dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular data and phylogenetic analysis The sequencing of the SSU rDNA from the myxospores of M. douradae n. sp. , resulted in a sequence of 2,031bp with a GC content of 49. 7%. BLAST search revealed that this sequence did not match any of myxozoans available in GenBank database. Sequences with the highest similarity identified by BLAST search were from Myxobolus pantanalis Carriero, Adriano, Silva, Ceccarelli and Maia, 2013 (query coverage 89%, maximum identities 85. 7%), reported in the gill filaments of Salminus brasiliensis, from the Pantanal National Park, state of Mato Grosso, Brazil  [27]. Phylogenetic relationship analysis based on the 58 most closely related myxozoan sequences placed M. douradae n. sp. sequence within a subclade containing nine myxobolid parasite species of South American characiform fish (Fig.
3 ). In this same subclade, M. douradae n. sp. appears as a closely related species of M. pantanalis (GenBank accession number KF296349). A genetic distance analysis of the SSU rDNA sequences of the myxobolids that cluster in the same clade with the new M. douradae n. sp. sequence showed the largest genetic divergence among these species, varying between 13. 5% for M. pantanalis and 20. 4% for Myxobolus oliveirai Milanin, Eiras, Arana, Maia, Alves, Silva, Carriero and Adriano, 2010 (Table  1 ).
Acknowledgments The authors thank the  São Paulo Research Foundation ,  FAPESP , for Post-Doc fellowship awarded to  P. D. Mathews  (grant no. 2018/20482-3  and  2019/13147-6 ) and  T. Milanin  (grant no. 2015/18807-3 ). A. C. Morandini was funded by  CNPq  ( 309440/2019-0 ) and  FAPESP  ( 2015/ 21007-9 ). The authors are grateful to  Juliana Naldoni  for the support in laboratory analysis. The authors thank  Dr K. Liittschwager  ( USA ) for revision of the English idiom.
Salminus franciscanus Lima and Britski, 2007. Type locality: São Francisco River (17 • 20 ' 06. 05 ′′ S, 44 • 50 ′ 16. 37 ′′ W) near of municipality of Pirapora, state of Minas Gerais, Brazil. Phylum: Cnidaria Verrill, 1865. Class: Myxosporea Bütschli, 1881. Order: Bivalvulida Shulman, 1959. Family: Myxobolidae Thélohan, 1892. Genus: Myxobolus Thélohan, 1892. Myxobolus douradae n. sp. Type host: Site of infection: Serous membrane of the spleen. Type material: Syntypes-air-dried slide stained with Giemsa deposited in the cnidarian collection of Zoology Museum of the University of São Paulo-USP, São Paulo, Brazil (MZUSP 8509). Partial 18S rDNA sequence (2,031bp) was deposited in GenBank under accession number MT644269.
According to these study results and to their known ecology, we assigned or re-assigned these species to EGs. This is further explained in the discussion. We calculated therefore the "Foram-AMBI" according to the original list  [47]  and the "Foram-AMBI-updated" according to the proposed updated list based on the present study findings. The FSI considers only two groups of indicative species: sensitive and tolerant species  [49]. This classification is based on literature review from their study area in the Eastern Mediterranean and we decided to follow this original classification to apply FSI to our dataset. FSI is defined by the following equation: FSI = (10 × %Sensitive) + (%Tolerant) As for the Foram-AMBI, some species were not assigned to any group (see Table  S1 ) however they were not excluded from the dataset since no specifications were given for this aspect  [49].
Replicate samples were pooled, and relative abundances data were log(x + 1) transformed prior to analysis. A heatmap was further used to visualize the distribution of species between stations (relative abundance > 5%, untransformed pooled data). Statistical tests were performed with the statistical language R version 4. 0. 3 [65]  and statistical packages (entropy v1. 2. 1, vegan v2. 5-7, irr v0. 84. 1, gplots v3. 1. 1 and ggplot2 3. 3. 5).
