dataesr/scientific-paragraphs-categorization huggingface dataset:
An irradiation program DV50 was carried out from 2002 to 2006 in the OSIRIS material testing reactor (CEA-Saclay center) to assess the pressure vessel steel toughness curve for a fast neutron fluence (E >1 MeV) equivalent to a French 900-MWe PWR lifetime of 50 years. This program allowed the irradiation of 120 specimens out of vessel steel, subdivided in two successive irradiations DV50 n • 1 and DV50 n • 2. To measure the fast neutron fluence (E >1 MeV) received by specimens after each irradiation, sample holders were equipped with activation foils that were withdrawn at the end of irradiation for activity counting and processing. The fast effective cross-sections used in the dosimeter processing were determined with a specific calculation scheme based on the Monte-Carlo code TRIPOLI-3 (and the nuclear data ENDF/B-VI and IRDF-90).
For the neutron calculation of the fast effective cross-section, we assume that the neutron energy spectrum does not vary or varies little during the irradiation: (E, t) = (E). (3) Therefore, the fast effective cross-section for a dosimeter would be the ratio of the calculated reaction rate and fast flux: E > 1 MeV = E > 1 MeV = ∞ 0 MeV (E) (E) dE ∞ 1 MeV (E) dE • (4) 02012-p. 4 Fast effective cross-sections used in the initial dosimetric interpretation of DV50 experiment were determined with a specific calculation scheme based on: -The Monte-Carlo code TRIPOLI-3 used for simulating the DV50 experiment in the OSIRIS reactor, -Its associated multigroup-wise data library from the nuclear data evaluation ENDF/B-VI for Monte-Carlo transport simulation, -The dosimetric response library IRDF-90 used for the fast dosimeters Ni, Cu, Fe and Nb.
In the TRIPOLI-3 calculation, a 3D-modeling of the core geometry was performed with homogenous blocks of fuel elements (Fig. 3 ). This was a fixed-source simulation using a neutron-source 2Ddistribution based on a power map calculated by the 2D-diffusion code DAIXY  [10]  (used in the operation of the OSIRIS reactor); a cosine-shaped profile was considered for the axial distribution and taken from previous measurements in the OSIRIS core. The neutron-source intensity was normalized to correspond to the rated power of the OSIRIS reactor (70 MWth). DV50 devices (n • 1 and n • 2) were identical, so one TRIPOLI-3 calculation was performed for both. The fast effective cross-sections should be the same for these two devices. Nevertheless, some correction factors (called "unbiaising factors" issued from different feedbacks and benchmarks) were used to correct only the fast effective cross-sections of DV50 n • 1, but not those of DV50 n • 2 (the reason was not clearly stated in the DV50-dosimetry reports emitted in 2005-2006).
New Dosimetric Interpretation of the DV50 Experiment The principle of this new dosimetric interpretation of the DV50 experiment is to reassess the fast effective cross-sections used in the dosimeter processing by using more recent tools and nuclear data, in -The Monte-Carlo code TRIPOLI-4 used for simulating the DV50 experiment in the OSIRIS reactor, -Its associated point-wise data library from the nuclear data evaluation JEFF3. 1. 1 for Monte-Carlo transport simulation, -The dosimetric response library IRDF-2002 used for the fast dosimeters Ni, Cu, Fe and Nb. For a given dosimeter i, the microscopic reaction rate (integrated in time) is the amount directly accessible by neutron measurement and therefore independent from the fast effective cross-section used in the dosimeter processing. So, we have: = i E>1 MeV T 3 • i E>1 MeV T 3 = i E>1 MeV T 4 • i E>1 MeV T 4 (5) i E>1 MeV T 4 = i E>1 MeV T 3 i E>1 MeV T 4 • i E>1 MeV T 3 (6) With: - T 4 respectively the fast effective section of the dosimeter i, issued from the "TRIPOLI-4" calculation scheme, and the corresponding "measured" fast neutron fluence (the result of the new dosimetric interpretation).
