dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Statistical analyses were carried out in R v4. 2. 1  68. Alpha diversity analyses of SSU rRNA genes were performed using breakaway v4. 7. 6 69 and DivNet v0. 4. 0 70 modelling the effect on samples due to all the categorical factors (i. e. , DWDS of origin and abundance cluster membership), while the correlations among eukaryotic and prokaryotic or viral beta diversities were tested using a Mantel test, as implemented in vegan v2. 6-2  71. Linear mixed effects models from the lme4 v1. 1-29 package  72  were used to evaluate the differences in water quality characteristics among different clusters, using a random effect to account for the differences among DWDS. Log transformations were used to correct for residuals' heteroscedasticity. For each rRNA genes module identified with the network analysis, hurdle negative binomial models in the package countreg v0.
Eukaryotic identification The majority of the sequenced data in metagenomic assemblies from complex environmental samples are typically contained in short contigs (e. g. , < 5 kbp), especially in case of complex communities with low abundance organisms  17, 75, 76. However, eukaryotic sequence identification tool benchmarks often focus predominantly on longer contigs  [22] [23] [24] [25]  , potentially leading to an overestimation of the tools' performances in complex metagenomes. Eukaryotic sequence identification from metagenome assemblies utilized either k-mer signature differences between eukaryotes and prokaryotes, or comparison of unknown sequences with reference databases. As described in previous studies, the performance of k-mer-based strategies improves with increasing contig length (Figure  1a ).
We further tested the integration of the two approaches to combine the ability to classify all sequences of kmer-based tools with the high-accuracy of reference-based approaches. Such hybrid strategy classifies contigs primarily using reference-based predictions, resorting to k-mer-based results in case no reference-based annotation is available. The integration of reference-based strategies with Majority w/o EukRep improved overall performance compared to the use of exclusively referenceor k-mer-based strategies, regardless of the reference-based tool used (Figure  1c ). In fact, CAT provided a 0. 5% higher MCC value than Kaiju with 5 kbp-long contigs, while Kaiju resulted in a 1. 3% higher MCC value with 1 kbp contigs. This ensemble approach for identification of eukaryotic sequences from metagenomic data is documented as a workflow, EUKsemble (https://github.
In addition, some of the co-occurrences retrieved which involve parasitic nematodes are possibly due to the infection of similar hosts, as the plant parasites Longidorus spp. and Xiphinema spp. 114  (present both in module 4). The detected association can be considered as groups of eukaryotes which present similar responses to environmental and DWDS management factors or even other (micro)biota. Such interpretation is supported by the analysis, within each module, of the number of occurrences as a function of DWDS disinfection strategy, climate zone, and source water origin. Except for module 6, which did not show any significant predictors (i. e. , p-value < 0. 05) of its presence or abundance, all the modules showed variations due to the tested factors (Figure  4c ). While the higher presence/abundance detected for some modules in disinfected systems might be due to the generically higher Nonpareil coverage of samples derived from such systems, noticeably module 8 shows lower presences in disinfected systems indicating potentially the higher sensitivity of its members to disinfection or the adaptation to low nutrient conditions of non-disinfected DWDSs.
Finally, a wide set of environmental conditions were also collected to aid the data interpretation 119. In the drinking water field, a similar standardized initiative was carried out by The Netherlands to monitor macroscopic invertebrates using optimized sampling techniques and microscopic techniques 120 , but is not widely adapted. Despite the recent growing attention eukaryotic-focused metagenomics is not as well established compared to the prokaryotic or viral counterparts, with studies reporting its complexity with current approaches  18. Compared to prokaryotes and viruses, both the reference data and the option of tools available for eukaryotes is limited, further exacerbating the complexity of the reconstruction of their genomes. In addition, due to the wealth of data provided by marine expeditions, reference databases included in several tools are highly skewed towards marine taxa (e.
