dataesr/scientific-paragraphs-categorization huggingface dataset:
eu), funded under the EU Horizon 2020 programme, aims to address this pressing problem. Together with seven other projects, FREIA is part of the cluster EURION: European Cluster to Improve Identification of Endocrine Disruptors (http://www. eurion-cluster. eu). The core objective of FREIA is to provide improved testing capabilities for the identification of EDCs that are harmful for female reproduction, as is expressed in the full project title "Female Reproductive toxicity of EDCs: a human evidence-based screening and Identification Approach". European law requires chemicals to go through a safety evaluation before being allowed on the market. Regulators assess the endocrine disrupting properties of a chemical mostly based on data from standard test protocols that are agreed upon by the Organisation for Economic Co-operation and Development (OECD) and described in the Guidance Document on Standardised Test Guidelines for Evaluating Chemicals for Endocrine Disruption (OECD GD150)  [5].
A solution to this challenge could be to create conceptual frameworks to describe the progression of an adverse outcome, from its molecular initiating event (MIE) to secondary, tertiary, and downstream cascading key events (KEs) at the cellular, tissue, or organism level leading eventually to a pathology or adverse outcome, termed an "Adverse Outcome Pathway" (AOP). For female reproduction, this type of information is very limited. The AOPWiki, an open-source database for AOPs, only contains one description of female reproductive toxicity (https://aopwiki. org/aops/7), which is mainly based on rodent data. It pertains to reduced aromatase levels leading to reduced oestrogen synthesis, altered cyclicity, and ultimately impaired fertility. Clearly, this is severely inadequate in covering the different susceptible windows and molecular pathways that most likely will underpin EDC-related female reproductive toxicity in humans.
This will allow the study of effects of prenatal exposure on the programming of maturation of GnRH secretion. In FREIA, we use carefully designed, fit-for-purpose in silico and in vitro models with relevant cell types to experimentally assess and quantify putative molecular and cellular events underlying EDC-induced female reproductive toxicity. These models will be optimised, and standardised protocols will be provided at the end of the FREIA project. These include, but are not limited to: (1) Quantitative structure-activity relationships (QSARs) allow us to predict molecular targets based on physico-chemical properties of a chemical. We will improve existing QSARs for aromatase and PPAR-gamma, two of the key events in a proposed adverse outcome pathway for female reproductive toxicity. These QSAR models and predictions will be made publicly available via the freely available DTU Danish (Q)SAR database (http://qsar.
Whole-genome sequencing and library construction were performed by GenoScreen (Lille, France). Libraries from purified genomic DNA were prepared for Illumina sequencing with a Nextera XT sample prep kit (Illumina, San Diego, USA) according to the supplier's recommendations. Sequencing was performed using an Illumina HiSeq platform in paired ends of 250 bp. At least 2. 8 million reads were obtained for each strain, for a mean coverage of more than 339ϫ for each based on a 2. 1-Mb genome size (Table  1 ). A quality check of sequencing data was performed using FastQC v0. 11. 5 (3), and trimming of reads was done using Sickle v1. 33 (4) before assembly using SPAdes v3. 11. 1  (5). The contigs were annotated using the NCBI Prokaryotic Genome Annotation Pipeline v4. 8  (6). All software tools were run using default parameters.
All of the strains were assembled into fewer than 80 contigs (range, 50 to 69) with a total assembly size of 2. 1 Mb. About 2,000 coding genes were annotated (range, 2,039 to 2,137). The metrics for each sample are shown in Table  1. Whole-genome sequencing (WGS) analysis confirmed the presence and structure of CRISPR-Cas genes previously described for these isolates  (2). Using PHASTER  (7) , available for free at http://phaster. ca/, remnant phages were discovered on the 4 genomes, and an intact prophage sequence (79% identity with Streptococcus pro-phage 315. 2 [GenBank accession no. NC_004585. 1]) was found on W10F3 isolates (contig 4). In GBS, hemolysin production is encoded by the cyl operon, containing 12 genes, including the cylE gene  (8). Transcriptional regulation of the cyl operon is controlled mainly by the two-component system CovS/CovR (csrS and csrR genes)  (9).
