dataesr/scientific-paragraphs-categorization huggingface dataset:
Characterization Small angle X-Ray scattering (SAXS) measurements were carried out on a "SAXSess mc²" instrument (Anton Paar), using line-collimation system. This instrument is attached to a ID 3003 laboratory X-Ray generator (General Electric) equipped with a sealed X-Ray tube (PANalytical, λ Cu, Kα = 0. 1542 nm) operating at 40 kV and 50 mA. Each sample was introduced in a "Special Glass" capillary, with a diameter equal to 1. 5 mm and 2. 0 mm for micellar solutions and liquid crystals, respectively, or between two sheets of Kapton® for materials, then placed inside an evacuated sample chamber and exposed to X-Ray beam. Scattering of X-Ray beam was recorded on a CCD detector (Princeton Instruments, 2084 x 2084 pixels array with 24 x 24 µm² pixel size, sample-detector distance = 309 mm). Using SAXSQuant software (Anton Paar), the two-dimensional image was integrated into onedimensional scattering intensities I(q) as a function of the magnitude of the scattering vector q = (4π/λ) sin(θ), where 2θ is the total scattering angle.
Thanks to a translucent beamstop allowing the measurement of an attenuated primary beam at q = 0, all measured intensities can be calibrated by normalizing the attenuated primary intensity. All data were then corrected for the background scattering from the cell and for slit-smearing effects by a desmearing procedure from SAXSQuant software, using Lake method. For micellar solutions, after correction, obtained intensities are scaled into absolute units using water as a reference material. Nitrogen sorption isotherms were determined on a Micromeritics TRISTAR 3000 equipment at -196 °C over a wide relative pressure range from 0. 01 to 0. 995. The pore diameter and the pore size distribution were determined by applying the BJH (Barret, Joyner, Halenda) method  [33]  to the adsorption branch of the isotherm.
Data and sampling Data were collected using two separate questionnaires from both household heads and spouses. The household head questionnaire covered topics including household characteristics, assets, access to services, technology adoption, knowledge of agricultural practices, experience with video, crop sales, non-farm income, savings, food security, shocks, and plot-level information on land use, production, and inputs. The spouse questionnaire included sections on assets, technology adoption, knowledge of agricultural practices, and experience with video. A total of 2,422 farm households were randomly selected from 30 woredas and 347 kebeles located in the study area (Table  3 ). The sampled households were selected using a four-stage sampling process, as follows. 1. Defining the study population.
For this reason, we did not encourage DAs to focus their attention on 10 development groups as we did in the treatment group kebeles. We discuss the implication of this difference below. Additional data were collected with a survey of 896 extension agents at baseline (  2017 ) and followed up with midline survey (2018) of 781 DAs. All DAs in service in the selected kebeles in all three arms of the trial were included in the sample of respondents for these surveys. The DA baseline survey collected information on their profiles, motivation, workload, and kebelelevel production figures for the previous year. Source: Authors.
ACKNOWLEDGMENTS This research received financial support from  Digital Green  with funding from the  Bill & Melinda Gates Foundation ; the  U. S. Agency for International Development (USAID)  under  Feed the Future's Developing Local Extension Capacity (DLEC)  Project; and the  CGIAR Research Program on Policies, Institutions , and Markets. This study is registered in the AEA RCT Registry with the unique identifying number  AEARCTR-0003724 ; see Abate et al. (2019) for details. We thank our colleagues at  Digital Green-Tadele Fayso ,  Kebede Ayele ,  Wondwossen Hailu ,  Chimdo Anchala ,  S. Kaushik ,  Kebede Ayele ,  Lakshmi Iyer ,  Michelle Kurian ,  Suprita Kudesia ,  Karin D. Lion , and  Rikin Gandhi -for their continuous support to this study. We also thank  Kate Orkin  and  Alemayehu Seyoum Taffesse  for their early input on the study design;  Zhe Guo  for technical support; colleagues from the  Ministry of Agriculture and Ethiopia's extension community  for their feedback on earlier versions of this work; and participants at the 2018  North East Universities Development Consortium (NEUDC ) annual conference, the 2019  International Food Security Symposium , and several other events for their comments on earlier versions of this paper.
