which both types of estimates existed did not show any significant differences between these entities (Supplementary Fig. 2 ). CUE values based on modelling were excluded (in our database we do not have BPE data from modelling).
Tests showed that the CUE value was systematically higher when GPP was estimated with micrometeorological methods, compared to values based on biometric or scaling methods. Only data with complete information on CUE, MAT, age, TAP, and latitude were used. Altogether, 142 observations were selected. In order to use the most complete information possible, a full additive model was constructed first (Eq. (  1 )). The method used for estimation of GPP (GPPmeth) was specified as a random effect on the intercept, as visual inspection suggested that CUE values were smaller where 'scaling' was used to estimate GPP compared to cases where 'micromet' was used to estimate GPP. In Eq. (  1 ) the variable 'age' represents the development status of the vegetation, i. e. either average age of the canopy forming trees or the period since the last major disturbance.
(  1 )) and compared it with all possible reduced models (Supplementary Table  2 ). The result of this analysis is the model with the smallest number of parameters that does not significantly differ from the full model. We also examined, whether there were any significant interactions of predictor variables. There were not. We used the R function lmer from the R-package lme4  63  to fit the mixed and ordinary multiple linear models to the data. We checked for potential problems of multicollinearity using the variance inflation factor (VIF)  64. All predictors had VIF < 5 (between 1. 1 and 3. 8). The model residuals were also tested for normality (using the Anderson-Darling test of non-normality, in the R-package nortest  65  ). For models that did not take a random intercept regarding 'GPPmeth' into account (16-30 in Supplementary Table  2 ) the Anderson-Darling test found significant deviation from normality of the model residuals, hence these models were excluded from the analysis.
The remaining models were compared with one another using the function ANOVA of the R-package lmerTest  66. This resulted in a 15 × 15 matrix of model comparisons in which the full model turned out to be significantly different from all other models. The same analysis was also performed with a log-transformed version of Eq. (  1 ): log FPE ð Þ¼ β 0 0 þ β 0 1 ln MAT þ 7:5 ð Þ þ β 0 2 ln age ð Þ þ β 0 3 ln TAP ð Þþβ 0 4 ln lat j j ð Þþη 0 Z GPPmeth þ ε ð5Þ where 7. 5 °C was added to MAT in order to make its minimum 1 °C. Note that the linear model from the log-transformed variables differs from the untransformed linear model. The coefficients, here noted with a prime, can be interpreted on the basis of the back-transformed model. Contrary to the untransformed linear model where effects are additive, the back-transformed model is a multiplicative effect model, with the slope parameters as exponents for each variable and the intercept (β 0 0 as power of e).
The adjusted squared correlation coefficients were similar: 0. 306 for the untransformed and 0. 321 for the log-transformed model. Despite considerable uncertainty of the CUE values, it was possible to derive significant, systematic, linear relationships between the four driver variables and CUE or ln (CUE). Both model variants showed the same direction and similar magnitudes of the effects. It can be concluded that CUE (or ln (CUE)) from a global dataset of a large variety of forests is significantly positively affected by MAT, TAP and |lat|, and significantly negatively affected by age. Even excluding from the analysis the five tropical forest data with |lat| < 20 degrees did not alter significantly the empirical relationship (Supplementary Table  5 ). Because the parameters of the untransformed, additive model much easier to interpret, we use the additive model in the main text and use the logtransformed model only as a confirmation of trends found in the additive model.
Outputs from TRENDY v. 7. We used the simulations from eight Dynamic Global Vegetation Models (DGVMs) performed in the framework of the TRENDY v. 7 project 2,67 (http://dgvm. ceh. ac. uk/node/9; data downloaded 27 November 2019). Models that did not provide NPP and GPP at plant functional type level were excluded because of the need to analyse CUE in forests without significant contributions from shrubs, grassland or crops. The selection comprises the following models: ISAM, JULES, LPJ-GUESS, CABLE-POP, ORCHIDEE, ORCHIDE-CNP, JSBACH and SDGVM (for references on models see refs. 2, 67 and Supplementary Table  6 ). All the models represent the surface fluxes of CO 2 , water and the dynamics of carbon pools in response to changes in climate, atmospheric CO 2 concentration, and land-use change across a global grid.