In the TRIPOLI-4 calculation, a 3D-modeling of the whole core geometry has been performed, including the exhaustive plate-by-plate description of the OSIRIS fuel elements, in order to take into account the transport of neutrons through water layers between fuel plates (Fig. 4 ). The composition of the fuel plates is fixed for each fuel element according to the burnup of the latter. The core burnup map is issued from the DAIXY code for a representative mid-cycle configuration. The radionuclide inventory as a function of burnup is previously calculated by the APOLLO-2 lattice transport code  [11]. The criticality mode has been used in the TRIPOLI-4 calculation; a neutron-source distribution is only needed to initiate simulation. Since DV50 devices (n • 1 and n • 2) were identical, one TRIPOLI-4 calculation has been performed for both.
Conclusion and Prospects In this paper, we present a new dosimetric interpretation of the DV50 experiment performed by using the Monte-Carlo code TRIPOLI-4 and more recent nuclear data (JEFF3. 1. 1 and IRDF-2002). Obtained results show consistency of previous and new calculated fast effective cross-sections used for dosimeter processing and hence consistency of measured fast fluences determined by initial and new dosimetric interpretations. The new interpretation does not challenge the initial one. However, new values of calculated fast neutron fluxes are found in better agreement with measurements. So, the calculation scheme, based on the TRIPOLI-4 Monte-Carlo code and the recent nuclear data, allows better prediction of fast flux in vessel steel experiment irradiated in the OSIRIS reactor.
5% for showering electrons in the endcaps  [21]. Muons are measured in gas-ionisation detectors embedded in the steel flux-return yoke outside the solenoid. They cover the pseudorapidity range |η| < 2. 4, with detection planes made using three technologies: drift tubes, cathode strip chambers, and resistive plate chambers. Matching muons to tracks measured in the silicon tracker results in a relative transverse momentum resolution for muons with 20 < p T < 100 GeV of 1. 3-2. 0% in the barrel and better than 6% in the endcaps  [22]. The first level of the CMS trigger system uses information from the calorimeters and muon detectors to select the most interesting events. A high-level trigger processor farm decreases the event rate from approximately 100 kHz to 600 Hz before data storage. A more detailed description of the CMS detector, together with a definition of the coordinate system and kinematic variables, can be found in Ref.
1 Monte Carlo (MC) program  [26] , matched to PYTHIA 6. 4  [27]  for the parton showering and hadronization, for the bb channel. Single top quark events are generated at NLO using POWHEG 1. 0. Simulated events for other samples are obtained using the MADGRAPH 5. 1 MC matched to PYTHIA 6. 4. The PYTHIA parameters affecting the description of the underlying event are set to those of the Z2* tune  [28]. All generators used for processes including τ leptons in the final state are interfaced with TAUOLA 2. 4  [29]  for the simulation of the τ decays. The detector response is simulated using a detailed description of CMS, based on the GEANT4 toolkit  [30]. The simulated samples account for contributions from pileup collisions that reflect the distributions observed in data. The trigger and reconstruction efficiency in the simulation is rescaled by as much as 2% in order to match that measured in the data  [24].
The bb channel The relevant sources of background for the bb final state originate from Z+jets processes, tt and tW production, diboson production, and vector boson production in association with a SM Higgs boson. The contributions of Z+jets and tt backgrounds are measured by means of a databased method, the diboson and tW backgrounds are normalised to the CMS measurements. For these backgrounds, the shapes are taken from MC, while the normalisations are extracted from data. The vector boson production in association with a SM Higgs boson is normalised to the theoretical prediction. The comparison of data and predictions after the selection of events for the bb final state shows the importance of an accurate theoretical calculation of the Z+jets production rate. In particular, in the 400-700 GeV range of the m bb distribution, the data is found to exceed the LO prediction by up to two standard deviations, depending on the considered mass.