Measurements of differential cross sections for inclusive very forward jet production in proton-lead collisions as a function of jet energy are presented. The data were collected with the CMS experiment at the LHC in the laboratory pseudorapidity range -6. 6 < η < -5. 2. Asymmetric beam energies of 4 TeV for protons and 1. 58 TeV per nucleon for Pb nuclei were used, corresponding to a center-of-mass energy per nucleon pair of √ s NN = 5. 02 TeV. Collisions with either the proton (p+Pb) or the ion (Pb+p) traveling towards the negative η hemisphere are studied. The jet cross sections are unfolded to stable-particle level cross sections with p T 3 GeV, and compared to predictions from various Monte Carlo event generators. In addition, the cross section ratio of p+Pb and Pb+p data is presented.
The HIJING event generator models hard QCD interactions using the PYTHIA v5. 7  [36]  event generator, based on leading order collinear factorization and parton showering with DGLAP evolution. The Eichten-Hinchliffe-Lane-Quigg parameterization of the nucleon pdf is used  [37]. The soft interactions are modeled with the Lund FRITIOF  [38]  and dual-parton  [39]  models. In addition, HIJING incorporates saturation effects via nuclear shadowing  [22, 40, 41]. These shadowing corrections are obtained from a fit of the ratio of nucleus to proton sea quark structure functions at moderate x values (x > 10 -3 ), which are then extrapolated to lower x. The EPOS-LHC program is an update of EPOS version 1. 99 that has been specifically tuned to reproduce the first LHC p+p, p+Pb, and Pb+Pb measurements.
This model uses a combination of soft pomeron exchange (as in Regge-Gribov theory  [42, 43] ) and a semihard contribution based on the convolution of a nonperturbative pre-evolution, a DGLAP-based hard evolution, and standard leading order QCD 2 → 2 cross sections  [44]. The semihard contribution has been tuned to HERA structure function data and is recast as a hard pomeron amplitude. Saturation is modeled through pomeron-pomeron interactions and is implemented by modifying the x dependence of the pomeron amplitudes. In Ref. [45] , however, it is shown that this leads to too strong a suppression of the hard component, a shortcoming that will be remedied in an upcoming version of the model. Similar to EPOS-LHC, the QGSJETII-04 model is based on the Regge-Gribov theory for the soft interactions and hard matrix elements convolved with DGLAP evolution for the hard scatterings.
In this case, pomeron self-interactions result in saturation effects. The Cosmic Ray Monte Carlo CRMC package v1. 5. 6  [46]  is used to generate the cross sections for the EPOS-LHC, HI-JING, and QGSJETII-04 models. The KATIE program allows calculations of tree-level matrix elements with offshell initial-state partons. It is therefore ideally suited to be used in combination with transversal-momentum dependent PDFs, known as "TMDs". In this paper, KATIE is used in a hybrid high-energy factorization approach where high-x partons in one incoming hadron are treated in the collinear framework, while low-x partons in the other incoming hadron are modeled using a TMD  [47] [48] [49] [50]. Such an approach is considered to be valid in configurations with very asymmetric fractional momenta, resulting, for example, in forward jet production.
The TMDs are obtained from the Kutak-Sapeta (KS) framework  [51] , combined with linear (BFKL) or nonlinear (BK) evolution with running coupling, respectively. The strength of the nonlinear term can be varied; here, a default strength of 75% is used, with a variation of ±25% to reflect the sensitivity to nonlinear evolution. The parton density used in the collinear framework is the CTEQ10 NLO set  [52]. The KATIE program is used to calculate leading order hard scattering matrix elements for the 2 → 1 process (g * q → q or g * g → g) with incoming offshell gluons. The subsequent hadronization is modeled using CASCADE v2. 4. 13  [53]. A more in-depth description of the predictions that are included in the present paper can be found in Ref. [54]. The AAMQS predictions  [24]  for the forward jet spectra are also obtained using hybrid factorization.