However, processes underlying the exchanges of water and carbon are based on different formulations in different models. In the TRENDY protocol all DGVMs were forced with common historical climate fields and atmospheric CO 2 concentrations over the period from 1700 to 2017. Climate fields were taken from the CRU-JRA55 dataset 2 , whereas the time series of atmospheric CO 2 concentrations were derived from the combination of ice core records and atmospheric observations. Land-use change was taken into account in the simulations (S3). However, similar simulations without land-use change (S2) were also tested, showing no differences. CUE was estimated as NPP/ GPP (where NPP is commonly obtained in models by subtracting R a from GPP) for the forest plant functional types simulated to be present in each grid cell.
Acknowledgements We thank  R. H. Waring ,  S. Vicca ,  M. Campioli ,  F. Pagani  and  E. Grieco  for early constructive comments and thoughtful suggestions;  S. Noce  for the map of data points. We thank efforts from all site investigators and their funding agencies. This paper contributes to the  AXA Chair Programme in Biosphere and Climate Impacts  and the  Imperial College initiative Grand Challenges in Ecosystems  and the Environment. A. C. and G. M. are partially supported by resources available from the  Ministry of University and Research  ( FOE-2019 ), under the project " Climate Change " ( CNR DTA. AD003. 474 );M. F. -M. is a postdoctoral fellow of the  Research Foundation-Flanders (FWO) ;.
This paper is the fourth in a series of the NA61/SHINE collaboration presenting experimental results on particle production in p + p interactions at CERN SPS energies. The relevant details of beam, target, experimental setup, and event selection were already described in previous publications  [23] [24] [25]. Therefore Sect. 2 contains only a short description of the NA61/SHINE spectrometer, of the data samples, and Fig. 1  Schematic layout of the NA61/SHINE detector system (horizontal cut in the beam plane, not to scale). Also outlined are the coordinate system used in the experiment and the beam detector configuration used with secondary proton beams in 2009 of the event selection. Section 3 summarizes the data analysis and systematic uncertainties. Section 4 presents and discusses the results of the present analysis together with the world data on φ production in p + p and Pb + Pb collisions and compares them with calculations of the three microscopic models Pythia, Epos, and UrQMD  [2, [26] [27] [28] [29].
The detector system of NA61/SHINE, depicted in Fig. 1 , is described in detail in Ref. [23]. The data studied in the present analysis were collected with secondary beams of positively charged hadrons at 40, 80 and 158 GeV/c. The latter were produced by 400 GeV/c protons extracted from the SPS onto a beryllium target in the slow extraction mode with a flat-top of 10 s. The secondary beam momentum and intensity was adjusted by proper setting of the H2 beam-line magnet currents and collimators. The beam was transported along the H2 beam-line towards the experiment. The precision of the bending power of the beam magnets was approximately 0. 5%. The protons in the secondary hadron beam (58% at 158 GeV/c, 28% at 75 GeV/c and 14% at 40 GeV/c) were identified by two Cherenkov counters, a CEDAR (either CEDAR-W or CEDAR-N) and a threshold counter (THC).
A GAP-TPC (GTPC) between VTPC-1 and VTPC-2 improves the acceptance for high-momentum forward-going particles. The TPCs record the tracks and energy loss (dE dx) of the charged particles produced in the collision. Their momentum vectors are calculated from the track parameters and the magnetic field. The present analysis was performed on minimum bias proton-proton collision data at three beam momenta 158, 80 and 40 GeV/c. The recorded and selected event statistics are shown in Table  1. The difference between the two numbers is caused by the event selection cuts (see below). A large sample of Monte Carlo (MC) events was generated in order to estimate the corrections for detector and analysis deficiencies. The MC samples contained 20 million p + p events at each collision energy. These were generated using the Epos 1.