The ττ channel Methods based on both data and simulation are used to estimate the residual background after event selection. Normalisations and mass distributions in the ZZ, Zh, as well as for the minor fully leptonic WWZ, WZZ, ZZZ and ttZ backgrounds are estimated from simulation. The Z+jets and WZ+jets contributions are measured by means of a data-based method. Production of Z+jets and WZ+jets constitutes the main source of background when at least one lepton is misidentified. Misidentified light leptons arise from semileptonic decays of heavyflavour quarks, decays in flight of hadrons, and photon conversions, while jets originating from quarks or gluons can be misidentified as τ h. Backgrounds with at least one misidentified lepton are estimated from control samples in data starting from the estimation of the lepton misidentification probabilities.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centres and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  MoER ,  ERC IUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NIH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  CINVESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MESTD (Serbia) ;  SEIDI  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ;  ThEPCenter, IPST, STAR  and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU  and  SFFR (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
This was confirmed by a later analysis of the National Health and Nutrition Examination Surveys (NHANES) that estimated FM to be 19% in adults  (NHANES 1999 (NHANES -2006) )   [13]. More recently, analyses of NHANES 2005-2010 have estimated FM to be 17%-25% in adults and 25%-30% in children aged 4-13 years  [14, 15]. In Mexico, FM has been estimated to be higher at 34. 5% in children and adolescents aged 1-18 years  [16]  despite high intake of beverages by children in Mexico  [17]. While data on the water content of foods are usually available in food composition tables and databases, the overall contribution of water in foods to TWI is seldom reported. In a recent survey of population nutrition and diet surveys in Europe, only one country reported calculating FM  [18]. As a consequence of this lack of data, the EFSA assumed that the contribution of FM to TWI was 20%-30% when formulating their recommendations on AI of total water.
The French Nutritional Survey (CCAF 2013) The French survey was a descriptive, cross-sectional study conducted in 2012-2013 by the Research Centre for the Study of Life Conditions (CREDOC). This is a non-profit government organization that studies living and lifestyle conditions in France and periodically collects data about intake of food, energy, macro-and micronutrients of the French population. The details of participant recruitment are consistent with the CREDOC methodology  [21]. A quota method, taking into account age, socioeconomic level, region, town size and household size was used to recruit, through face-to-face interviews, a nationally representative sample of 2000 French households. The final sample for this analysis consisted of 901 children aged 3-18 years and 1062 adults aged 19 years and over.
The data collection was conducted between October 2012 and July 2013. To control for seasonal differences in intake, the study was carried out in four successive phases (October-December, January-March, April-mid-June, mid-June-July), during each of which approximately a quarter of the participants were included. A seven-day diary was used to assess all dietary intake (fluids and solids). The participants received written instructions on how to complete the seven-day food and fluid diary. The diary was used to record information on all eating and drinking occasions throughout the day and for individual eating occasions. The circumstances of intake (time and location) were noted. Respondents completed the food diary with the aid of a validated photographic booklet  [22] , which presented various common foods and beverages in different portion sizes.
To ensure comparability between age groups of both surveys, data from children aged less than four years was excluded in the NDNS sample. Some individuals later agreed to be interviewed by a nurse with fewer agreeing to give blood or urine samples. The data were weighted to minimize selection bias. All relevant research ethics and governance committees approved the survey. Subjects were asked to complete a four-day estimated food diary with a randomly selected start date, which facilitated sampling across all days of the week. An interviewer visited the household to explain how to keep the four-day food diary. Interviewers visited or phoned the household on day two or three to check the ongoing diary completion and visited to review and collect the diary no more than three days after the final recording day.
Photograph atlases were provided to aid estimation of portion sizes. Parents or carers assisted children aged 11 years and younger with completion of the diary. A trained nurse measured height and weight on a subsequent visit. Completed diaries were coded and entered into HNR's dietary assessment system (DINO, Diet In Nutrients Out) that used food composition data from the Department of Health's NDNS Nutrient Database to estimate dietary intake of foods, including water and beverages and nutrients, including energy and water. Computerized raw data files and documentation from this survey were obtained under license from the UK Data Archive  [25]. The food groups considered as fluids are shown in supplementary materials Table  S1. Soup was categorized as a food and milk as fluid.