1 ). There is thus a requirement for sufficient harmonization and consistency to be able to scale regional budgets to the globe and to compare different regions with each other for all component fluxes and each greenhouse gas. In RECCAP-2, the results of top-down atmospheric inversions will also be compared with bottom-up accounting approaches. Since research groups working on the synthesis of greenhouse gas budgets in different regions or using different approaches use different datasets and definitions, it is important to provide a set of shared and agreed definitions that are as precise as possible for each flux to be reported. We focus here on land C and CO 2 budgets, defined from two approaches: "top-down" estimates from atmospheric inversions and "bottom-up" carbon accounting approaches based on C stock inventories and process-and data-oriented models.
Some (but not all) countries provided such datasets  (Ogle et al. , 2018). One approximation for defining managed lands in the absence of national gridded areas could be to use masks of intact forests  (Potapov et al. , 2017)  with some adjustment to match reported national totals. (Canadell et al. , 2015)  the same fossil fuel emission estimate was subtracted from the total posterior fluxes of participating inversions, even when those inversions had used different fossil fuel inventories  (Peylin et al. , 2013). This inconsistency between the inversion process and the inversion post-processing induced artifacts (see discussion in  Thompson et al. , 2016)  but is of lesser importance for the intercomparison than the use of different fossil fuel inventories within the inversion ensemble. We thus recommend here that a standard gridded a priori fossil fuel CO 2 emission estimate is used by all regions in RECCAP2, such as the one recently prepared by  Jones et al.
, 2020; Ciais et al. , 2020). For improving the completeness of regional bottom-up C budgets in RECCAP2, below we define a reasonable number of component C fluxes that can all be estimated from observations. In most cases, full observationbased estimates of component C fluxes are not feasible, but limited observations can be generally extrapolated using empirical models to the scale of RECCAP2 regions. Figure  2  displays the required set of component C fluxes between the land and the atmosphere to be estimated for each region. No unique dataset or method is imposed to estimate each individual C flux, but we give references to existing datasets that already quantified those fluxes wherever possible. Two criteria informed the selection of C fluxes that we recommend for reporting in the RECCAP2 budgets: (1) there exists at least one estimate of each flux available at regional scale that can be used as a default tier in the case where no regional new estimate can be obtained, and (2) each flux is a non-negligible component of the global land C budget, typically an annual flux larger than 0.
1 PgC yr -1 , and thus cannot be ignored. If more detailed C fluxes are available for some RECCAP2 regions, we recommend these to be regrouped into the categories shown in Fig. 2  and for this grouping to be described. The general recommendation is to provide, where possible, several estimates for each C flux based on different approaches. This could take the form of ensemble medians and ranges from different models. In the case where one estimate is thought to be more realistic than others, for instance a model with a better score when benchmarked against observations or a higher spatial resolution dataset with better ground validation, the underlying reasons for preferring that estimate need to be explained based on peer-reviewed literature or evaluation. Uncertainty can be calculated from the spread of different estimates in those cases where the state of knowledge cannot establish that one estimate is better than another.
The use of IPCC methods  (Mastrandrea et al. , 2011)  and uncertainty language (http://climate. envsci. rutgers. edu/climdyn2013/IPCC/ IPCC_WGI12-IPCCUncertaintyLanguage. pdf, last access: November 2021) is recommended when different estimates of the same component C flux are available. If different estimates report their own uncertainty, either based on data or an evaluation of the method used, e. g. , by performing sensitivity analysis through changing model parameters, input datasets, or randomly varying input data, this information should be used to evaluate consistency between estimates, given their uncertainties. It is recommended to use the word "uncertainty" when comparing different estimates and "error" for the difference between an estimate and true values. Because "truth" is unknown for component C fluxes at the scale of large regions, errors cannot be estimated in RECCAP2.
Carbon fluxes in and out each region due to trade Net trade-related C fluxes for wood and crop products exchanged by each region with other regions need to be reported in C units using statistical economic data about the trade volume and the carbon content of each product. These are available from regional datasets (or using FAOSTAT and GTAP data) or the global dataset of  Peters et al. (2012). This net trade flux should be reported separately for crop products and wood products (F crop trade and F wood trade in Fig. 2 ). If the amount is relevant, it can be reported for animal products as well, but this flux is much smaller than that in crops and wood and is therefore not shown in Fig. 2. Our best-practice recommendation is to separate the net trade C flux into gross fluxes of imports and exports.
