dataesr/scientific-paragraphs-categorization huggingface dataset:
Data acquisition and processing software The recording of the measured values (mass, pressure, temperatures, and outputs of the device under test) is carried out by a Gantner Instruments QStation T acquisition unit equipped with A101, A107 and A109 cards. The measurement channels are configured using the Gantner Instruments "Test. Commander" software. The recording, the visualization of measurements in real time, as well as the data processing are carried out by a software produced by LNE-CETIAT, called "EXPERT". This modular and scalable software has been programmed in Python for the interface and in C for the calculations. It makes it possible, among other things, to convert the mass into flow by means of a Kalman filter, filter the data, apply corrections to the measured physical quantities, and correct the response time of the measurement system.
The dedicated Kalman filter is also used to denoise the signal whatever the form of the fluctuation (slots, ramps, oscillations). The following figure shows an overview of the EXPERT software user interface. Fig. 9  shows a general view of the main user-interface of the EXPERT software, which is composed of three areas. The first one allows the user to select the channel to record and display and edit figures. In this first panel (framed in red in Fig. 9 ), it is also possible to apply filters, corrections factors, and all types of algebraic operations to the selected channels. The second panel (framed in green in Fig. 9 ), allows for the selection of the parameters used for the automatic detection of plateaus and the subsequent calculations, such as relative errors between two different channels.
The third panel (framed in blue in Fig. 9 ), displays the different channels selected in one or more figures. Within this panel, it is possible to zoom in and out on the curves, place cursors on the curves anddisplay local coordinates, and save the selected figures either to text or comma-separated values (CSV) files or images. The EXPERT software makes it possible to record and export the processed data, including the mean values, accuracy errors and standard deviations of the device under test. A routine makes it possible, in a few clicks, to automatically detect the flow rate plateaus and to export their mean and standard deviation values, as illustrates in the following Fig. 10. As shown in the bottom part of Fig. 10 , the automatic calculations of averages absolute and relative errors standard deviations are displayed in a dedicated window where the values can either be exported either to text of CSV files.
The pilot study was performed by means of three flow profiles with volumes of approximately 50 L, 80 L and 100 L simulating dynamic flow load changes in a flow rate range up to 1600 l/h. The following Fig. 11  presents a schematic of the transfer standard package. The selected flow profiles were determined on the basis of previous studies of consumption data from various apartments and apartment buildings in some European Union (EU) countries. These data were evaluated in relation to flow rates, their durations and speed of flow changes. The flow profiles were chosen with respect to the laboratories capabilities and so that the flow profiles are statistically representative of the actual water consumption in households. Each flow profile represents a different situation, for which the participating laboratory had to demonstrate its capacity to handle different flow profiles.
Step response As an example, the following Fig. 19  shows the flow rates measurement by the dynamic primary standard (green line) and an electromagnetic flow meter analog 4-20 mA output (red line) for a step of zero to about 8000 l/h and a duration of about 30 s. Using LNE-CETIAT's dedicated EXPERT software, the data acquired and displayed in Fig. 19  have been processed to calculate automatically the following parameters: For the example shown in Fig. 19 , the values of the aforementioned parameters are presented in the following Table  9. The response time as been evaluated as the time to reach the target value of 8000 l/h. Dynamic error: E dyn = 1 n ∑ n 1 Q DUT i -Q REF i ( 3 As shown by the results obtained and presented in Table  9 , the error introduced by the response time of the flow meter in a given period (here, 1 s sliding time period), called dynamic error, is significantly increased in the case of a step flow: a factor of ten degradation of the relative error can be shown for the experimental example described above.
Oscillations As an example, the following Fig. 20  shows the flow rates measurement by the dynamic primary standard (green line) and an electromagnetic flow meter analog 4-20 mA output (blue line) for oscillations of: • Amplitude = 0-4500 l/h, frequency = 0. 1 Hz, duration = 50 s. • Amplitude = 1500-2500 Hz, frequency = 0. 5 Hz, duration = 30 s. Using LNE-CETIAT's dedicated EXPERT software, the data acquired and displayed on Fig. 20  has been processed to calculate automatically the following parameters: dynamic error and relative stability (as defined in paragraph 4. 1), and phase shift between the reference signal and the DUT. Results presented in Table  10  show the effect of an increasing oscillation frequency on the DUT's metrological performances: its relative error doesn't evolve, but its relative stability decreases as the amplitude is dampened, and the output signal phase shifts compared to the phase of the reference flow rate.
