dataesr/scientific-paragraphs-categorization huggingface dataset:
TD03 independent of the dataset version. For dataset 3, the number of mismatches is equivalent between the two methods when the full dataset is used. However, the AGBD method produces more split and merge mismatches when applied to the dataset reduced to one sequence per haplotype. portion of OTUs obtained that correspond to a grouping of several putative species, as a function of the magnitude of the mismatch (the number of putative species that are grouped in a single OTU). The dots on the horizontal green line indicate the proportion of OTUs that correspond to a single putative species (perfect match). For ASAP and RESL the number of mismatches generated by the processing of the reduced dataset is equal to or lower than that obtained with the full dataset. However, the identity of the putative species affected by these mismatches remains the same whichever dataset considered.
On the other hand, a certain number of putative species are split after analysis of the complete dataset, whereas this is not the case with the reduced dataset. This is especially true for the RESL and ASAP models. With PTP, a higher proportion of mismatches is also observed when analysing the full dataset, but some putative species are also split after analysis of the reduced dataset whereas they are not with the full dataset (e. g. , for Pontoscolex corethrurus and HaplotaxidaGEN sp. TD02). Finally, there are also differences in the magnitude of the mismatches, i. e. , the number of OTUs resulting from the split of a given putative species. For example, the putative species Ocnerodri-lidaeGEN_sp. TD01 is split into 4 OTUs by the PTP method applied to the reduced dataset, whereas it is split into 15 OTUs with the full dataset.
In general, the analysis of dataset 1 shows ABGD and ASAP to be the two most reliable methods when it comes to analyzing less than one thousand sequences. In a second step, we compared the reliability of the results obtained by these two methods with datasets 2 and 3 which contain a larger number of sequences (Figures  9  and 10 ). With dataset 2, no split is observed using the ABGD method, and a few merge mismatches appear with the full dataset, whereas with ASAP a single split is observed for the putative species Neogaster sp. TD03 independent of the dataset version. For dataset 3, the number of mismatches is equivalent between the two methods when the full dataset is used. However, the AGBD method produces more split and merge mismatches when applied to the dataset reduced to one sequence per haplotype.
Actually, the best approach would be to systematically and comparatively use several methods  [30] , if possible on multi-locus data sets. However, with the widespread use of DNA barcoding and NGS approaches, more and more large monolocus datasets are being produced, making an objective comparison of OTU delineation methods necessary. We tested five of the most commonly used delineation methods. While most studies that have performed this type of comparison have focused on the relevance of the delineations  [13, 31] , we went further by also considering the efficiency of the software in terms of computation time, the nature and usability of the output, as well as the nature of the prior knowledge required (Paz and Crawford 2012)  [32]. The upstream preparation of phylogenetic trees is a major constraint of both the GMYC and PTP methods, especially in the case of GMYC, for which a molecular clock has to be selected to obtain dated trees.
For GMYC and ASAP, only one parameter needs to be entered before the analysis, and none is required for the RESL method. Thus, we clearly distinguish the approaches based on phylogenetic trees built upstream (PTP and GMYC) from three others (RESL, ABGD and ASAP) for which this prerequisite is not necessary. Of these, RESL and ASAP are the methods that require the least prior knowledge and do not require any a priori assumptions. Regarding computational time, RESL, ABGD and ASAP methods are clearly more efficient than methods using phylogenetic trees. This is particularly evident when the dataset to be processed is large. In our comparison, the computation time of the latter methods is consistently longer than that of the distance matrix methods, and they fail to converge and delimit OTUs when the number of sequences to be processed was large.
With GMYC, however, this tree is insufficient as labels on the terminal branches are impossible to read. An important advantage of ASAP over other the other methods is that it offers a single clustering tree on which the 10 best delimitations are mapped. This option, as well as the ASAP score that accompanies each delimitation, allows the user to efficiently compare the different proposals with other sources of information (e. g. , morphology, ecology, distribution) in order to select the most relevant through an integrative taxonomy approach (as for example in  Decaëns et al. , 2016 [15] ). Our results suggest that at least in the case of large earthworm barcode dataset, distance matrix approaches are more reliable than those based on a phylogenetic approach. In our study, the PTP and GMYC methods produce numerous splits, probably due to bias in the reconstruction of ultrametric trees.