NEE cannot be derived using the bottom-up approach from a single observation-based approach. Various bottomup datasets and methods must be combined to obtain each component flux, and those fluxes can be summed up to NEE. We recommend that when a component C flux of NEE contains meaningful amounts of C emitted as CO, CH 4 , and VOCs, the type and fraction of reduced carbon compound emitted should be reported. For instance, F grazing emits carbon partly as CH 4 ; F fires emits CO (and a smaller component of CH 4 ), VOCs, and CH 4 ; F wood products emits CO when burned and CH 4 when the products decay in landfills (see Sect. 2. 5); F rivers outgas , F lakes outgas , and F estuaries outgas emit CH 4 (see Sect. 2. 6); and F geological emissions emit CH 4 and CO 2 (see Sect. 2. 7). The CO 2 and reduced C composition of each flux should be reported separately for clarity, with both expressed in C units.
, 2016)  and for MODIS-NPP  (Running et al. , 2004)  or the BETHY-DLR  (Wißkirchen et al. , 2013 )  global products. Field estimates of BPE can also be combined with satellite products of GPP to derive NPP  (Carvalhais et al. , 2014). Discussing uncertainties of satellite NPP and GPP products is not in the scope of this report, but light use efficiency formulations used in many datasets tend to ignore the effect of CO 2 fertilization and soil moisture deficits, which has motivated attempts to use data-driven models or hybrid models combining process-based leaf-scale photosyn-thesis models with satellite data, e. g. , FAPAR, like in the P-MODEL  (Stocker et al. , 2019)  or the BESS model for GPP  (Jiang and Ryu, 2016). Those models assimilate satellite observations but include the effects of CO 2 , diffuse light, or water stress on photosynthesis.
Additional methods can be used to estimate regional NPP. For crop NPP, aggregated estimates can be obtained from yield statistics and allometric expansion factors  (Wolf et al. , 2011) , the spatial scale being the one at which yield data can be collected (e. g. , farm, county, province, country). For forest NPP, woody NPP can be obtained from forest inventories, with some of the sites having several decades of measurements to enable studies of trends. The recommendation for RECCAP2 is to document the definition of NPP in the datasets that will be used for each region and the ecosystems covered in the case of NPP estimates limited to specific ecosystems as precisely as possible. It also needs to be made explicit how NPP datasets were obtained and what their possible limitations are. We recommend that NPP (rather than GPP) should be reported for each region, given that C from NPP links directly to biomass and soil C inputs and to partial appropriation by humans and animals in managed ecosystems, due to the fact that harvested C is further displaced laterally and turned into emissions of C to the atmosphere where it is used.
Carbon emissions from soil heterotrophic respiration (SHR) Soil heterotrophic respiration (SHR) is the C emitted by decomposers in soils and released to the atmosphere. Up until recently, this flux could not be estimated directly, but the availability of point-scale measurements from 6000 sites for total soil respiration and ≈ 500 sites for heterotrophic respiration in the peer-reviewed literature used by the SRDB 4. 0 database (Bond-Lamberty, 2018) allows for regional and global upscaling of this flux for averages over a given period  (Hashimoto et al. , 2015; Konings et al. , 2019; Warner et al. , 2019)  or with annual variations  (Yao et al. , 2021)  that can be used for RECCAP2.
Carbon emissions from fires This flux, called F fires , represents the emission of all carbon species to the atmosphere from wildfires, prescribed fires, biomass burning, and biofuel burning, including CO 2 , CO, CH 4 , and black carbon, separated if possible into crop residue burning and other fires. The burning of crop residues occurs though small-scale fires, which continue to be underestimated by global satellite burned area products. Further, some residues are burned out of the field, and those emissions are not measurable with satellites. Burning emissions from crop residues can be calculated from fuel consumption and carbon emission factors. Emissions from other fires can be estimated by ground-based and aerial surveys (several countries perform such surveys) or from satellite-based datasets based on burned areas, such as GFED (van der Werf et al.
