dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors thank the aircraft crew of the WIPAFF campaign,  Rudolf Hankers ,  Thomas Feuerle ,  Mark Bitter  and  Helmut Schulz  from the  Technische Universitat Braunschweig  for their great support. The project WIPAFF is funded by the  German Federal Ministry for Economic Affairs and Energy (Bundesministerium für Wirtschaft und Energie ) on the basis of a decision by the  German Bundestag  under grant number:  FKZ 0325783.
Acknowledgements. AHMD, ME, HDM, and KZ are each grateful for support from respective  National Science and Engineering Research Council of Canada Discovery Grants. TLF  and  FAB  acknowledge support from the  Swiss National Science Foundation  under grant  PP00P2 -170687  and from the  European Union 's  Horizon 2020 Research and Innovative Programme  under grant agreement number  821003  (CCiCC). GFDL ESM2M simulations were performed at the  Swiss National Supercomputing Centre (CSCS). CDJ and AW acknowledge the  Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ) and the  EU Horizon 2020   CRESCENDO  project, grant number  641816. JR, RS and MM acknowledge support from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement number  820829  (CONSTRAIN).
VB is grateful to  Thomas Raddatz  and  Veronika Gayler  for assistance with the MPI-ESM experimental setup and acknowledges support from the  EU Horizon 2020   CRESCENDO  project. AVE was supported by the  Russian Foundation for Basic Research  (grant  18-05-00087 ). TH and KT acknowledge the support from " Integrated Research Program for Advancing Climate Models " by the  Ministry of Education, Culture, Sports, Science, and Technology of Japan. PBH was funded by  LC3M , a  Leverhulme Trust Research Centre Award. AJT acknowledges support by the  Swiss National Science Foundation  (#  200020_172476 ) and the  Oeschger Centre for Climate Change Research. IIM was supported by the  Russian Science Foundation  (grant  19-17-00240 ). GS was supported by  FONDE-CYT (Chile)  grant  1190230. LM acknowledges support from the  Australian Research Council   FT180100606.
Data availability. ESM data will be published and freely available as per CMIP6 data policy on the Earth System Grid Federation (https://esgfnode. llnl. gov/projects/cmip6/ ). EMIC data will be published and freely available on a dedicated server (terra. seos. uvic. ca/ZEC). The annual global mean variables used for the present analysis will also be made available on the server. 32 https://doi. org/10. 5194/bg-2019-492 Preprint. Discussion started: 20 January 2020 c Author(s) 2020. CC BY 4. 0 License.
Datasets We used 296 measurements of AP from 139 published studies  (Margalef et al. , 2017). This dataset contained 54 tropical sites and 242 ex-tropical sites (155 in Europe). Location, soil pH and soil nutrient contents (total soil C, soil organic C, total nitrogen, and total P) for each site were obtained from the original publications. We separated the data set into four subsets as shown in Figure  1:  (1) data used to train the ML models (Data set A), (2) data used for cross-validating the spatial gradients of AP modeled for Europe (Data set B), (3) data used to evaluate AP patterns in temperate regions outside Europe (Data set C), and (4) data used to evaluate AP patterns in tropical regions (Data set D). Dataset A contains 126 European sites with complete information for the all the predictors listed below.
Data set B contains all 155 European sites. Data set C and Data set D contained 87 temperate sites outside Europe and only 54 tropical sites, respectively. We selected 12 variables as predictors for the upscaling based on findings from manipulation experiments  (Hogan et al. , 2010; Huang et al. , 2011; Marklein and Houlton, 2012) , observation time series  (Sardans et al. , 2006)  and previous regression analyses  (Margalef et al. , 2017) : soil pH (SoilpH), soil clay content (Clay), soil organic carbon (OC), soil total nitrogen (TN), soil total phosphorus (TP) and soil labile organic phosphorus (Po), net primary productivity (NPP), mean annual temperature (MAT), amplitude of yearly temperature (AMP), mean annual precipitation (MAP), soil type (SoilType), and vegetation type (VegType). We extracted predictors from each original publication when available.