Statistical Analysis The SAS 9. 2 software (SAS Institute Inc. , Cary, NY, USA) was used for statistical analysis. The data of TWI were normally distributed. Continuous variables are presented as mean and standard deviation as appropriate and dichotomous variables as frequency and percentage. The number and proportion of participants with an inadequate TWI was calculated by comparing TWI to the age and gender specific AI of TWI set by EFSA  [5] , which are shown in Table  1. General linear models were used to identify the variation in TWI due to water from fluids or FM. Before analyzing the four waves of NDNS as one sample, a statistical comparison between the waves was performed. A significant difference in TWI between waves was observed only among subjects aged 11-18 years (p = 0. 006), and not among subjects aged 4-10 years, 19-64 years and ≥65 years.
Acknowledgments: CREDOC received funding for data access and data analysis, and J. G. for paper redaction from Danone Research. The costs to publish in open access were covered by Danone Research. Author Contributions: J. G. , I. G. , H. G. , C. M. and G. T. conceived and designed the experiments; G. T. analyzed the data; all authors contributed to data interpretation; and all authors contributed to the manuscript. Conflicts of Interest: I. G. , H. G. , and C. M. are full-time employees of Danone Research. G. T. has received research funding of Danone Research. J. G. is a member of the fluid intake expert group of Danone Research. J. K. has no conflict of interest to declare.
e. α s (m 2 Z ) = 0. 1134 at NNLO, was obtained. In Ref. [45] , the impact on the ABM11 analysis of fitting different jet datasets from D0, CDF and the 2010 ATLAS data was studied. This was done both at NLO and at NLO * (including the effects of threshold resummation) in jet production. As compared to the default fit, once the Tevatron jet data sets were included, the fitted value of α s (m 2 Z ) was found to vary by an amount +0. 001 -0. 002 , depending on the particular data set and jet algorithm. These jet measurements have not been included in the posterior ABM PDF releases. All these PDF fitting groups have presented major updates recently: MMHT14  [10] , CT14 [6] NNPDF3. 0  [11] , ABM12  [5]  and HERAPDF2. 0 [9] , which will be discussed in more detail in Sect. 2. 3 below.
0 set. Also, Tevatron collaborations are still releasing data on lepton asymmetry and top-pair cross-sections and differential distributions. A wide variety of data sets which help constrain the parton distributions has been made available at the LHC from the ATLAS, CMS and LHCb collaborations. This includes data sets on rapidity-and mass-dependent W ± , Z and γ production, inclusive jet cross-sections, inclusive top-pair production and W + charm quark production, differential top pair production and dijet cross sections. Many of these measurements have been included in recent global PDF sets. Therefore, for all PDF groups the fitted dataset is now considerably wider than that at the time of the 2010 PDF4LHC recommendation.
1187 ± 0. 0007  [107]. A dedicated study of the uncertainties in the determination of α s (m 2 Z ) in the MMHT14 analysis has been presented in  [51]. The NNPDF3. 0 PDF sets  [11]  are the recent major update within the NNPDF framework. In comparison to NNPDF2. 3, NNPDF3. 0 includes HERA inclusive structure function Run-II data from H1 and ZEUS (before their combination), more recent ATLAS, CMS and LHCb data on gauge boson production and inclusive jets, and W +charm and top quark pair production. A subset of jet data was included at NNLO using an approximate NNLO treatment, based on a study  [52]  of the region where the threshold approximation  [53]  is reliable by comparing with the exact gg channel calculation  [54]. Heavy quarks are treated using the FONLL-B scheme (FONLL-A was used in NNPDF2.
1 and NNPDF2. 3), which includes mass corrections to an extra order in α s , thereby achieving a better description of low-Q 2 data on the charm structure function. The compatibility of the NNPDF3. 0 NLO set with the LHCb forward charm production cross-sections has been demonstrated in  [55] , where it was also shown that these data could be useful in reducing the uncertainty on the small-x gluon. The NNPDF3. 0 fitting procedure has been tuned by means of a closure test, namely, by generating pseudo-data based on an assumed underlying set of PDFs. One verifies in this case that the output of the fitting procedure is consistent with the a priori known answer. As a byproduct, one can investigate directly the origin of PDF uncertainties, and specifically how much of it is due to the uncertainty in the data, how much it is due to the need to interpolate between data points, and how much is due to the fact that there is an infinite number of functions which produce equally good fits to a given finite number of data-points.