Conclusion As part of its mission of French national standard for liquid flow, and in the scope the EURAMET EMPIR Metrowamet project, LNE-CETIAT has developed and validated a primary standard composed of a dynamic flow generator and a dynamic gravimetric measurement system. The flow generator is composed of two flow control systems, allowing a generation of steps, ramps, and oscillations, either by controlling the flow upstream or downstream of the device under test. The flow control systems are based on fast pneumatic valves along with control valves which are piloted by a PLC programmed by LNE-CETIAT. The data acquisition system is composed of a fast and synchronous sampling station, for the primary standard measurands (mass, pressure, temperature) and the device under test output(s). The EXPERT software developed by LNE-CETIAT allows piloting, visualization, and processing of all measurement channels.
1  of  Meredith et al. (2018). This could be explained by a lack of organic precursors to produce COS  (Whelan et al. , 2016). Therefore, we considered that desert soils, which correspond to a specific non-vegetated PFT in ORCHIDEE, do not emit COS. For other ORCHIDEE biomes, COS production was estimated using α and β for each PFT and the mean soil temperature over the top 9 cm. The unit of P oxic was converted from pmol g -1 min -1 to mol m -3 s -1 (in Eq. 3) using soil bulk density information from the Harmonized World Soil Database (HWSD; FAO/IIASA/IS-RIC/ISSCAS/JRC, 2012).
COS concentrations at the NOAA Earth System Research Laboratories (ESRL) sites The NOAA surface flask network provides long-term measurements of the COS mole fraction at 14 locations at weekly to monthly frequencies from the year 2000 onwards. We use an extension of the data initially published in  Montzka et al. (2007). The data were collected as paired flasks analysed using gas chromatography and mass spectrometry. The stations located in the Northern Hemisphere had sample air masses coming from the entire Northern Hemisphere domain above 30 •. Among them, the sites LEF, NWR, HFM, and WIS have mostly continental footprints  (Remaud et al. , 2022) , while the sites SPO, CGO, and PSA sample mainly oceanic air masses of the Southern Hemisphere  (Montzka et al. , 2007). The locations of these sites are depicted in Fig.
Data assimilation One of the main difficulties with the implementation of a model is to define the parameter values that lead to the most accurate representation of the processes in ORCHIDEE. Calibrating the model parameters is of interest as  Ogée et al. (2016)  indicate that some of the model parameters such as f CA and the production term parameters have to be constrained by observations. Moreover, the default values for the soil COS model parameters used in this study (Tables  A1  and A2  in Appendix A) are determined by laboratory experiments  (Ogée et al. , 2016; Whelan et al. , 2016) , which is why it is interesting to study how the values obtained by calibration against field observations differ from these default values. Data assimilation (DA) aims at producing an optimal estimate by combining observations and model outputs.
In this study, we used DA to find the model parameter values that improve the fit between simulated and observed soil COS fluxes from the empirical and the mechanistic models. We used the ORCHIDEE Data Assimilation System (ORCHI-DAS), which is based on a Bayesian framework. ORCHI-DAS has been described in detail in previous studies  (Bastrikov et al. , 2018; Kuppel et al. , 2014; MacBean et al. , 2018; Peylin et al. , 2016; Raoult et al. , 2021) , so below we only briefly present the method. Assuming that the observations and model outputs follow a Gaussian distribution, we aim at minimizing the following cost function J (x) by optimizing the model parameters  (Tarantola, 2005) : J (x) = 1 2 (M (x) -y) T • E -1 • (M (x) -y) + (x + x b ) T • B -1 • (x + x b ) , ( 21 ) where x is the vector of parameters to optimize and y is the observations.
The impact of soil model parameter optimization was studied at two sites. This study exhibited strong arguments in favour of the mechanistic model, as performing an optimization of the empirical model parameters can lead to aliasing errors and a degradation of the simulated soil water content. A larger database of COS flux measurements at the site scale and especially full year time series would greatly help for the next step, which would be to optimize the parameters of ecosystem COS fluxes. Appendix A: Parameters, variables, and constants for soil COS models   (0. 7) (0. 9) (0. 9) (0. 4) (0. 9) (0. 9) (0. 4) (0. 6) (0. 3) (0. 4) Mechanistic 1. 2 0. 6 0. 9 0. 5 0. 2 0. 3 1. 0 0. 4 1. 3 0. 8 (oxic + anoxic) (0. 7) (0. 6) (0. 7) (0. 1) (0. 2) (0. 5) (0. 1) (0. 0) (0. 1) (0. 1) Launois 1. 1 1.