In case information on predictors were not reported, we extracted the missing data from gridded datasets (Table  1 ) based on the geographical coordinates of each measurement site  (Shangguan et al. , 2014; Ballabio et al. , 2016 Ballabio et al. , , 2019;; Fick and Hijmans, 2017; Hengl et al. , 2017; MOD12Q2 and MOD17). The databases for each predictor variables are listed in Table  1. Detailed information about the databases  are provided in Supporting Information Appendix S1. Details on the gap filling of predictors can be found in Supporting Information Appendix S2. AP, NPP, AMP, and MAP, which followed a lognormal distribution, were log-transformed before build the prediction model (Supporting Information Appendix S2, Figure  S2 ).
Extrapolation of AP for Europe We resampled all of those gridded datasets of predictors (Table  1 , Table  S1 ) on a 10 km resolution using area-weighted mean methods for numerical predictors, and by using mode resampling for categorical predictors (i. e. , vegetation types and soil types). Then we used the 25 selected model ensembles (25 best models × 126 LOO rounds) that produced AP with the highest correlation with observed AP in combination with gridded maps of the predictors at a resolution of 10 km (Figure  2 ) to scale up point observations to continental Europe (36-75 • N and 20 • W to 32 • E). We first used the median of the 25 estimates for each LOO round and then derived the median estimates of AP across 126 LOO rounds to define the AP maps and their uncertainties across the European continent.
Upscaling of Site Data to a Global Map of AP We used the BPN models trained on European data in a first attempt to scale up AP to the global scale. The modeled global spatial pattern of AP show a clear latitudinal gradient of increasing AP from high latitudes to the equator (Figure  7 ). Modeled AP is also much higher for coastal than inland areas. The global up-scaled distribution of AP is however more uncertain than the European distribution. The BPN models explained only a small fraction (<15%) of AP (Supporting Information Appendix S5) variance over all measurements for both temperate sites outsides Europe (Data set C) and tropical regions (Data set D) (Figures  S12A-D ). The low prediction capacity for these sites may be partly attributed to the low accuracy of extracted gridded TN from ISRIC-WISE (Supporting Information Appendix S1).
Implication and Uncertainties The BPN models successfully extrapolated the site AP measurements on the Europe region. However, there are some inevitable uncertainties due to the gap-filling of input predictors which were not reported in the original literature. The extracted values from gridded datasets are biased to some extent, e. g. , due to the coarse spatial resolution of predictor maps as some variables like TN, TP, and Clay vary strongly within very short (cm-m) distance  (Lechowicz and Bell, 1991; Iqbal et al. , 2005). As a consequence, the covariation between observed and extracted values of TN, TP, and Clay are not very strong (Figure  S1 ). However, non-linear BPN models are considered to be more sensitive to the orders of predictor values rather than linear variation among predictor values.
This advantage of BPN compared to linear models reduces the influence of biases in gap-filling on AP predictions. The spearman rank correlations between observed and extracted values for Europe sites are found to be significant for all variables except for Clay (Figure  S1 ). Thus, the data quality seems in general sufficient for the BPN model. Still, the BPN model might underestimate the driving effect of Clay on AP pattern due to the large biases. High-quality gridded datasets especially for soil clay content are required, to reduce the uncertainty in the extrapolations based on machine learning methods.
Moreover, as it was hard to track the whole pathway for each patient, we relied on these sections as a timeline for patients' comments to point out their experiences through the stages of the pathway. We have chosen to analyse the reviews published over a complete year period (from July 2016 to July 2017). The data was timeconsuming to gather and analyse as all the steps were done manually from collection to interpretation. Collected data provides very rich primary source information about their pathway from the discovery of the disease to follow-up and the survivorship stage. In order to model the patients' healthcare pathway along the various stages of treatments, we focused particularly on the types of concerns experienced by patients. Prior studies have come to agree that experience involves a multidimensional interaction through sensorial (senses and how they arouse pleasure, excitement, satisfaction, etc.
A climatology can be built with a strong emphasis on surface observations using model fields to fill the gaps between the sparse network of measurement sites (e. g. Kinne et al. , 2013)  or by merging model fields, satellite data and surface observations using empirical methods (e. g. Liu et al. , 2005). A further option is to rely on a data assimilation system, which is the approach followed in this work. The MACC reanalysis of reactive trace gases and aerosols (MACCRA,  Inness et al. , 2013)  was the first multi-year atmospheric composition reanalysis effort developed with the MACC system, taking advantage of the 4D variational assimilation system (4D-Var) for atmospheric composition  (Benedetti et al. , 2009). Total aerosol optical thickness (AOT) was constrained by assimilating the AOT retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) observations.