99 model  [26, 27]  available within the Crmc 1. 4 package  [30]. The detector response was simulated using the Geant 3. 21 package  [31]. Event reconstruction was performed by the same NA61/SHINE software version as used for the treatment of experimental data. Two modifications were applied to the original Epos code: the natural width of the φ resonance was adjusted to its PDG value  [32] ; the branching ratio for the φ → K + K -decay channel was set to 100 % to increase the number of detectable φ decays. By virtue of the relatively small φ multiplicity, this latter change has no significant effect on the overall event characteristics and thus does not bias the obtained corrections. Epos was chosen as event generator, because other tested models performed worse in comparison with NA61/SHINE results on hadron production in hadron-hadron and hadronnucleus interactions  [24, [33] [34] [35].
The NA61/SHINE results are now compared to three microscopic models. These are Epos 1. 99  [26, 27]  and Pythia 6. 4. 28  [45]  from the Crmc 1. 6. 0 package  [30]  and UrQMD 3. 4  [28, 29]. In Epos the φ width had to be adjusted to the PDG value. In case of Pythia, the main Perugia 2011 tune 350  [46]  is used. The results of the model calculations on p T and rapidity spectra are compared to the measurements in Fig. 13. Pythia reproduces the shapes of the p T spectra quite well, while UrQMD produces slightly harder and Epos slightly softer spectra. This applies to both data sets 158 GeV/c and 80 GeV/c. The widths of the rapidity distributions are reproduced by the models within the systematic uncertainties. Figure  15a  presents ratios of total yields of φ mesons to mean total yields of pions in p + p and central Pb + Pb  [21]  collisions as a function of energy per nucleon pair.
The σ y of K + and K -were calculated from the distributions given in Ref. [44]. The excitation function of σ y for the φ meson in Pb + Pb collisions is significantly steeper than the one of the other particles. The peculiarity of this result is emphasized by the new NA61/SHINE Fig. 16  Energy dependence of a total yields and b midrapidity yields of φ mesons in p + p collisions. World data on total yields come from Refs. [9] [10] [11] [12] [13] , while on midrapidity yields come from Refs. [15, 17, 18]   18a  together with the experimental data. The difference between the coalescence expectations and the actual measurements is much smaller for p + p than for Pb + Pb data points.
Acknowledgements We would like to thank the  CERN EP, BE  and  EN Departments  for the strong support of NA61/SHINE. This work was supported by the  Hungarian Scientific Research Fund  (Grant  NKFIH 123842/123959 ), the  Polish Ministry of Science and Higher Education  (Grants  667/N-CERN/2010/0 ,  NN 202 48 4339  and  NN 202 23 1837 ), the  National Science Centre Poland  (Grants  2011/03/N/ ST2/03691 ,  2013/11/N/ST2/03879 ,  2014/13/N/ST2/02565 ,  2014/14/E/ ST2/00018 ,  2014/15/B/ST2/02537 ,  2015/18/M/ST2/00125 ,  2015/19/ N/ST2/01689 ,  2016/23/B/ST2/00692 ,  2017/25/N/ST2/02575 ,  2018/30/ A/ST2/00226 ), the  Russian Science Foundation , Grant  16-12-10176 , the  Russian Academy of Science  and the  Russian Foundation for Basic Research  (Grants  08-02-00018 ,  09-02-00664  and  12-02-91503-CERN ), the  Ministry of Science and Education of the Russian Federation , Grant No.
2012 ). In addition, fluoroquinolone resistance and reduced susceptibility to the second cornerstone drug, azithromycin, is widespread within the ST  (Unemo et al. 2012). ST-1901 has a global distribution, but its prevalence varies between regions: it is among the most commonly observed N. gonorrhoeae strains in Europe, the USA, and Japan, but seems to be less frequent in Africa, with none of the current 197 African isolates in the pubMLST database  (Jolley et al. 2018 ) belonging to the ST-1901 lineage. The high global incidence of  ST-1901  displaying reduced susceptibility to key antibiotics prompted us to perform an in-depth genomic investigation. Here we analyze a global collection of 741  ST-1901  genomes to shed light on the historic spread of the sequence type, with particular focus on the emergence and spread of ESC RRS alleles.