Acknowledgements. The authors thank the reviewers for their insightful and useful comments which helped to improve this study. The authors are very grateful to everyone who participated in field data collection used in this study. We thank  Vladislav Bastrikov  for providing the ORCHIDAS code. We also acknowledge  Nicolas Vuichard  for providing the soil bulk density map used in OR-CHIDEE simulations. Operation of the US-HA site is supported by the  AmeriFlux Management Project  with funding by the  US Department of Energy's Office of Science  (contract no. DE-AC02-05CH11231 ), and additionally it is a part of the  Harvard Forest Long Term Ecological Research (LTER)  site supported by the  National Science Foundation  (grant no. DEB-1832210 ). The field campaign at DK-SOR was supported by the  Danish ICOS contribution (ICOS/DK)  and by the  Independent Research Fund Denmark  (grant no.
DFF-1323-00182 ). Financial support. This research has been mainly supported by the  4C  project of the  European Commission 's  Horizon 2020 framework programme  (grant no. 821003 ) and to a small extent by  VERIFY  (grant no. 776810 ). Florian Kitz ,  Felix M. Spielmann , and Georg Wohlfahrt were supported by the  Austrian Science Fund (FWF)  (contract nos. P26931 ,  P27176 ,  P31669 , and  I03859 ) and the  University of Innsbruck. Review statement. This paper was edited by  Sönke Zaehle  and reviewed by two anonymous referees.
Code availability. The CMIP6 version of the ORCHIDEE model including the soil COS sub-models is available on request to the authors. The LMDZ model is available at http://svn. lmd. jussieu. fr/ LMDZ/LMDZ6/ (Laboratoire de Météorologie Dynamique, 2021) under the CeCILL (CEA CNRS INRIA Logiciel Libre) v2 free software license. Data availability. For FI-HYY, we used the 2015 soil chamber COS measurements described in  Sun et al. (2017) , which can be found at https://doi. org/10. 15146/R39P4R or in Zenodo at https://doi. org/10. 5281/zenodo. 322936. For US-HA, we used the soil COS flux data derived from eddy covariance COS and CO 2 measurements and soil chamber CO 2 measurements conducted in 2012 and 2013 as described in Wehr et al. (2017). We used the COS flux data published in Kitz et  al. (2020; https://doi.
Determining the phylogenetic origin of mitochondria is key to understanding the ancestral mitochondrial symbiosis and its role in eukaryogenesis. However, the precise evolutionary relationship between mitochondria and their closest bacterial relatives remains hotly debated. The reasons include pervasive phylogenetic artefacts, as well as limited protein and taxon sampling. Here, we developed a new model of protein evolution that accommodates both across-site and across-branch compositional heterogeneity. We applied this site-and-branch-heterogeneous model (MAM60+GFmix) to a considerably expanded dataset that comprises 108 mitochondrial proteins of alphaproteobacterial origin, and novel metagenomeassembled genomes from microbial mats, microbialites, and sediments. The MAM60+GFmix model fits the data much better and agrees with analyses of compositionally homogenized datasets with conventional site-heterogenous models.
Results To date, most studies that aimed to phylogenetically place the mitochondrial lineage have relied exclusively on mitochondrion-encoded protein datasets that range from 12 to 38 proteins  [16] [17] [18] 32, 11, 12. These ma kers are not only few (e. g. , 24 genes and 6,649 sites in  11  ) but tend to be compositionally biased because most mitochondrial genomes are rich in A+T. The only set of nucleus-encoded proteins of mitochondrial origin published thus far comprises 29 proteins  19, 20. To expand the number of proteins for placing the mitochondrial lineage, we systematically surveyed both nuclear and mitochondrial proteomes. After a multi-step phylogenetic screening, we identified 108 marker proteins of alphaproteobacterial origin in eukaryotes. Of these, 64 are exclusively nucleus-encoded, 27 are both nucleus-and mitochondrion-encoded, and 17 are exclusively mitochondrion-encoded proteins (Fig.