Indeed, it is acknowledged that GMYC tends to separate biological species into several OTUs due to excessive lineage splitting  [13, 32]. The RESL method is a good example of a method that is effective for certain taxa such as insects  [8] , but weakly adapted to the specificities of our dataset. Indeed, the graph approach seems unable to correctly identify the barcode gap in the case of earthworms in which COI is deeply divergent (i. e. , barcode gap generally around 14%  [3, 33, 34] ). Therefore, our results suggest that the BINs as proposed automatically in the BOLD platform should be considered with caution for taxa with deep COI intraspecific variability. Compared to the other methods, ABGD and ASAP emerge as the two most reliable methods. ABGD gives somewhat better results for small datasets, but this difference fades for larger datasets.
Conclusions Phylogenetic approaches are less suitable for delineating OTUs from DNA barcodes in earthworms, especially for datasets with a large number of sequences. The computation times are unreasonable, they often fail to converge, and they also show a strong tendency to oversplit species, making OTU delineation difficult to use in any type of study. Therefore, distance matrix methods seem preferable. RESL is easy to use as it is implemented directly on the BOLD platform, but this method also has a clear tendency to oversplit earthworm species. ABGD and ASAP are less prone to split and merge mismatches, and both have short completion times. ASAP requires less prior knowledge for model parameterisation. In addition, it provides a graphical output with the different OTUs delimitations visualised directly on a clustering tree, which is easy to understand and represents an effective aid to the interpretation of the results.
Figure7. Mismatch rates corresponding to splits (when a putative species is split into several OTUs) obtained while processing dataset 1 with the five delimitation methods. The graphs represent, for the dataset in its full version or after reduced to one sequence per haplotype, the proportion of putative species that are split by the different methods as a function of the magnitude of the mismatch (the number of OTUs resulting from the split). The dots on the horizontal green line indicate the proportion of putative species that are recovered in the OTU partitions (perfect match).
Figure7. Mismatch rates corresponding to splits (when a putative species is split into several O obtained while processing dataset 1 with the five delimitation methods. The graphs represen the dataset in its full version or after reduced to one sequence per haplotype, the proportion tative species that are split by the different methods as a function of the magnitude of the mism (the number of OTUs resulting from the split). The dots on the horizontal green line indica proportion of putative species that are recovered in the OTU partitions (perfect match).
Acknowledgments: We are grateful to the  Parc Amazonien de Guyane  (http://www. parcamazonienguyane. fr), the  Réserve Naturelle des Nouragues  (https://www. nouragues. fr/), and the  Réserve Naturelle de la Trinité  (http://www. reserve-trinite. fr/)for collecting authorizations and for financial and logistical support. This work was supported by  'Investissement d'Avenir ' grants managed by the  Agence Nationale de la Recherche  ( CEBA :  ANR-10-LABX-25-01 ;  TULIP :  ANR-10-LABX-41 ). In the Mitaraka region, material was collected as part of ' Our Planet Reviewed ' Guyane-2015 expedition, which was organized by the  Muséum national d'Histoire naturelle  ( MNHN, Paris ) and  Pro-Natura  international in collaboration with the  Parc Amazonien de Guyane , with financial support of the  European Regional Development Fund (ERDF) , the  Conseil régional de Guyane , the  Conseil général de Guyane , the  Direction de l'Environnement, de l'Aménagement et du Logement and the Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche.
Additional funding was provided by the  Trinité Natural Reserve. At the Nouragues , the project was supported by two grants  CNRS  Nouragues' 2010 and 2011. DNA barcoding was supported as part of the  International Barcode of Life program , the  Centre National de la Recherche Scientifique  (INEE-APEGE-2013:  DJ/ST/IP/2013/D-112 ) and the  Canada First Research Excellence Fund.