1 ) allowed the exclusion of a phylogenetic affiliation between isolates W10E2 and W10F3, despite them having the same serotype and a common terminal direct repeat and ancestral spacer. The phylogenetic tree with numbering above the branches indicating the similarity coefficient between the strains (Fig. 1 ) was constructed with concatenation of SNPs using the software BioNumerics v7. 6. 3 (Applied Maths) with the settings "SNP-based categorical" and "clustering method by UPGMA. " This result confirms the difference in sequence types found (sequence type 4 [ST4] and ST28). Isolates from type W8A2 and type W8A6 were genetically closely related. These results show the in vivo coexistence of carriage of 2 types of closely related strains with a probable common ancestor. Data availability. The draft genome sequences of strains W8A2, W8A6, W10E2, and W10F3 have been deposited in DDBJ/ENA/GenBank under the accession no.
[100] , and adopted in Ref. [24]. We now focus on the comparisons with the 118 SXS NR waveforms produced in this paper. In Fig. 11  we show the unfaithfulness for IMRPhenomPv3(HM) and SEOBNRv4P(HM) in the left (right) panels. We compare waveforms without higher modes, to NR data that has only the = 2 modes, and the other models to NR data with ≤ 4 modes. The performance of both waveform models on this new NR data set is largely comparable to what was found for the public catalog. Both families perform well on average, with most cases having unfaithfulness below 2% for models without higher modes and 3% for models with higher modes. However, for some configurations IMRPhenomPv3(HM) reaches unfaithfulness values above 3% for total masses below 125M. Once again, the overall distribution is shifted to lower unfaithfulness values for SEOBNRv4P(HM).
An operating temperature of -10°C and an electric field of 300 V/mm are appropriate to reach these performances. Figure  1  shows a picture of Caliste-HD. We use these characteristics to perform Compton imaging with this detector integrated in a very compact system, namely WIX-HD, with a total weight of 1 kg. WIX-HD includes one Caliste-HD module installed into a small vacuum tight enclosure connected to a data acquisition electronics system. The cooling is realized by miniature thermos-electric Peltier coolers. This system is slightly different to the usual Compton imaging gamma-ray cameras. It is indeed composed of one planar layer of detection without interaction depth measurement, which causes disadvantages in terms of angular resolution because of the uncertainty on the interaction depth.
B. Results The total number of detected events with the acquisition at (10°,0°) is 5 423 829. Applying the energy selection according to the method described in section III, 8119 Compton events are extracted, corresponding to 0. 15 % of the total number of recorded events. With the two sources mix augmented data set, the total number of events is 8 614 247 and 13 076 Compton events are extracted, also corresponding to 0. 15 % of the events. Computation time is evaluated with the following hardware setup: Intel® Xeon® Silver 4114, CPU @ 2,20 GHz. The algorithms are implemented in Python using NumPy library.
VI. CONCLUSION AND OUTLOOKS Compton imaging can be performed with a single plane miniature pixelated detector with fine pitch and high energy resolution. It requires a particular attention on the selection of the Compton events, given the configuration and the characteristics of the Caliste-HD detector, followed by a reconstruction algorithm. We tested four algorithms on real data and demonstrated that SOE-RR and a new Bayesian algorithm, are able to separate two sources of 137 Cs separated by only10° which a classical Direct Back Projection cannot do. In forthcoming studies, we envision to further develop the advanced SOE-RR algorithms, noticeably including the energy resolution and iterative event selection. As computation time might be a severe limitation for use in real time applications, we will cascade several algorithms using one as a prior for the next.
ERT data acquisition In 2016, surface and depth ERT electrodes were installed regularly along 2-D planes, each of them crossing the middle of four plots (see Fig. 1a ): one in WD, the other one in control conditions. For ERT data acquisition, we used 123 electrodes, including 48 placed on the surface (with a separation of 0. 15 m) and 75 electrodes distributed along 13 boreholes (Fig. 3 ). Boreholes were made of PVC sticks, with embedded stainless-steel rings used as electrodes, with a diameter of 46 mm for each ring (same as  Beff et al. 2013) , the separation between electrodes increases with depth (Fig. 1a ). Between each pair of boreholes, we placed four surface electrodes. The ERT data was collected using an ARES II 10channel automatic resistivity system (from GF instru-ments®). ERT monitoring data set consisted of threetime stamps (denoted by t1 to t3), with data collected along the experimental block every week along three consecutive weeks.