, 2010) (https://www. globalfiredata. org, last access: November 2021), or based on fire radiative power, such as GFAS  (Di Giuseppe et al. , 2018). The Global Fire Emission Database version 4. 1s (GFED4. 1s) is an update of the GFED3 product with an updated burned area and is complemented by an active fire detection algorithm that improves detection of small fires (van der  Werf et al. , 2017). In tropical regions, deforestation causes fires (including peat fires in Southeast Asia). It is important here to avoid double accounting by checking in each region if C emissions from deforestation fires were already included in land use change emissions (F LUC ), and if this is the case these must be subtracted from F fires. A possible approach here is to separate fire emissions over intact, transitional, and managed lands if spatially explicit datasets for managed lands are available (Table  1 ).
Carbon emissions from biomass grazed by animals This flux, called F grazing , represents the C emission that incurs from the consumption of herbage by grazing animals, including the decomposition of animal products used in the bio-economy, the decomposition of manure, and direct animal emissions from digestion. Only the fraction of manure from animals grazing on grass should be accounted for because C emitted from manure originating from crop products given to animals is already included inF crop products. Grass requirements by animals can be derived from grass biomass use datasets  (Herrero et al. , 2013). Grass biomass use per grazing animal head in a region can be calculated based on data of total metabolizable energy (ME) of ruminants in each region. Actual grass intake can be derived from empirical models or from vegetation models that include management of pasture  (Chang et al.
Component fluxes of net ecosystem exchange for inland waters Carbon emissions from rivers, lakes, and reservoirs The fluxes, called F rivers outgas and flakes plus reservoirs outgas in Fig. 2 , correspond to those from the outgassing of C from lakes and rivers, respectively. There are two global observation-based estimates of this flux calculated using the same GLORICH river pCO 2 database but with different data selection criteria and upscaling techniques. That of  Raymond et al. (2013)  was produced using the COSCAT regions that represent groups of watersheds and can be re-interpolated to the RECCAP2 regions. That of  Lauerwald et al. (2015)  was produced on a 0. 5 • × 0. 5 • global grid and does not include lakes. Gridded CO 2 emissions of boreal lakes have been estimated separately by  Hastie et al.
Geological carbon emissions This flux, called F geological emissions , corresponds to natural emissions of CO 2 and CH 4 from geological pools. The Earth's degassing of geological carbon consists of geogenic CO 2 emissions of 0. 16 PgC yr -1  (Mörner and Etiope, 2002) , microbial oxidation of rock carbon  (Hemingway et al. , 2018) , and CH 4 emission estimated to be 0. 027 PgC yr -1  (Etiope et al. , 2019) , which has recently been revised  (Hmiel et al. , 2020)  to a smaller value of 0. 0012 PgC yr -1. Geogenic CH 4 and C land emissions are from volcanoes, mud volcanoes, geothermal sources, seeps, and micro-seepage, and if the gridded dataset of  Etiope et al. (2019)  is used, we recommend removing the marine coastal seepage CH 4 and C emissions reported separately in this dataset. Geogenic CO 2 and C emissions are almost exclusively related to geothermal and volcanic areas (high-temperature fluid-rock interactions, crustal magma, and mantle degassing).
Weathering uptake of atmospheric CO 2 This flux, called F weathering uptake , corresponds to the weathering of carbonate and silicate rocks, which is a net sink of atmospheric CO 2 and corresponds to C then transferred by rivers to the ocean. We recommend that these fluxes should be reported for each region as they are needed to rigorously compare the output of CO 2 inversions (which cover all CO 2 fluxes) with bottom-up NEE estimates (Fig. 2 ). This can be achieved using the global dataset from  Hartmann et al. (2009)  and the gridded product of  Lacroix et al. (2020)  for instance. Weathering of cement is represented in Fig. 2   should be reported as part of fossil fuel emissions, which is not within the scope of this paper. 4 Methods to estimate bottom-up components of NEE The methods described here are as follows: -C stock changes from ground-based estimates (forest biomass and soil carbon inventories), -CO 2 fluxes measured by eddy covariance, other ground-based measurements (e.