The CHE project has developed an enhanced capability to produce global, regional and local CO 2 simulations, with a focus on the representation of anthropogenic sources. The project has achieved advances towards a CO 2 global inversion capability at high resolution to connect atmospheric concentrations to surface emissions. CHE has also demonstrated the use of Earth observations (satellite and ground-based) as well as proxy data for human activity to constrain uncertainties and to enhance the timeliness of CO 2 monitoring. High-resolution global simulations (at 9 km) covering the whole of 2015 (labelled CHE nature runs) fed regional and local simulations over Europe (at 5 km and 1 km resolution) and supported the generation of synthetic satellite observations simulating the contribution of a future dedicated Copernicus CO 2 Monitoring Mission (CO2M).
By the end of the CHE project, the global-scale developments have 1) demonstrated the high-resolution CO 2 modelling capability in global Nature-runs  (Agusti-Panareda et al. , 2021) , 2) integrated updated global CO 2 sectoral emission datasets  (Choulga et al. , 2020) , 3) constructed a new high-resolution (∼10 km) input dataset for fossil CO 2 emissions since the 1960s  (Jones et al. , 2021) , and 4) advanced the use of Ensemble-based uncertainty characterisation preparing the data assimilation step  (McNorton et al. , 2020). Sizeable advances at European scale include the realisation of high-resolution CO 2 emission inventories  (Super et al. , 2020b ) that served also as exploratory studies of what can be achievable at global scale, where high quality inventories are made available. Similarly, the global modelling and data assimilation advances  (Bousserez, 2019; Barré et al.
, 2020)  had beneficial links and interactions, comparing the methodological work done over Europe and exploiting a wealth of dataset gathered within the VERIFY partner project (https://verify. lsce. ipsl. fr/). CHE also supported some rapid response studies during the 2020 COVID-19 pandemic, estimating a 17% decrease in global daily CO 2 emissions during the initial outbreak phase  (Le Quéré et al. , 2020; Liu et al. , 2020) , which has stimulated advances in the use of human activity data for rapid and continuous assessment of CO 2 emissions (e. g. , https://carbonmonitor. org). These results have been supported by EO-based estimations  (Buchwitz et al. , 2020; Chevallier et al. , 2020; Weir et al. , 2020; Zheng et al. , 2020). The CHE Horizon 2020 project ran from October 2017 to December 2020.
This variability, which should be taken as a source of uncertainty for inversions based on episodic measurements from polarorbiting satellites, was quantified at scales typical of CO 2 space-based measurements. Preliminary evaluation provided in Klonecki and Prunet, (2020, CHE D2. 8) suggests significant turbulent-induced variability on XCO 2 at the scale of satellite measurements (of the order of 20%), with possible biases on flux retrieval if not properly taken into account. Work is foreseen to use Large-Eddy-Simulations for deriving a reliable model representation uncertainty for local scale transport of power plant plumes. Turbulent features not captured by the forward modelling used in the inversion/assimilation process can be parametrised using the Large-Eddy-Simulations dataset. The study of well-known emission hotspots has demonstrated the synergy of satellite observations of CO 2 and NO 2  (Reuter et al.
, 2019)  in the case of isolated sources. Assessing interannual variability of CO 2 emissions remains a challenge with the current satellite coverage  (Buchwitz et al. , 2020; Chevallier et al. , 2020; Weir et al. , 2020; Zheng et al. , 2020). However, city-scale monitoring capability has great potential with the increased data quality and availability offered by the CO 2 satellite mission  (Meijer et al. , 2020)  as demonstrated for the city of Berlin by  Kuhlmann et al. (2019 Kuhlmann et al. ( , 2020)). While the CO 2 monitoring capacity brought by CO 2 Monitoring mission will be a key asset of the future CO2MVS (e. g. , greater accuracy of detection, larger swath, higher spatial resolution and constellation of satellites), presence of clouds and large amounts of aerosol-load still pose challenges to the observational coverage in some areas.
Earth Observations Developments CO 2 observations of fluxes and concentrations with other types of Earth observation data such as radiocarbon, NO 2 , oxygen, solarinduced fluorescence and carbonyl sulphide are reviewed in Chevallier (2020, CHE D5. 2). These are clustered in satellite CO 2 and non-CO 2 , ground-based remote sensing, in situ and flask-sampling observations. The relevant information from the Copernicus CO 2 Monitoring Mission Requirements Document and from the three reports of the Copernicus Expert group and of the CO 2 Task Force is included. Research needs for the identification of the role of each relevant Earth observation type in the Copernicus CO 2 support capacity system are identified, for data streams currently available. The synthetic satellite data instead aim at supporting studies for CO 2 Monitoring mission satellite constellation and are detailed in Strandgren, (2020, CHE D2.