1A , Fig. S1 ). Our expanded dataset comprises most marker proteins previously identified  11, 19, 20  and adds 56 new ones (Fig. S1 ). Functional annotations show that these proteins have diverse functions within mitochondria (Fig. 1B , Table  S1 ). Most are involved in energy metabolism (e. g. , respiratory chain complex subunits) and protein synthesis (e. g. , ribosomal subunits) (Fig. 1B , Table  S1 ). The fact that all these proteins have mitochondrial functions strengthens the view that the genes that encode them were transferred from (proto-)mitochondria to nuclear genomes and are therefore not secondary lateral transfers to eukaryotes. The new nucleus-encoded proteins also tend to have much less variable and biased amino acid compositions in comparison to those which are mitochondrion-encoded and some that are both nucleus-and mitochondrion-encoded (Fig.
So far, all of these alphaproteobacterial MAGs came from oceanic open waters and tend to be small and A+T-rich  11. Moreover, none of them appeared to be most closely related to mitochondria to the exclusion of other alphaproteobacteria 11. To further increase taxonomic sampling across the Alphaproteobacteria, we assembled MAGs from metagenomes sequenced from diverse microbial mats, microbialites, and lake sediments (see Table  S2  for details). In addition, we also screened MAG collections released previously  11, [33] [34] [35] [36] [37] [38] [39]  , as well as the GTDB r89 database  40  , for potentially phylogenetically novel alphaproteobacteria-together, these databases comprise more than ~ 3,300 alphaproteobacterial genomes and MAGs. The newly assembled MAGs were considerably diverse and widely distributed across the tree of the Alphaproteobacteria (Fig.
1D , Table  S3 ). L: Ribosome translational factor; M: rRNA modification/maturation; N: tRNA modification/maturation; O: Aminoacyl-tRNA synthetase; P: RNA polymerase; Q: Branched-chain amino acid/fatty acid metabolism, R: Pyrimidine biosynthesis; S: Ubiquinone biosynthesis; T: Protein import/export; U: Iron-sulfur cluster biogenesis; V: Clp protease complex subunit; W: Proteasome-like complex subunit; X: Mitochondrial division (see also Table  S1 ). (C) Phylogenetic tree of 154 novel MAGs reported here, the 45 MAGs reported by Martijn et al. (2018), and 1,188 of maximally diverse alphaproteobacterial genomes in GTDB r89 database. Taxon sample reduction was done with Treemmer  41  and phylogenetic inference with IQ-TREE (-fast mode) and the LG4X model (120 GTDB-Tk marker genes; 14,048 amino acid sites).
(D). Phylogenetic tree for the 154 alphaproteobacterial MAGs reconstructed from diverse metagenomes sequenced in this study and summary of major features for each MAG. Tree was inferred with IQ-TREE (fast mode) and the LG4X model after having removed 50% of most compositionally heterogeneous ɀ sites (120 GTDB-Tk marker genes; 7,024 amino acid sites) (see also Table  S3 ). To address recent controversies  11, 26, 12  , we first assembled the largest dataset to date that includes a new set of 64 nucleus-encoded and 44 mitochondrion-encoded proteins (108 genes in total and 33,704 amino acid sites; see above). Our dataset also comprised a wide taxon sampling with twelve mitochondria from diverse eukaryotes (from most 'supergroups'), and a broad set of 104 alphaproteobacteria that covered all major known lineages and maximized phylogenetic diversity (subsampled from a set of more than 3,300 genomes; see Methods).
Importantly, our dataset incorporated several Rickettsiales species that have short branches and are less compositionally biased (Fig. 1D , Fig. S2 , Table  S4 ), as well as novel representatives of the MarineProteo1 clade (Fig. 1D , Fig. 2A , Table  S4 ). Instead of relying on Beta-, and Gammaproteobacteria as outgroups (as in  11, 12  ), we used the much closer Magnetococcia which has been consistently found to be sister to all other alphaproteobacteria (e. g. ,  11, 12, 20  ). This was done to decrease potential artefactual attractions between the long mitochondrial branch and distant outgroups, a concern raised before  11, 26, 12. Furthermore, we also removed sites estimated to have undergone functional divergence at the origin of mitochondria (these represented only 5. 2% of all sites) using the FunDi mixture model  42.