To assess whether the substitution rates differed between Clades A and B, we created separate dated phylogenies for the two clades, which resulted in very similar evolutionary rate estimates ] mutations per genome per year for Clades A and B respectively). Next, to investigate whether ST-1901 was truly monophyletic at the genome level, we generated a phylogenetic tree from 9535 genomes from a global in-house database. When ST-classifications were annotated on the tree, it was clear that Clade B isolates were only distantly related to Clade A and that the overlapping MLST profiles were the result of convergent evolution and recombination (Fig. S1 ). Clade A isolates were by far the more prevalent and were also associated with higher ESC minimum inhibitory concentrations (MICs). We thus chose to focus our analysis on this clade.
Alleles associated with reduced ESC susceptibility. In order to investigate the distribution and spread of alleles contributing to reduced ESC susceptibility, we identified and annotated penA, ponA and porB alleles across the dataset. We identified a diverse set of penA alleles, with a dominance of penA 34. 001 (Fig. 1 ). ESC MIC values were available for 609 of the 741 isolates. Based on the observed MIC measurements, we defined alleles 10. 001, 34. 001, 34. 009, 42. 001, 71. 001 and 72. 001  as RRS penA alleles (Fig. 2  A; note that the 34. 009 allele was identified in a single isolate and thus excluded from the figure). In addition, we considered the allele 13. 001 as an ESC RRS allele based on published findings  (Liao et al. 2011; Osnes et al. 2020) , despite the lack of MIC data for isolates carrying it in this study.
In porB, mutations in codons 120 and 121 have been shown to confer decreased ceftriaxone susceptibility  (Liao et al. 2011; Osnes et al. 2020 ). To investigate the effect of different porB alleles in ESC RRS penA backgrounds, we stratified isolates carrying penA 10. 001 and 34. 001 by porB type. We restricted the analyses to these two penA alleles as they were the only ESC RRS alleles found in sufficient numbers across multiple porB allele backgrounds in our dataset. This analysis confirmed that porB alleles with mutations in both codons 120 and 121 were associated with higher ceftriaxone MICs (Mann-Whitney-Wilcoxon p < 0. 05) in both penA backgrounds (Fig 2  B ). No significant associations were found for cefixime.
3  and 4 , event number 10) and independent acquisitions of alleles 34. 009 and 42. 001 in Germany and Spain in patients diagnosed in 2011 and 2012, respectively (Figs. 3  and 4 , event number 11 and 12). We inferred that the effective population size of Wave 2 grew steadily until it peaked around 2011, following which the effective population size declined (Fig. 5B ). To ensure that the above inferences were not overly shaped by biased sampling, we repeated the analyses on a significantly downsampled, but more geographically balanced dataset (see Materials and Methods). The inferences from this dataset largely recapitulated our findings above, with the exception of Wave 1, which was inferred to be the result of two export events from Asia (Fig. S4 ).
Similar analyses performed on a downsampled geographically balanced dataset (see Materials and Methods) largely recapitulated the above findings: The credibility intervals for key node ages (Table  S6 ) overlapped with our original inference, despite a weaker temporal signal in the downsampled dataset (Root-to-tip R 2 of 0. 12, where higher R 2 is indicative of a stronger correlation between sampling times and genetic divergence). In addition, the spatiotemporal history of Wave 2 was perfectly recapitulated, whereas the inferred first wave was split in two out-of-Asia events in the downsampled dataset with independent introductions to Europe and America (Fig. S4 ). This split seems to be the result of the loss of informative European and American genomes. When also taking into account the stronger temporal signal of the full dataset, we believe the inferences from the full dataset to be more robust.