Data analysis The p+Pb and Pb+p collisions that are analyzed in this study were delivered to CMS in 2013. Only data collected when the whole CMS detector was optimally functioning are used, corresponding to a total integrated luminosity of the p+Pb and Pb+p data sets of 3. 13 and 6. 71 nb -1 , respectively. The peak pileup (i. e. , the maximum average number of simultaneous interactions per bunch crossing) in these runs was 0. 0763. Events were collected using a minimum bias trigger  [67]  requiring the presence of a track in the pixel detector (with acceptance |η| ≤ 2. 5) with a p T above 0. 4 GeV. In addition, an energy deposit above 4 GeV in the HF calorimeters (with acceptance 3 ≤ |η| ≤ 5) on both sides is required offline. It has been shown in Ref. [68]  that this selection effectively suppresses the contribution of photon-induced and diffractive events to a negligible level.
These systematic uncertainties are described in Section 5. For the Pb+p data and simulations, the energy spectrum extends well beyond the incoming energy per nucleon of 1. 58 TeV, because the jet algorithm picks up energy deposits originating from the ion remnants, for which the spectra are not corrected. The detector-level distributions indicate that HIJING yields the best overall description of the data, although the description of the beam remnants can certainly be further improved. The EPOS-LHC and QGSJETII-04 models underestimate the measurements, in particular in the high-energy part of the p+Pb spectrum. The ratio of energy spectra of p+Pb to Pb+p data is shown in Fig. 3. The data are corrected for detector efficiency and acceptance, bin migrations, and detectorlevel jets nonexistent at stable-particle level (fake jets) through an unfolding procedure.
Systematic uncertainties The following sources of systematic uncertainty are investigated, given in order of the size of their contribution: • Energy scale uncertainty. The CASTOR energy scale uncertainty is ±15%  [77]. Therefore, the CASTOR jet-energy spectra are obtained with the energy scale varied in the reconstructed data by ±15% to estimate the associated uncertainty. The propagated energy scale uncertainty is largest in the p+Pb spectrum, since this is the steepest descending spectrum. • Model dependence. Different MC event generators (EPOS-LHC, HIJING, and QGSJETII-04) are each used to unfold the data. As shown in Fig. 2 , HIJING is found to describe the data best and therefore this generator is used for the default unfolding proce-dure, while EPOS-LHC and QGSJETII-04 are used to estimate the systematic uncertainty.
These simulated events are subsequently used to unfold the data. For the detector-level spectra the alignment uncertainty was obtained by reconstructing the spectra with CASTOR simulated at its maximally displaced positions. • Jet identification uncertainty. Jets are identified to be of hadronic origin before calibration functions are applied. This results in a small fraction of "true" rejected jets (1 and 0. 4% in p+Pb and Pb+p, respectively). Since some discrepancy between data and simulation was observed in the distribution of the jet identification variables, the energy spectra are also reconstructed in data without any jet identification, thus treating all reconstructed jets at detector level as being of hadronic origin and letting the unfolding procedure correct for any mismatch between detector and stableparticle level.
Results The unfolded differential cross sections for inclusive forward jet production as a function of jet energy are shown in Fig. 4  for p+Pb and Fig. 5  (left) for Pb+p data. The ratio of the cross sections in p+Pb to Pb+p data is shown in Fig. 5  (right). The latter distribution would be in principle expected to be directly sensitive to parton saturation effects, independently of any particular data-model comparison. Indeed, whereas jets in the p+Pb data probe the ion parton +1 -2 +120 - 78 +1 -2 +35 -35 Model dependence +18 -18 +41 - 41 +4 -4 +60 - 60 +1 - 17 +47 -47 Alignment +4 -4 +34 - 34 +10 - 10 +33 - 33 +14 -3 +34 -6 Jet identification +2 -2 +24 - 24 +2 -2 <1 <1 <1 <1 +25 -25 Total +19 - 19 +160 - 92 +11 - 11 +140 -100 +27 - 26 +77 -54 density at low values of x and are therefore sensitive to possible enhanced saturation effects in nuclei, the Pb+p cross section would be sensitive to the low-x content of the proton, which is expected to be less affected by saturation.