For instance, in their global synthesis of forest C stock changes,  Pan et al. (2011)  used simple fractions of growing stocks to estimate soil carbon changes. In national inventories, more detailed models of soil C change can be used. C stock changes are assumed to be the sum of NEE and lateral C fluxes exported from or imported into the territory considered. For RECCAP2, this territory is the area of each region, where the lateral fluxes consist of C exported to the ocean via inland waters and exported or imported from trade routes, as it is impractical to have observation-based gridded datasets of lateral fluxes at sub-regional resolution. Therefore, when comparing observation-based C stock change estimates with independent NEE estimates, e. g. , from inversions or other sources, it is strongly recommended to first correct the stock change from each region by the net import or export of C in trade and by the export in rivers.
Other ground-based measurements The list provided here is not exhaustive. It includes "ecological" measurements of NPP (e. g. ,  Olson et al. , 2001) , biometric C stock changes at site level (e. g. ,  Campioli et al. , 2015; Luyssaert et al. , 2007) ; soil respiration, e. g. , the SRDB database (Bond-Lamberty and Thomson, 2010); and pCO 2 data in rivers and lakes (GLORICH). These measurements are sparse and local in nature. In a similar fashion to the flux tower measurements described above, it is possible to derive empirical relationships linking point data with local climate and other predictor variables; these relationships can then be used for spatial or temporal extrapolation using gridded fields of the same predictors. In recent years, gridded estimates have been provided for soil respiration  (Hashimoto et al.
Models driven by satellite data Satellite data are also used in upscaling forest inventory, eddy covariance, and other ground-based measurements, although giving a full list of this category of models is not the purpose of this paper. Here we refer to satellite-driven NPP models  (Bloom et al. , 2016; Smith et al. , 2016; Running et al. , 2004; Tum et al. , 2016; Wißkirchen et al. , 2013)  based on light use efficiency formulations or hybrid land carbon cycle models that explicitly represent photosynthesis (and NPP) driven by directly assimilated satellite data. Similarly, fire emission models like GFED and GFAS rely on satellite input data like burned area and fire radiative power (FRP) but estimate emissions using fields from models or other datasets (information on the fuel load, the burning completeness, and emission factors for different gaseous species).
Remotely sensed models of aboveground biomass, derived from optical sensors, i. e. , MODIS  (Baccini et al. , 2017) , lidar from ICESAT-1 GLAS  (Saatchi et al. , 2011) , synthetic aperture radar (SAR,  Santoro, 2018) , and L-band vegetation optical depth (VOD,  Liu et al. , 2015) , have been produced globally and regionally (i. e. , for mangroves using X-band radar,  Simard et al. , 2019). When they are repeated over time they allow estimates of biomass stock change, such as those presented by  Brandt et al. (2018)  over Africa and  Fan et al. (2019)  over the tropics. These datasets differ not only in their methodology and training datasets but also in their spatial (300 m to 25 km) and temporal (annual, or epoch) resolutions, and thus an ensemblebased approach is preferable for assessing uncertainty.
Process-based terrestrial carbon cycle models Dynamic global vegetation models (DGVM) simulate bottom-up NEE and a number of ecosystem carbon pools and fluxes, and their change over time on a gridded basis worldwide. The grid resolution ranges from 0. 5 • for global applications, e. g. , TRENDY  (Sitch et al. , 2015)  or MstMIP  (Wei et al. , 2014) , to fine resolutions (300 m or less) regionally. These models are not tightly driven by observations (unlike those in Sect. 3. 5), but some observations are used by modelers to calibrate parameters. TRENDY models are now benchmarked following ILAMB  (Friedlingstein et al. , 2019). Dense observation datasets are not assimilated systematically, although some carbon cycle data assimilation systems exist that make use of DGVMs  (Kaminski et al. , 2013; MacBean et al.