ERT inversion theory We used the pyGIMLi software  (Rücker et al. 2017)  for the inversion of the ERT data sets. In the inversion algorithm, the Gauss-Newton scheme is used to iteratively minimize the objective function (ϕ): ϕ ¼ ϕ d þ λϕ m ¼ ‖ d -f m ð Þ log 1 þ E model ð Þ ‖ 2 2 þ λ‖W m -m 0 ð Þ‖ 2 2 ð1Þ where ϕ d corresponds to the data misfit between the measured data vector (d ) and the data estimated by the model ( f m ð Þ) down-weighted by the error model (E model ). ϕ m represents the model misfit and is multiplied by the regularization parameter λ determining the weighting of the model versus data misfits. d stands for the log of measured apparent resistivities from real/ numerical experiments, m 0 is a homogeneous starting model vector, W is a first-order smoothness regularization matrix used to stabilize the inverse problem that is inherently ill-posed.
ERT simulations -generation of synthetic resistivity distributions Modeled water content maps were transformed to electrical resistivity fields using Eq. 4. The pyGIMLi software was used for ERT forward simulations on the synthetic resistivity model, which solves Poisson's equation in 2. 5-D to compute the modeled ERT data with the same injection scheme than in reality. The forward modeled ERT measurements were contaminated with Gaussian noise with an amplitude defined by the error model quantified for field data. Inversion of the modeled ERT data was performed using the same methodology as in the field. Different meshes were used for forward and inverse calculation to avoid inverse crime.
Comparing Figs. 8b,  e,  and f , we see that the presence of roots increases electrical resistivity in some regions: for the yellow region (log(ρ) = 1. 65) in Fig. 8b  to light green (log(ρ) = 1. 75) in Fig. 8e  for Scenario 2 (ρ root = 10 Ωm) and to dark green (log(ρ) = 1. 84) for Scenario 3 (ρ root = 100 Ωm). Figures  8(b, d, f, h)  show the time difference or the change of log resistivity between t1 and t3 (δρ) of the forward and inverted datasets for control and the three scenarios of WD. Areas with a positive δρ correspond to zones from which soil water is extracted. Forward simulations for the control treatment show no increase in electrical resistivity (Fig. 8b ), probably due to the rainfall events. Even if, in general, the inversion recovered well the main patterns, no information on root water uptake depths can be inferred from the control treatment.
Acknowledgments  S. Rao  was funded by the  Fonds de la Recherche Scientifique -FRS-FNRS  under Grant  T. 1088. 15. The field trial was supported by the  Région Wallonne  under the project ForDrought  D31-1341. We acknowledge  Prof. Richard Lambert ,  Sébastien François ,  Maxime Doffagne , and  Benjamin Van der Verren  for providing the ERT data. We thank profs  F; Nguyen ,  S. Garré ,  A Kemna , and  N. Schwartz  for their useful suggestions.
The ssp370-lowNTCF experiment explicitly excludes a reduction in methane concentrations. However, the ssp370-lowNTCF emissions dataset as described in  Gidden et al. (2019)  and calculated in  Meinshausen et al. (2019)  does include reduced methane emissions and hence atmospheric concentrations. We include a 'ssp370-lowNTCF-gidden' scenario to complement ssp370-lowNTCF and examine the consequences of a strong reduction in methane emissions. The standard set of inputs from CMIP5 and CMIP6 was translated into the RCMIP experiment protocol, using the WGIII format  (Gidden and Huppmann, 2019) , so it could be used by the modelling groups. Gidden et al. (2019)  and are taken from https://tntcat. iiasa. ac. at/SspDb/dsd?Action= htmlpage&page=60, hosted by IIASA. Where WMGHG gas emissions are missing, we use inverse emissions based on the CMIP6 concentrations from MAGICC7.
CMIP6 emissions projections follow For CMIP6 historical emissions, we have used data sources which match the harmonisation used for the CMIP6 emissions projections. This ensures consistency with CMIP6, albeit at the expense of using the latest data sources in some cases. CMIP6 historical anthropogenic emissions for CO 2 , CH 4 , BC, CO, NH 3 , NOx, OC, SO 2 and volatile organic compounds (VOCs) come from CEDS  (Hoesly et al. , 2018). Biomass burning emissions data for CH 4 , BC, CO, NH 3 , NOx, OC, SO 2 and volatile organic compounds (VOCs) come from UVA  (van Marle et al. , 2017). The biomass burning emissions are a blend of both anthropogenic and natural emissions, which could lead to some inconsistency between RCMs as they make different assumptions about the particular anthropogenic/natural emissions split.