The running at different energies gives sensitivity to F L (x, Q 2 ) which provides some new information on the gluon. The HERAPDF2. 0 sets are made available also with a default of α s (m 2 Z ) = 0. 118 and with 14 eigenvector sets along with further variations to cover uncertainties due to model assumptions and changes in the form of the parametrization; for CT, MMHT and NNPDF this is not necessary because the input form of the PDFs is flexible enough. Sets including HERA jet and charm data have also been made available. The HERAPDF2. 0 PDFs agree fairly well in general with the CT, MMHT and NNPDF sets, but there are some important differences in central values, particularly for high-x quarks. Overall, the uncertainties on HERAPDF2. 0 PDFs are markedly smaller than for previous versions, but there are still some regions where the PDFs have significantly larger uncertainties than those in the global fits.
Let us also mention that the three general-mass variable-flavour number schemes used by these four PDF sets (S-ACOT-χ, FONLL and RT/RT opt ) exhibit a remarkable convergence, especially at NNLO  [56]. All these four sets fit the HERA charm cross-sections with comparable quality, and residual differences between the three GM-VFN schemes translate into rather small differences in the resulting PDFs. [57]  The most recent update for the ABM collaboration, ABM12  [5] , now includes the HERA combined charm cross-section data, an extension of the HERA inclusive data to higher Q 2 than previously used, and vector boson production data from ATLAS, CMS and LHCb. The heavy flavour contributions to structure function data are now calculated using the M S renormalization scheme  [58]  rather than the pole mass scheme.
The main change compared to ABM11 PDFs is in the details of the decomposition into up and down quarks and antiquarks, this being affected by the LHC vector boson data. The ABM12 PDF sets are determined together with α s , whose value comes out to be rather lower than the PDG average, namely α s (m 2 Z ) = 0. 1132 ± 0. 0011 at NNLO. Top quark pair production data from the LHC is investigated, but not included in the default PDFs. Its inclusion tend to raise the high-x gluon and α s (m 2 Z ) a little; the precise details depend on the precise value of the top quark mass (and mass renormalization scheme) used. The ABM12 PDFs are available with 28 eigenvector pairs for the default α s (m 2 Z ) and at NNLO only. Within the same framework there has been a recent specific investigation of the strange sea  [59]  in the light of LHC data and NOMAD and CHORUS fixed-target data, but this is not accompanied by a PDF set release.
More recently, an update of the ABM12 with additional data from gauge boson production at the Tevatron and the LHC has been presented  [60] , though this study is not accompanied by the release of a new PDF set. There has also been an update of the (G)JR PDFs: JR14  [8]. The default sets from this group make the assumption that PDFs must be valence-like at low Q 2 0 = 0. 8 GeV 2. This analysis extends and updates the fixed-target and collider DIS data used, in particular including CCFR and NuTeV dimuon data as a constraint on strange quarks, and includes both HERA and Tevatron jet data at NLO, but does not include any LHC data. The strong coupling is also determined along with the PDF, yielding a value α s (m 2 Z ) = 0. 1136 at NNLO, and a large small-x gluon distribution. There are some significant changes compared to the JR09 NNLO PDFs.
Dependence on the heavy-quark scheme The results of various benchmark studies  [13, 32, 78]  illustrate how the softer high-x gluon and smaller α s (m 2 Z ) present in PDF sets like ABM12 and JR09 leads to a smaller prediction for top pair cross sections and Higgs bosons as compared to the global PDF sets. In both cases, the magnitude of the difference depends on whether the default α s (m 2 Z ) is obtained directly from the fit or if it is treated as an external input. In these sets also the quark PDFs seem to lead to slightly larger vector boson production cross-sections as compared to the global fits. On the other hand the CT, MSTW/MMHT, and NNPDF, and HERAPDF PDFs at NNLO are in generally good agreement, though HERAPDF has associated rather larger uncertainties from the reduced dataset.