However, the optimal RT protocol to induce a clinically relevant anti-tumor immunity remains to be defined. For example, the best activation of cGAS/STING pathway occurs between 2 and 8 Gy while higher radiation doses do not confer immunogenicity due to a dose-dependent upregulation of TREX1. 34  Thus, future research will also require the identification of predictive biomarkers that can help in scheduling combination modalities. Promising immunotherapy exploiting RT immunostimulatory effects. -Therapeutic monoclonal antibodies are one of the most successful cancer immunotherapies since they can be used to target specific tumor antigen, to generate memory immune responses but also as ICI. Durvalumab (MEDI4736), a humanized PD-L1 monoclonal antibody, is currently being tested in a multicenter phase II trial combining RT with bevacizumab in GBM patients (NCT02336165).
2009) -No toxicity of the safety of bevacizumab (anti-VEGF) current GBM under hypofractionated RT (5 × 6 bevacizumab -Increased median 10. 105 Gy) + survival of 50% 10. 45 -Phase I/II, single arm, to -15 patients with recurrent (Combs et al. 2006) -No published results test the safety of cetuximab GBM undergoing standard (anti-EGFR) RT + TMZ protocol On-going clinical trials 10. 110 -Phase III, single arm, -Newly diagnosed patients NCT02617589 : active, not recruiting 10. 50 to test the efficacy of with standard RT + TMZ NCT02667587 : active, not recruiting Nivolumab (anti-PD1) protocol -Phase II, single arm, -Newly diagnosed patients NCT03197506 : active, not recruiting to test the efficacy of with standard RT + TMZ NCT03899857 : active, not recruiting 10. 115 pembrolizumab (anti-PD1) protocol 10.
55 -Phase I/II, single arm, to -Patients with re- NCT02866747 : recruiting test the safety and efficacy current GBM under of Durvalumab (anti-PD-L1) hypofractionated RT (3 × 8 -Phase I/II, single arm, Gy) -Newly diagnosed patients NCT03174197 : active, not recruiting 10. 120 10. 60 to test the efficacy of Atezolizumab (anti-PD-L1) with standard RT + TMZ protocol -Phase II, single arm, to test -Newly diagnosed patients NCT02968940 : active, not recruiting the efficacy of Avelumab with standard RT + TMZ NCT03047473 : active, not recruiting (anti-PD-L1) protocol NCT04729959 : not yet recruiting.
This paper provides initial results from a multimodel ensemble analysis based on the volc-pinatubofull experiment performed within the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP) as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The volc-pinatubo-full experiment is based on an ensemble of volcanic forcing-only climate simulations with the same volcanic aerosol dataset across the participating models (the 1991-1993 Pinatubo period from the CMIP6-GloSSAC dataset). The simulations are conducted within an idealized experimental design where initial states are sampled consistently across models from the CMIP6-piControl simulation providing unperturbed preindustrial background conditions. The multi-model ensemble.
Consensus volcanic forcing datasets were thus defined for each experiment and implemented in terms of zonal and monthly-mean optical properties of stratospheric volcanic aerosol. Second, climate conditions defining the initial states of individual members of the ensemble must be selected in a consistent manner across the participating models so that the diagnosed expected climate response is not biased by potential effects of a preferential phase of ongoing internal variability at the time of the eruption (see  Zanchettin et al. , 2013; Swingedouw et al. , 2015; Lehner et al. , 2016; Khodri et al. , 2017; Coupe and Robock, 2021). This is achieved by defining desired states of climate variability modes to be sampled along the parent CMIP6-DECK piControl simulation representative of unperturbed preindustrial climate conditions.
The volc-pinatubo experiments use a volcanic forcing dataset derived from satellite observations  (Thomason et al. , 2018)  but do not account for the actual climate conditions at the time of the 1991 Mt Pinatubo eruption nor for other forcing factors concomitant with the eruption -hence their idealized character. The idealized volcpinatubo experiments complement full-forcing transient experiments, where historical climate simulations show a general improvement of CMIP6-generation Earth system models in the simulation of the high-latitude climate response to the 1991 Mt Pinatubo eruption compared to previous CMIP results  (Pauling et al. , 2021). This paper provides the first multi-model analysis of the volc-pinatubo-full ensemble. The aim is to provide a first assessment of the experiment and general guidance about emergent gaps of knowledge revealed by major discrepancies across results from different models.