CO 2 global land-use emissions are taken from the Global Carbon Budget 2016  (Quéré et al. , 2016). Other CMIP6 historical emissions come from PRIMAP-hist  (Gütschow et al. , 2016)  Version 1. 0 https://doi. org/10. 5880/PIK. 2016. 003 (N 2 O and CO 2 land-use regional information). Where required, historical emissions were extended back to 1750 by assuming a constant relative rate of decline based on the period 1850-1860. While this means that our historical emissions are highly uncertain, all we require is consistent emissions inputs so we leave improved quantifications of emissions in this period for other research. CMIP6 concentrations follow  Meinshausen et al. (2019). CMIP6 radiative forcings follow the data provided at https://doi. org/10. 5281/zenodo. 3515339). CMIP5 emissions, concentrations and radiative forcings follow  Meinshausen et al.
Diagnostics Given their focus on global-mean, annual mean variables we request a range of output variables from each RCM (detailed in Supplementary Table  S4 ). The output variables focus on the response of the climate system to radiative forcing (e. g. surface air temperature change, surface ocean temperature change, effective radiative forcing, effective climate sensitivity) as well as the carbon cycle (carbon pool sizes, fluxes between the pools, fluxes due to Earth System feedbacks). The temporal resolution of the models means that we request the data on an annual timestep but may increase the temporal resolution in Phase 2. The output dataset represents a huge data resource. In this paper we focus on a limited set of variables, particularly related to the climate response to radiative forcing.
MAGICC MAGICC, the 'Model for the Assessment of Greenhouse Gas Induced Climate Change' has its origins in early upwellingdiffusion ocean parameterisations by  Wigley and Schlesinger (1985) , with components for sea level rise  (Wigley and Raper, 1987)  and a carbon cycle being added subsequently  (Wigley, 1991). MAGICC has been used frequently in all past IPCC reports, often in conjunction with the ISAM  (Jain et al. , 1994)  and Bern-CC  (Siegenthaler and Joos, 1992)  carbon cycle models. Initially, MAGICC was written in Fortran 77, then transferred to Fortran 90 in the 2000s  (Meinshausen et al. , 2011a, c) , amended by a permafrost module  (von Deimling et al. , 2012)  and a new sea level rise module  (Nauels et al. , 2017). An updated version (MAGICC7. 0. 0) was used to generate the GHG concentration projections that are the input assumptions for the CMIP6 ESM experiments  (Meinshausen et al.
, 2019). Around 2000 simulations pass the observational consistency test and are accepted into the final observation-constrained model ensemble. This final model ensemble thus contains the small number of parameter value combinations that produce observation-consistent simulations, in which the values of the 21 input parameters are no longer independent of one another. The observation-constrained simulations are integrated into the future for the SSP scenarios (using non-CO 2 radiative forcing following the Effective Radiative Forcing from Shared Socioeconomic Pathways available at https://doi. org/10. 5281/zenodo. 3515339). To perform the idealised scenarios, that do not have a historically forced period with which to extract the final ensemble, the final observation-consistent model ensemble generated from the ssp585 scenario is used.
Conclusions Phase 1 of RCMIP has identified some of the strengths, weaknesses and limitations of various RCMs which exist in the literature today. This paper has focussed on surface air temperature (GSAT) changes but many more output variables are available in the Phase 1 database. To facilitate further research, the entire database is available under a Creative Commons Attribution-ShareAlike 4. 0 International (CC BY-SA 4. 0) license. We have found that RCMs are capable of reproducing broad-scale characteristics of observed historical GSAT changes as well as the response of ESMs under various experiments. Further work could focus on why RCMs exhibit relatively high recent warming rates compared to observations and using the ever growing body of CMIP6 results to improve RCM emulation capabilities.
Much of the work of defining community standards, data handling practices and communication methods has been established and now only needs refining. Further phases of RCMIP could focus on many different themes, for example, considering a wider range of variables, probabilistic climate projections (something which cannot be done with more complex models due to computational expense), specific components of the earth system (e. g. Figure  1. Historical global-mean annual mean surface air temperature (GSAT) simulations. Thick black line is observed GSAT  (Richardson et al. , 2016; Rogelj et al. , 2019). Medium thickness lines are illustrative configurations for RCMIP models. Thin solid lines are CMIP models (CMIP6 in dark blue, CMIP5 in grey). In order to provide timeseries up until 2019, we have used data from the combination of historical and ssp585 simulations for RCMIP and CMIP6 models and rcp85 data for CMIP5 models.
https://doi. org/10. 5194/gmd-2019-375 Preprint. Discussion started: 21 January 2020 c Author(s) 2020. CC BY 4. 0 License. //monash. edu/research/simple-climate-model/mscm/overview_i18n. html?locale=EN. The last stable version code is available on GitHub at https://github. com/christianstassen/greb-official/releases. For RCMIP scenarios, the model is run with all processes on. The climatology used for flux correction is taken from ERA-Interim Reanalysis IFS model (ECMWF_IFS) within a time window from 1979 to 2015. Other parameters and input variables (not initial conditions), are taken from either ERA-Interim or NCEP Reanalysis data.