The predictions of the EPOS-LHC and QGSJETII-04 model differ by more than two orders of magnitude at E = 2. 5 TeV. The HIJING model describes the measured distributions best, but this does not automatically imply that its underlying dynamics, DGLAPbased nuclear PDFs with shadowing, is favored by the data, given the many other model ingredients incorporated in this MC generator that affect the results. The EPOS-LHC and QGSJETII-04 models both yield an energy spectrum that is too soft and underestimate the data at high energy. As mentioned in Section 3, EPOS-LHC suffers from a mismodeling of the absolute normalization of hard scatterings in proton-nucleus collisions that has been improved in more recent versions of the event generator  [45]. Predictions of the KATIE model are compared to data in Fig.
The EPOS-LHC and HIJING models describe the data reasonably well, but are too low in normalization. The QGSJETII-04 model again yields a spectrum that is too soft. Finally, EPOS-LHC, HIJING, and QGSJETII-04 predictions are also compared to the ratio of cross sections in p+Pb and Pb+p data in Fig. 5  (right), where substantial uncertainty cancelation occurs. None of the models are able to describe the data over the whole range. The HIJING model describes the shape of the data best, but fails to describe its magnitude because it underestimates the normalization of the Pb+p spectrum. The EPOS-LHC model describes the lower energy part of the ratio spectrum well, but fails to describe the shape at high energies because of problems describing the hard component of the jet cross section. Finally, QGSJETII-04 underestimates both the shape and normalization of the ratio, which can also be attributed to the poor description of the p+Pb spectrum.
The data-model discrepancies described above appear more pronounced in the detector-level distributions plotted in Fig. 3 , as these latter distributions do not have an associated modeling uncertainty. pseudorapidity range -6. 6 < η < -5. 2, and as a function of jet energy. Collisions with either the incoming proton (p+Pb) or the incoming ion (Pb+p) directed towards the negative η hemisphere are studied. The jet cross sections are unfolded to stable-particle level cross sections with p T 3 GeV and compared to predictions from various Monte Carlo event generators. The cross section ratio for p+Pb to Pb+p data as a function of jet energy has also been measured, and exhibits a much smaller systematic uncertainty than the individual spectra. The so-far unexplored kinematic phase space covered by this measurement is sensitive to the parton densities and their evolution at low fractional momenta.
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 centers 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) ;  SENESCYT (Ecuador) ;  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) ;  NKFIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CINVESTAV ,  CONACYT ,  LNS ,  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, CPAN, PCTI  and  FEDER (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).
Individuals have received support from the  Marie-Curie program  and the  European Research Council and Horizon 2020  Grant, contract No. 675440  ( European Union ); the  Leventis Foundation ; the A. P. Sloan Foundation; the  Alexander von Humboldt Foundation ; the  Belgian Federal Science Policy Office ; the  Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture (FRIA-Belgium) ; the  Agentschap voor Innovatie door Wetenschap en Technologie  ( IWT-Belgium ); the  F. R. S. -FNRS  and  FWO (Belgium)  under the " Excellence of Science -EOS "be. hproject n. 30820817; the  Ministry of Education, Youth and Sports (MEYS)  of the Czech Republic; the  Lend ület ("Momentum") Program  and the  János Bolyai Research Scholarship of the Hungarian Academy of Sciences , the  New National Excellence Program ÚNKP , the  NKFIA  research grants  123842 ,  123959 ,  124845 ,  124850  and  125105  (Hungary); the  Council of Science and Industrial Research, India ; the  HOMING PLUS program  of the  Foundation for Polish Science , cofinanced from  European Union, Regional Development Fund , the  Mobility Plus program of the Ministry of Science and Higher Education , the  National Science Center (Poland) , contracts Harmonia  2014/14/M/ST2/00428 ,  Opus 2014/13/B/ST2/02543 ,  2014/15/B/ST2/03998 , and  2015/19/B/ST2/02861 , Sonata-bis  2012/07/E/ST2/01406 ; the  National Priorities Research Program  by  Qatar National Research Fund ; the  Programa Estatal de Fomento de la Investigaci ón Científica y Técnica de Excelencia María de Maeztu , grant  MDM-2015-0509  and the  Programa Severo Ochoa del Principado de Asturias ; the Thalis and Aristeia programmes cofinanced by  EU-ESF  and the  Greek NSRF ; the  Rachadapisek Sompot Fund for Postdoctoral Fellowship, Chulalongkorn University  and the  Chulalongkorn Academic into Its 2nd Century Project Advancement Project (Thailand) ; the  Welch Foundation , contract  C-1845 ; and the  Weston Havens Foundation (USA).