The experiment The VolMIP protocol established the CMIP6 stratospheric aerosol dataset to be used for the volc-pinatubo-full experiment. This dataset covers the CMIP6 historical period  and provides zonal and monthly-mean stratospheric aerosol extinction, single scattering albedo, and asymmetry factor as a function of latitude, height, wavelength, and time  (Luo, 2018a, b). The recommended CMIP6 stratospheric aerosol forcing is tailored to the spectral resolution of each participating model, which can vary greatly. For the models included in this study, the number of solar bands ranges from 4 to 23, and the number of terrestrial bands ranges from 9 to 16. For the years around the 1991 eruption of Mt Pinatubo, the CMIP6 stratospheric aerosol data are constructed directly from the Global Space-based Stratospheric Aerosol Climatology (GloSSAC) observational reconstruction  (Thomason et al.
1, which corrected erroneous aerosol extinction values which arose through cloud screening of satellite data in the lower stratosphere. Nonetheless, version 3 remains the recommended forcing dataset for the VolMIP volc-pinatubo experiments and was used in all simulations included in this study. This choice was deemed to be preferable since inter-model consistency in forcing is one of the pillars of VolMIP and volc-pinatubo-full simulations were already performed by some modelling groups at the time version 4 was released. Therefore, in cases when CMIP6 historical simulations are run with the version 4 dataset, inconsistency in the forcing contributes to explaining possible inconsistencies between the volc-pinatubo-full and the historical simulations performed with the same model. Comparison of stratospheric aerosol optical depth (SAOD) for the CMIP6 v3 and v4 datasets confirms that both datasets show a very similar spatio-temporal evolution of the SAOD, with an initial tropical peak, followed by transport of the aerosol to the mid and high latitudes of both hemispheres (Fig.
S1  in the  Supplement). Differences between the two versions are strongest in the tropics in the first few months after the Mt Pinatubo eruption and at the highest latitudes of both hemispheres. Rieger et al. (2020)  compared CMIP6 historical simulations performed with two models using v3 and v4 of the CMIP6 forcing dataset, and they found generally small differences in the simulated post-Pinatubo surface climate response, with the notable exception of temperature response in the tropical stratosphere. By protocol, a minimum of 25 simulations are branched off from the parent piControl simulation on 1 June of selected years, sampled in a way that they include equally distributed cold, neutral and warm states of the El Niño-Southern Oscillation (ENSO) and negative, neutral, and positive states of the North Atlantic Oscillation (NAO).
, 2003). The GPCP precipitation data are provided by the NOAA/OAR/ESRL PSL, Boulder, Colorado, USA, from their website at https://psl. noaa. gov/ data/gridded/data. gpcp. html (last access: 2 February 2022). GPCP data are preprocessed to remove the climatological seasonal cycle. Both HadCRUT and GPCP time series are detrended with a second-order polynomial fit to the data to remove long-term variability, especially the centennial global warming signal, and improve comparability with the volcpinatubo-full output. Observed anomalies are defined as deviations from the 1990 average.
org/10. 5281/zenodo. 3251114 (Swart et al. , 2019). The code of "version E2. 1 of Model E" of GISS-E2. 1-G is available at https://simplex. giss. nasa. gov/snapshots/ (NASA GISS ModelE development team, 2021). The code of MPI-ESM1. 2-LR is available to the scientific community under licences, which define the conditions under which the models, component models and other software can be accessed and used; see https: //mpimet. mpg. de/en/science/modeling-with-icon/code-availability (Max-Planck-Institut für Meteorologie, 2022) for further information. The code of MIROC-ES2L is available at https://doi. org/10. 5281/zenodo. 5975701 (Abe, 2022). The IPSL-CM6A-LR model code used in this work was frozen (version 6. 1. 0) and subsequently altered only for correcting diagnostics or allowing further options and configurations.
For further information on how to apply for a licence, see http://www. metoffice. gov. uk/ research/modelling-systems/unified-model  (Met Office, 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-15-2265-2022-supplement. Author contributions. DZ, CT and MK designed the study. DZ performed the statistical analyses, drafted the initial manuscript and coordinated the writing process. CT, MK, AS and MT contributed to writing the paper. CT configured the MPI-ESM1. 2-LR simulations and provided the output data with the help of SWF. MK and NL performed the IPSL-CM6A-LR simulations and provided the output data with the help of SB. JC and LR ran the CanESM5 simulations and provided the output data. KT ran the GISS-E2. 1-G simulations and provided the output data with the help of HW and ANL.