Acknowledgements. We acknowledge the  World Climate Research Program (WCRP) Coupled Model Intercomparison Project  ( CMIP ) and 540 thank the climate modeling groups for producing and making available their model output. RCMIP could not go ahead without the outputs of CMIP6 nor without the huge effort which is put in by all the researchers involved in CMIP6 (some of whom are also involved in RCMIP). We also thank the  RCMIP Steering Committee , comprised of  Jan Fuglestvedt ,  Maisa Corradi ,  Malte Meinshausen ,  Piers Forster ,  Joeri Rogelj  and  Steven Smith , for their support and guidance throught Phase 1. We look forward to their ongoing support in further phases. ZN benefited from support provided by the  ARC Centre of Excellence for Climate Extremes  ( CE170100023 ). KT is supported by the  545 Integrated Research Program for Advancing Climate Models (TOUGOU Program ), the  Ministry of Education, Culture, Sports, Science, and Technology (MEXT), Japan.
The ACC2 model code is available upon request. The implementation of the AR5IR model used in this study is available in the OpenSCM repository: https://github. com/openclimatedata/openscm/blob/ar5ir-notebooks/notebooks/ar5ir_rcmip. ipynb The model version of ESCIMO used to produce the RCMIP runs can be downloaded from http://www. 2052. info/wp-content/uploads/2019/12/mo191107%202%20ESCIMO-rcimpfrom%20mo160911%202100%20ESCIMO. vpm. The vpm extension allows you to view, examine and run the model, but not save it. The original model with full documentation is available from http://www. 2052. info/escimo/. FaIR is developed on GitHub at https://github. com/OMS-NetZero/FAIRand v1. 5 used in this study is archived at Zenodo (Smith et al. , 2019). The GREB model source code used is available, upon request, on Bitbucket: https://bitbucket.
org/rcmipgreb/greb-official/src/official-rcmip/. The last stable versions are available on GitHub at https://github. com/christianstassen/greb-official/releases. The Held two layer model (uom implementation) is available in the OpenSCM repository: https://github. com/openclimatedata/openscm/blob/ar5ir-notebooks/notebooks/held_two_layer_rcmip. ipynbHector is developed on GitHub at https://github. com/JGCRI/hector. The exact version of Hector used for these simulations can be found at https://github. com/JGCRI/hector/releases/tag/rcmip-tier1. The scripts for the RCMIP runs are available at https://github. com/ashiklom/hector-rcmip. MAGICC's Python wrapper is archived at Zenodo (https://doi. org/10. 5281/zenodo. 1111815)and developed on GitHub at https://github. com/openclimatedata/pymagicc/.
OSCAR v3 is available on GitHub at https://github. com/tgasser/OSCAR. WASP's code for the version used in this study is available from the supplementary material of  Goodwin Goodwin  (2018): https://doi. org/10. 1029/2018EF000889. See also the WASP website at http://www. waspclimatemodel. info/download-wasp. The other participating models are not yet available publicly for download or as open source. Please also refer to their respective model description papers for notes and code availability. Author contributions. ZN and RG conceived the idea for RCMIP. ZN, MM and JL setup the RCMIP website (rcmip. org),produced the first draft of the protocol and derived the data format. All authors contributed to updating and improving the protocol. ACC2 results were provided by  KT  and  EK. AR5IR  and Held et al.
The goals of this study are to better understand the synoptic-scale conditions under which regional FLCs in the Namib occur, to understand how synoptic-scale variability changes local conditions, and thereby to assess the relevance of different potential fog formation mechanisms. To these ends, a 14-year time series of geostationary satellite observations of FLCs in the central Namib is combined with reanalysis data and air-mass back trajectories to systematically analyze the large-scale dynamic conditions and air-mass characteristics that are associated with FLC occurrence in the Namib. The guiding hypothesis for this study is given in the paragraph below. Fog and low clouds in the central Namib are primarily of an advective nature and therefore associated with distinct synoptic-scale patterns of atmospheric dynamics and airmass history.