5). From experience gained within CHE and CAMS, the NO 2 and Solar Induced Fluorescence (SIF) satellite-based data are identified as global-coverage Earth observation information with currently more direct usability for data assimilation purposes. In situ observing capability is paramount to Evaluation and Quality Control of the CO2MVS. Sizeable advances in the Earth observations capability covering both satellite-based remote sensing (e. g. , Copernicus Sentinel-5P, NASA OCO-3) and the ground-based network (e. g. , the TCCON-Total Carbon Column Observing Network, the FLUXNET micrometeorological sites and the ICOS-Integrated Carbon Observation System sites) are documented in  (Ciais et al. , 2014).
While a full representation of biogenic related uncertainties (e. g. , structural vegetation properties, land-use-change) is not yet developed, this study has highlighted the importance of accounting for flow dependent errors and the interplay of biogenic and anthropogenic CO 2 emissions. Also, as part of the CHE project, Jones et al. (  2021 ) have produced a gridded global dataset of CO 2 emissions and their uncertainty at 0. 1-degree resolution to be used as a prior for multi-decadal runs of "top-down" models (e. g. ,  Friedlingstein et al. , 2020) , where uncertainties respect the country and sector of origin, relevant for the target of consistent climate reanalysis  (Dee et al. , 2014).
The timeliness of the monitoring suite will especially depend on the availability of the input satellite data (e. g. , current commitment from ESA and EUMETSAT for the CO 2 Monitoring mission is 24 h after sensing), but also on more detailed user requirements. The exact schedule for a reanalysis inclusive of the CO 2 discussed here, will also have to be defined and tested based on user requirements and reprocessing capabilities by EUMETSAT. Furthermore, all service provision activities at ECMWF will have to be linked to and coordinated with the contracted service provision activities as well as with other relevant activities within CAMS (e. g. , NO 2 ). All these aspects are a critical element of the ramp-up phase and will require significant time and resources. ECMWF has gained expertise with the implementation and operation of the current C3S and CAMS services, which will benefit the introduction of the CO2MVS in an operational environment.
drive the offline model LOTOS-EUROS, which computes reactive trace gases and aerosols on top of the tagged tracers. A comparison of the different transport models and prior datasets is included in Agusti-Panareda and Brunner (2020, CHE D5. 4) to assess the different capabilities of the models and priors used to perform the CHE library of simulations. The Tier-2 global nature runs (Agusti-Panareda et al. , 2021), see Figure 2, constitute a step improvement with respect to the Tier-1 runs, in both atmospheric transport and surface emissions, and demonstrate the incremental improvement cycle that will support the CO2MVS capacity. The lessons learnt on the key CO 2 modelling aspects span from the importance of high spatial resolution and accurate atmospheric transport (Agusti-Panareda et al. , 2019) to the importance of specifying a vertical profile for emission sources not released at the surface level (.
ACKNOWLEDGMENTS The authors wish to thank  Erik Andersson  and  Bernard Pinty  ( EC-DEFIS ),  Mark Dowell  ( EC-JRC ),  Nils Wedi  and  Massimo Bonavita  (ECMWF),  Xubin Zeng  (GEWEX Co-chair),  Paolo Ruti  and  Ruediger Land  (EUMETSAT) and  Yasjka Meijer  ( ESA CO2M ) for their fruitful comments. The  CHE Advisory  board chairs  Han Dolman  ( VU Amsterdam ) and  Peter Rayner  ( U. Melbourne ) together with Miss Monika Kacic (REA) and Prof. Stephen Briggs  ( U. Cambridge/U. Reading ) are specially thanked for their stewardship assured all along the  CHE  project duration. Finally, CHE work would not have seen the light without the inspirational support of the ECMWF directorate. The CHE and CoCO2 projects have received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement N.