CAMS is currently updating MAC-CRA with a new high-resolution atmospheric composition reanalysis (the CAMS reanalysis, CAMSRA), and as an interim product between MACCRA and CAMSRA, a new dataset (CAMS Interim reanalysis, CAMSiRA;  Flemming et al. , 2017)  was produced. CAMSiRA shows a good agreement with the latest surface AOT observations, and combines the most recent advances in global aerosol modelling and satellite retrieval, so it represents an improvement with respect to the current TG97 climatology as well as MACCRA. It also provides a better framework to evaluate the impact of coupling the IFS prognostic aerosol model to the operational forecast system. This document describes the development of a 3D monthly-mean climatology of five aerosol species based on CAMSiRA (Sect. 2). A comparison of the climatological values against daily aerosol optical thickness observations will be discussed in Sect.
This bias is likely linked to the industrial emissions used in TG97 for Europe that are based on the Global Emissions Initiative (GEIA) database relative to the year 1985  (Benkovitz et al. , 1996)  and not representative of the emissions in the period 2003-2013 together with absorption characteristics that are too high, associated with the industrial species. implementation in the ECMWF global weather forecasting system (IFS). We implemented in the IFS the full 3D CAMS climatology with the optical properties computed as described in Appendix B, and we will discuss here the impacts of the new climatology compared to the IFS configuration using the old climatology based on TG97. The implementation of the TG97 and CAMSiRA climatologies in the IFS radiation scheme differ in a number of ways. Both climatologies are represented in the radiation scheme in terms of the three bulk radiative properties mass extinction coefficient (in square metres per kilogram), single scattering albedo and asymmetry parameter at each of the 30 spectral bands of the radiation scheme.
For TG97, the optical properties were computed based on the OPAC database  (Hess et al. , 1998)  at a fixed value of relative humidity. For the CAMSiRA climatology the optical properties are computed according to the description in Appendix B. For the hydrophilic species the optical properties are interpolated to the relative humidity value provided by the IFS model at every grid point and vertical level. Moreover, as already mentioned, CAMSiRA climatology is defined for each grid point and vertical level, while for TG97 an empirical vertical distribution is assumed (see Sect. 2. 1) The different impacts of the two climatologies in the IFS depend therefore on the combined effect of different aerosol spatial and vertical distributions and different radiative properties.
Conclusions This work documents a new monthly-mean climatology of aerosol distribution based on the Interim reanalysis from CAMS  (CAMSiRA, Flemming et al. , 2017)  and its control run. The dataset represents a monthly-mean distribution of the mass mixing ratio of five aerosol species subdivided into 11 types over 60 vertical levels. The climatology is available at full native resolution for the 3D fields or at any coarser horizontal grid. The user can associate the radiative properties of choice with the aerosol distribution, and here we present results that used the bulk properties for each species computed for the 30 radiative bands of the ECMWF radiative scheme  (Hogan and Bozzo, 2018). We tested the impact of the CAMS climatology on the ECMWF Integrated Forecasting System in comparison to the aerosol climatology operational until summer 2017, which was derived from  Tegen et al.
A2. 5 Sea salt The refractive index for seawater is as in the OPAC database, and the optical properties are integrated across the three size ranges in the CAMS model, using bimodal log-normal distributions with particle radius limits 0. 03, 0. 5, 5 and 20 µm as in  Reddy et al. (2005)  and with the same hygroscopic factors according to  Tang et al. (1997) , Table  A2. The complete set of bulk optical properties for all aerosol types is shown in Fig. A2  for the full range of spectral bands used in ECRAD. In Fig. A3  the optical properties used in CAMSiRA climatology are compared to the properties used in the IFS for the TG97 climatology.