Exposure data In the region of London and Oxford PM was monitored during the years 2010-2011 as part of the European Study of Cohorts and Air Pollution Effects (ESCAPE) project. 10 11 Filters measuring PM 10 and PM 2. 5 from the ESCAPE project were analysed for elemental composition and de Hoogh et al (2013)  12  developed land use regression (LUR) models for a number of the elemental components including metals as part of the TRANS-PHORM project. These models were used to predict PM 10 and PM 2. 5 elemental composition for our study population for 2010-2011. In brief, 20 sites were monitored for three 2-week periods 10 and PM 2. 5 and PM 10 were separately collected using Harvard impactors. Their elemental composition was analysed using energy dispersive X-ray fluorescence. The association of PM elemental components with land use covariates relative to traffic, population, industry, or nature was evaluated with LUR models.
Confounder data-deprivation, ethnicity and smoking data To adjust for possible confounders in this study, we included area-level ethnicity from Census 2011 and accounted for percent of White and Asian people per ward as covariates in the models. We also used the 2007 index of multiple deprivation (IMD) as a relative measure of area-level deprivation (publicly available from the Department for Communities and Local Government data. gov. uk). This combines seven domains: 'income', 'employment', 'education', 'barriers to housing and services', 'crime', 'health' and 'living environment'. The latter is divided into two subdomains: 'indoor' measuring the quality of housing and 'outdoor' linked to air quality and road traffic accidents. 13 We excluded from the study the 'health' and 'outdoor living environment' domains,  14  since we examined associations between health outcomes and air pollution measures.
health data Mortality counts for cardiovascular (International Classification of Diseases, Tenth Revision (ICD10) I00-I99) and respiratory (CDC10 J00-J99) disease and lung cancer incidence counts (C33 and C34 ICD10 codes) were extracted for 2008-2011 from Office National Statistics data held by the Small Area Health Statistics Unit, which provide 100% coverage of deaths. The counts were then adjusted by sex and 5-year age band.
5 and PM 10 size fractions. Metal exposures were highly correlated, so it is difficult to definitively attribute an association with one metal element. Advantages of our study include the use of extremely large data sets with population coverage giving good statistical power to detect even very small associations. Another advantage was the use of standardised exposure models developed from standardised monitoring campaigns to estimate spatial variability in long-term exposures. While exposure data were derived from LUR models that showed good predictability, they may   still misclassify true exposure as (i) prediction is good but not perfect (ii) using a model of exposure at residence as a proxy for personal exposure. A limitation in our exposure assessment is the limited number of monitoring sites, 20, which potentially can lead to overfitting of the developed LUR models.
Acknowledgements We would like to acknowledge and thank  Professor John Gulliver ,  Dr Gary Fuller ,  Dr David Morley  and  Professor Nicky Best  for their useful comments. CACI tobacco expenditure data are © Copyright 1996-2014 CACI Ltd. Contributors AL and AFS drafted the paper and ran the statistical analyses. KdH provided exposure data. SL, JM and MB advised on the statistical methods. AH designed the study. All the authors provided intellectual input, interpreted the results and helped to revise the manuscript. All authors approved the final version of the manuscript and agreed to be accountable for all the aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. AH is the guarantor of this paper.