This work must improve the performance while preserving the code accessibility for climate scientists who use and develop it but are not necessarily experts in computing sciences. This optimization work lies within this framework and we gathered, in this study, authors with very complementary profiles: oceanographers, NEMO developers, specialized engineers in climate modeling and frontier simulations, and pure HPC engineers. This work complements the report of  Maisonnave and Masson (2019)  by presenting the new HPC optimizations that have been implemented in NEMO 4. 0, the current reference version of the code. The tendency to increase the number of cores on supercomputers might translate to an increase in cores per share memory node, the total machine node number, or both. In NEMO, parallel subdomains are not sharing memory and the MPI library is required to exchange the model variables at their boundaries.
Thus, the work of this study focuses on the reduction of the MPI communication cost, for both intermode and intra-node communications. We expect that the need for such reduction will continue in future, independent of hardware evolution. We first describe the new features that have been added to the code in order to support the optimization work (see  Sect. 2). This includes an automatic definition of the domain decomposition (Sect. 2. 1), which minimizes the subdomain size for a given maximum number of cores while staying compatible with the removal subdomains containing only land points. We next present, in Sect. 2. 2, the new benchmark test case that was specifically designed to be extremely simple to be deployed while being able to represent all physical options and configurations of NEMO.
Getting land-sea mask information We need to read the global land-sea information for two purposes: get the land fraction (step 1) and find the number of land-only subdomains in a given domain decomposition (step 3). By default, reading NetCDF configuration files in NEMO can be trivially done via a dedicated Fortran module ("iom"). This solution was used up until and including revision 3. 6 but has a major drawback: each MPI process was reading the whole global land-sea mask. This potentially implies an extremely large memory allocation followed by a massive disk access. This is clearly not the proper strategy when aiming at running very large domains on large numbers of MPI processes with limited memory. The difficulty here lies in the minimization of the memory used to get the information needed from the global land-sea mask.
The BENCH configuration On top of finding the best domain decomposition, exploring the code numerical performance requires a proper benchmark. Preferably one that is easy to install and configure while keeping the code usage close to production mode. The NEMO framework proposes various configurations based on a core ocean model: e. g. , global (ORCA family) or regional grids, with different vertical and horizontal spatial resolutions. Different components can moreover be added to the ocean dynamical core: sea ice (i. e. , SI3) and/or biogeo-chemistry (i. e. , TOP-PISCES). A comprehensive performance analysis must thus be able to scrutinize the subroutines of every module to deliver a relevant message about the computing performance of the NEMO model to the whole user community. On the other hand, as pointed out by the former RAPS consortium  (Mozdzynski, 2012) , performing such benchmarking exercise must be kept simple, since it is often done by people with basic knowledge of NEMO or physical oceanography (e.
BENCH flexibility Any NEMO numerical scheme and parameterization can be used in BENCH. To help the user in his namelist choices and to test diverse applications, a selection of namelist parameters is provided with BENCH to confer to the benchmarks the numerical properties corresponding to popular global configurations: ORCA1, ORCA025 and ORCA12. The SI3 sea-ice and TOP-PISCES modules can be turned on or off by choosing the appropriate namelist parameters and CPP keys when compiling. The initial temperature and salinity definition was designed in such a way that if SI3 is activated in the simulation, the sea ice will cover about onefifth of the domain, which corresponds approximately to the annual ratio of ORCA ocean grid points covered by sea ice. Any closed or periodical conditions can be used in BENCH and specified through a namelist parameter (nn_perio in namusr_def ).
The stability of the BENCH configuration allows us to perform innovative benchmarks to easily test the potential impact of future optimization of MPI communications. BENCH can indeed run for quite a large number of time steps even if we artificially decide to skip MPI communications. For example, it is possible to examine the code scalability without any communication by simply adding a RETURN command at the beginning of NEMO communication routine. If we add an IF test with a modulo to decide if this RETURN command is applied or not, one can have an idea of what the code performance would be if we could reduce the communications by a factor of N. Likewise, one can test the potential benefits of plenty of ideas without getting too deep into the implementation.