Acknowledgements. We thank  Irina Sandu ,  Robin Hogan ,  Richard Forbes ,  Mark Rodwell  and  Julien Chimot  for their valuable comments at various stages of this work. We also thank the  CAMS consortium  for their work towards the improvement of the atmospheric composition modules coupled to the  IFS  and for their efforts in the evaluation of the atmospheric composition products. Luke Jones provided much-needed support for the AERONET comparisons. We thank the  PIs and Co-I's  and their staff for establishing and maintaining the AERONET sites used in this investigation. Two anonymous reviewers provided very helpful comments, which contributed significantly to the improvement of the initial version of this paper. Financial support. This work is supported by the  Copernicus Atmospheric Monitoring Services (CAMS)  programme managed by the  ECMWF  on behalf of the  European Commission.
Code availability. The IFS source code is available subject to a licence agreement with ECMWF; see also Flemming et al. (2015)   and Rémy et al. (2019)  for details. The code used to generate the optical properties for each aerosol species is based on the standard  Wiscombe (1980)  scheme for Mie scattering. Data availability. Two datasets described in this work are available from the CAMS data repository, which are the monthly-mean layer-integrated mass mixing ratio of all aerosol species at a resolution of 3 • × 3 • and 60 vertical levels and the optical properties computed for each species for the 30 spectral band of the ECMWF radiation code  (Hogan and Bozzo, 2018). The data are hosted on the CAMS data archive and available for download at https://doi. org/10. 24380/jgs8-sc58 (Bozzo et al.
The screening was performed on a Microflex LT spectrometer (Bruker Daltonics, Bremen, Germany) as previously reported  [11]. The spectra obtained were saved into MALDI BIOTYPER 3. 0 software (Bruker Daltonics) and analysed against the main spectra of the bacteria included in the local URMS database (https://www. mediterranee-infection. com/urms-data-base) (Fig. 1 ).
Strain identification To classify the strain Marseille-P2775, its 16S rRNA gene was amplified using the fD1 and rP2 primer pair (Eurogentec, Angers, France) and sequenced using the Big Dye® Terminator v1. 1 Cycle Sequencing Kit and 3500xLGenetic Analyser capillary sequencer (Thermofisher, Saint-Aubin, France)  [12, 13]  as described previously. The 16S rRNA nucleotide sequences were assembled and corrected using CODONCODE ALIGNER software (http://www. codoncode. com). The 16S rRNA gene sequence analysis of strain Marseille-P2775 showed 97. 19% identity with Gordonibacter urolithinfaciens strain CEBAS 1/15P (GenBank accession number: NR134044), the phylogenetically closest species with a standing in nomenclature (Fig. 2 ). This value was <98. 7% of similarity, the threshold above which a strain is considered a new species  [14].
Genome sequencing The DNA genomic extraction was performed using the EZ1 biorobot and the EZ1 DNA tissue kit. Genomic DNA (gDNA) was quantified by a Qubit assay. The sequencing was performed using MiSeq technology (Illumina, San Diego, CA, USA) with the Paired-End (Illumina). The assembly was performed with SPADES software  [17]. The reads with low quality were trimmed using TRIMMOMATIC software  [18]. GAPCLOSER software  [19]  was used to reduce the assembly gap. Scaffolds < 800 bp and scaffolds with a depth value < 25% of the mean depth were removed. The total length of the G. massiliensis genome is 3. 9 megabases encompassing 1 contig with a G + C content of 65. 1 mol%. The predicted genes analysis reported 3248 genes. The degree of genomic similarity of strain Marseille-P2775 with closely related species was estimated using the ORTHOANI software  [20].
The spin asymmetry and/or polarisation have been measured in pp collision data by the ATLAS and CMS Collaborations at √ s = 8 TeV using various analysis techniques  [9, 17, 18]. In this paper, the differential cross section of combined single top quark and antiquark production in the t channel is measured by the CMS experiment at √ s = 13 TeV as a function of the top quark transverse momentum (p T ), rapidity, and polarisation angle, the p T and rapidity of the charged lepton that originates from the top quark decay, and the p T of the W boson from the top quark decay. The spin asymmetry is further determined from the measured differential cross section with respect to the polarisation angle. Additionally, a measurement of the differential charge ratio is performed as a function of the p T and rapidities of the top quark and charged lepton, and the p T of the W boson.