Acknowledgements. Resources supporting this work were provided by the  NASA   High-End Computing (HEC) Program  through the  NASA Center for Climate Simulation (NCCS)  at  Goddard Space Flight Center. The simulations by MIROC-ES2L used the Earth Simulator and JAMSTEC Super Computing System, supported by  TOUGOU/SOUSEI , the  Integrated Research Program for Advancing Climate Models  (grant number  JPMXD0717935715 ) and Program for  Risk Information on Climate Change , through the  Ministry of Education, Culture, Sports, Science, and Technology of Japan. MPI-ESM1. 2-LRsimulations were performed at the  Deutsches Klimarechenzentrum (DKRZ). The IPSL-CM6A-LR experiments were performed using the high-performance computing (HPC) resources of  TGCC (Très Grand Centre de Calcul ) under allocations  2019-A0060107732 ,  2020-A0080107732  and  2021-A0100107732  (project  gencmip6 ) provided by  GENCI  ( Grand Equipement National de Calcul Intensif ).
As such, it benefited from the  French  state aid managed by the  ANR  under the " Investissements d'avenir " programme with the reference  ANR-11-IDEX-0004-17-EURE-0006. This study benefited from the  ESPRI (Ensemble de Services Pour la Recherche  l'IPSL) computing and data centre (https://mesocentre. ipsl. fr,last access:  10 March 2022 ), which is supported by  CNRS ,  Sorbonne Université ,  École Polytechnique , and CNES as well as through national and international grants. The UKESM simulations were carried out on the joint UK Met Office and  Natural Environment Research Council (NERC).
, , 2009;; Faghmous et al. , 2015; Liu et al. , 2016). Other studies are more interested in the rotation of the coherent structures to separate them from a non-rotative background by using the Okubo-Weiss parameter or by using rotational speed consideration  (Isern-Fontanet et al. , 2003; Le Vu et al. , 2018; Mkhinini et al. , 2014; Morrow et al. , 2004; Nencioli et al. , 2010). Moreover, a Lagrangian point of view has been used to detect Coherent Lagrangian Vortices. Different methodologies were developed to detect mesoscale eddies through direct considerations on advected fields or particles evolving together in time, in particular to reduce the number of thresholds commonly used in Eulerian methods  (Abernathey and Haller, 2018; Beron-Vera et al. , 2008; El Aouni, 2021; Haller, 2016). Many regional studies on the detection and tracking of mesoscale eddies have been conducted, but global analyses are rare due to the lack of globally accessible databases.
It is simpler to properly tune a detection algorithm in a restricted area, where such characteristics as the Earth's rotation or the ocean stratification are homogeneous, than to take into account their variability at a global scale  (Zhang et al. , 2013). It is also faster, less consuming of computing capacities, and easier to manipulate reasonable quantities of data, concentrated in time and space. As mesoscale eddies generated by the destabilization of strong currents are different from island-generated eddies for example, and because the water masses differ from one oceanic basin to another, a regional approach has often been chosen to better integrate the eddies' specificities. Nevertheless, some global databases of mesoscale eddies exist  (Chelton et al. , 2007 (Chelton et al. , , 2011;; Faghmous et al.
, 2015; Martínez-Moreno et al. , 2019; Tian et al. , 2020; Zhang et al. , 2013). The first global database was presented in  Chelton et al. (2011 ), covering the 1993 -2008 period (hereafter CH11). This database was regularly updated until 2016 to consider the extending period and the changes of the input altimetry maps (weekly then daily production, improvement of the standards), and was available on the Oregon State University (OSU) affiliated website (https://ceoas. oregonstate. edu/ mesoscale-eddies, last access: 7 March 2022). The operational production of the database was then transferred to CLS/CNES team in 2017, updated and distributed by AVISO as the Mesoscale Eddy Trajectory Atlas (META1. 0exp in 2017) thanks to a fruitful collaboration between OSU and CLS/CNES. In 2018, META2. 0 was produced with an improved tracking scheme.