Dedicated tool for communication cost measurement The last piece of the puzzle that we implemented to set up an effective benchmarking environment in NEMO is to collect and summarize information related to the performance of the MPI part of the code. In NEMO, MPI communications are wrapped in a small number of high-level routines co-located in a single Fortran file (lib_mpp. F 90). These routines provide functionalities for halo exchange and global averages or min/max operations between all subdomains. With very little code changes in this file, it is possible to identify and characterize the whole MPI communication pattern. This instrumentation does not replace external solutions, based on automatic instrumentation, e. g. , with "Extrae-Paraver"  (Prims et al. , 2018)  or "Intel Trace Analyzer and Collector"  (Koldunov et al.
, 2019) , which provide a comprehensive timeline of exchanges. The amount of information collected by our solution is much smaller and the possibilities of analysis reduced, but we are able to deliver, without any external library, additional computing cost or additional postprocessing, simplified information on any kind of machine. A counter of the time spent in MPI routines is incremented at any call of an MPI send or receive operation (named "waiting time"). A second counter handles MPI collective operations (named "waiting global time"). Additionally, a third counter is incremented outside these MPI-related operations (named "computation time"). On each process, only three data are collected into three floats: the cumulative time spent sending and receiving, gathering MPI messages, and the complementary period spent in other operations.
Reducing or removing unnecessary MPI communications This section presents the code optimizations that were done following the implementation of the benchmarking environment described in the previous section. In a former study  (Maisonnave and Masson, 2019 ) relied on the measurement tool (Sect. 2. 3) to assess the performance of the BENCH configuration (Sect. 2. 2). This work focused only on global configurations with grid sizes equivalent to 1 to 1/12 • horizontal resolution that may or may not include sea-ice and bio-geo-chemistry modules. Maisonnave and Masson (2019) modified various NEMO subroutines to reduce (or group) MPI exchanges with a focus on the North Pole folding. In this section, we propose to complement the work of  Maisonnave and Masson (2019)  by improving NEMO's scalability in regional configurations instead of global configurations.
This implies the configuration the BENCH namelist in a regional setup including open boundaries, as detailed in the Appendix A. We also deactivated the sea-ice component which was deeply rewritten in NEMO version 4 and necessitates a dedicated work on its performance. As evidenced by  Tintó et al. (2019) , the MPI efficiency in NEMO is not limited by the volume of data to be transferred between processes but by the number of communications itself. Regrouping the communications that cannot be removed is therefore a good strategy to improve NEMO's performance. Our goal is thus to find the parts of the code which do the most communications and then figure out how we can reduce this number either by removing unnecessary communications or by grouping indispensable communications. Note that following the development of  Tintó et al.
Straight open boundaries along domain edges Figure  4a  shows schematic representation of the BENCH configuration with straight open boundaries on every side that will be used to explain the optimizations performed in this part of the code. The code structure of NEMO requires domains to be bordered by land points (in brown) on all directions except when cyclic conditions are applied. The four open boundaries (red stripes on each side of the domain) are thus located next to the land points, on the second and before last rows and columns of the global domain. Let us consider an MPI subdomain located on the eastern side of the global domain, represented by the red square in Fig. 4a  and detailed in Fig. 4b. Originally, the treatment of the open boundaries was performed only in the inner domain (red stripes over blue cells), and a communication phase was used to update the value on the ghost cells (red stripes over white cells).
Conclusions We presented in this paper the new HPC optimizations that have been implemented in NEMO 4. 0, the current reference version of the code. The different skills we gathered among the co-authors allowed us to improve NEMO performance while facilitating its use. The automatic and optimized domain decomposition is a key feature to perform a proper benchmarking work, but it also benefits all users by selecting the optimum use of the available resources. This new feature also points out a possible waste of resources to the users, making them aware of the critical impact of the choice of the domain decomposition on the code performance. The new BENCH test case results from a close collaboration between ocean physicist and HPC engineers. We distorted the model input, boundary and forcing conditions to ensure enough stability to do benchmark simulations in any configuration with as few input files as possible (basically the namelist files).
Code availability. The NEMO source code and all the code developments described in this paper are freely available on NEMO svn depository at https://forge. ipsl. jussieu. fr/nemo/browser/ NEMO/releases/r4. 0 (last access: 1 February 2022) or alternatively here: https://doi. org/10. 5281/zenodo. 5566313 (NEMO System Team, 2021). Data availability. The data to reproduce the research can be accessed here: https://doi. org/10. 5281/zenodo. 6047624 (Irrmann and Jouanno, 2022).