This was done to reduce potential artefacts from model misspecification as no phylogenetic model currently available adequately captures such patterns of functional divergence in proteins. We first analyzed our dataset using the MAM60 site-heterogeneous model that was specifically inferred from our own dataset-this model has been shown to have a better fit than generic site-heterogenous models (e. g. , C10-60)  43. Analyses on the untreated dataset (i. e. , without compositionally heterogeneous sites removed) placed mitochondria as sister to all of the Alphaproteobacteria with maximum support, i. e. , both the monophyly of the Alphaproteobacteria and the Alphaproteobacteria-mitochondria clade were fully supported (Fig. 2A ). However, these analyses also recovered the grouping between the Pelagibacterales, Holosporaceae, and other long-branching species (Fig.
For all four models, all topologies other than the maximum-likelihood tree were rejected with p-values of < 0. 0001 according to Bonferroni-corrected χ 2 tests. See Table  S6  for all tree topologies and datasets tested. All studies to date have exclusively relied on either site-homogenous or purely site-heterogeneous models (e. g. , CAT in PhyloBayes or C60 in IQ-TREE)  11, 12, [14] [15] [16] [17] [18] [19] [20] 22, 23, 32. Indeed,  o tractable model that accounts for compositional heterogeneity across branches and sites simultaneously is available; current branch-heterogeneous models cannot be combined with site-heterogenous models  31  , or are too computationally intensive and suffer from convergence problems  45, 46. To overcome these shortcomings, we developed a model that captures the most important compositional heterogeneity in alphaproteobacterial genomes-namely the variation in the G A R P/F I M N K Y amino acid ratio that is driven by variation in G+C vs.
To further test the phylogenetic placement of mitochondria, we used the MAM60+GFmix model to estimate log-likelihoods on two sets of fixed trees (Fig. 2A , Fig. 2B , Fig. S4 ). The first tree set was inferred from the untreated dataset (108 genes, 33,704 sites), whereas the second tree set was inferred from a compositionally homogenized dataset through site removal (108 genes, 16,029 sites); the latter dataset minimized the differences of G A R P/F I M N K Y amino acid ratios among taxa (Table  S5 ). (Both tree sets were inferred using the MAM60 site-heterogeneous model; see above. ) We then varied the position of mitochondria along all backbone branches on each fixed tree (Fig. 2A , Fig. 2B , Fig. S4 ). Furthermore, we also grouped proteins into partitions according to distances calculated based on their G A R P/F I M N K Y compositional disparity (Fig.
S5 ). Our analyses show that likelihoods estimated under the MAM60+GFmix model improved significantly when compared to conventional site-heterogeneous models (Fig. 2D , Table  S6 , likelihood ratio test (LRT) p-value = 0); model fit was improved even more when the proteins were grouped into ten separate partitions according to G A R P/F I M N K Y compositional disparity (Fig. 2D , Table  S6 , LRT p-value = 0). Importantly, the partitioned MAM60+GFmix model clearly favours trees that display the Alphaproteobacteria-sister relationship and where the grouping of longbranching and compositionally biased taxa (e. g. , Pelagibacterales, Holosporaceae) is disrupted (i. e. , those trees recovered from compositionally homogenized datasets through ɀ site removal; Fig. 2D , Table  S6 ). This suggests that the removal of ɀ sites effectively decreases overall compositional heterogeneity and potential artefacts.
The top three trees often favored by the MAM60+GFmix model (i. e. , those with the highest likelihoods) have mitochondria in adjacent branches: Alphaproteobacteria-sister (trees A11 and B9 in Fig. 2A  and Fig. 2B ), Rickettsiales-sister (trees A5 and B4 in Fig. 2A  and Fig. 2B ), and mitochondria as sister to all alphaproteobacteria except the Rickettsiales (or Caulobacteridae-sister; trees A10 and B8 in Fig. 2A  and Fig. 2B )  29, 47. However, Bonferroni-corrected χ 2 topology tests show that the optimal trees that display the Alphaproteobacteria-sister relationship are significantly better than all trees with other positions for mitochondria (see Fig. 2D ). Even though the Alphaproteobacteria-sister relationship is also favored by the MAM60+GFmix model for the mitochondrion-encoded protein dataset, the Caulobacteridae-sister relationship cannot be rejected by the Bonferroni-corrected χ 2 tests (i.
e. , p-values > 0. 05; Table  S6 ). This further supports the notion that the phylogenetic signal for the placement of mitochondria is weaker in mitochondrion-encoded proteins (see above). The Rickettsiales-sister relationship is rejected for all datasets and models (p-value < 0. 005; Table  S6 ). Overall, most of our distinct phylogenetic approaches show support for the Alphaproteobacteria-sister hypothesis.