Data set and simulated samples The analysed pp collision data set was recorded in 2016 by the CMS detector and corresponds to an integrated luminosity of 35. 9 fb -1  [22]. Events were triggered by requiring at least one isolated muon candidate with p T > 24 GeV and |η| < 2. 4 or one electron candidate with p T > 32 GeV and |η| < 2. 1, with additional requirements  [23]  that select genuine electrons with an efficiency of about 80%. Various samples of simulated events are used in this measurement to evaluate the detector resolution, efficiency, and acceptance, estimate the contributions from background processes, and determine the differential cross sections at the parton and particle levels. Single top quark events in the t channel are simulated at next-to-leading order (NLO) in the four-flavour scheme (4FS) with POWHEG v2  [24, 25]  interfaced with PYTHIA v8.
The selection requirements for these additional muons/electrons are as follows: looser identification and isolation criteria, p T > 10 (15) GeV for muons (electrons), and |η| < 2. 5. The transverse W boson mass is calculated from the formula m T (W) = 2p T p miss T 1 -cos(φ -φ miss ) (3) using the p T and the φ of the charged lepton and p miss T. Jets are reconstructed from PF candidates and clustered by applying the anti-k T algorithm  [44]  with a distance parameter of 0. 4 using the FASTJET package  [45]. The influence of pileup is mitigated using the charged hadron subtraction technique  [48]. The jet momentum is determined as the vectorial sum of all particle momenta in the jet. An offset correction is applied to the jet p T to account for contributions from pileup. Further corrections are applied to account for the nonuniform detector response in η and p T of the jets.
Multijet background estimation Since the probability for a simulated multijet event to mimic the final state of the signal process is very small, it becomes impractical to simulate a sufficiently large number of events for this background. Therefore, the background from multijet events in the analysis phase space region is estimated in a two-step procedure based on data in a sideband region. First, templates of the m T (W) distribution from multijet events are obtained from data in a sideband region. Their normalisations are then estimated in a second step through a template-based ML fit to the events in the 2j1b and 3j2b categories, simultaneously with the number of signal events, as described in Section 6. In this section, a dedicated ML fit is discussed that is performed on events in the 2j0b category only for validating the procedure.
Summary Differential cross sections for t-channel single top quark and antiquark production in protonproton collisions at √ s = 13 TeV have been measured by the CMS experiment at the LHC using a sample of proton-proton collision data, corresponding to an integrated luminosity of 35. 9 fb -1. The cross sections are determined as a function of the top quark transverse momentum (p T ), rapidity, and polarisation angle, the charged lepton p T and rapidity, and the p T of the W boson from the top quark decay. In addition, the charge ratio has been measured as a       function of the top quark, charged lepton, and W boson kinematic observables. Events containing one muon or electron and two or three jets are used. The single top quark and antiquark yields are determined through maximum-likelihood fits to the data distributions.
The Infrared Atmospheric Sounding Interferometers (IASIs) are three instruments flying on board the Metop satellites, launched in 2006 (IASI-A), 2012 (IASI-B), and 2018 (IASI-C). They measure infrared radiance from the Earth and atmosphere system, from which the atmospheric composition and temperature can be retrieved using dedicated algorithms, forming the Level 2 (L2) product. The operational near real-time processing of IASI data is conducted by the EUropean organisation for the exploitation of METeorological SATellites (EUMETSAT). It has improved over time, but due to IASI's large data flow, the whole dataset has not yet been reprocessed backwards. A necessary step that must be completed before initiating this reprocessing is to uniformize the IASI radiance record (Level 1C), which has also changed with time due to various instrumental and software modifications.
In 2019, EUMETSAT released a reprocessed IASI-A 2007-2017 radiance dataset that is consistent with both the L1C product generated after 2017 and with IASI-B. First, this study aimed to assess the changes in radiance associated with this update by comparing the operational and reprocessed datasets. The differences in the brightness temperature ranged from 0. 02 K at 700 cm -1 to 0. 1 K at 2200 cm -1. Additionally, two major updates in 2010 and 2013 were seen to have the largest impact. Then, we investigated the effects on the retrieved temperatures due to successive upgrades to the Level 2 processing chain. We compared IASI L2 with ERA5 reanalysis temperatures. We found differences of ~5-10 K at the surface and between 1 and 5 K in the atmosphere. These differences decreased abruptly after the release of the IASI L2 processor version 6 in 2014.