dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014). In order to refine the chronology of these deposits, in this study, six new charcoal samples were collected from the key archaeological layers in excavation area MN 47/48 of S2. These samples were dated using the AMS radiocarbon method at the CEA Saclay (France). Detailed procedures of the chemical preparation and the dating in the lab were referred to  Cottereau et al. (2007). The dates were calibrated using the IntCal13 data set  (Reimer et al. , 2013)  and reported in years before 2000 AD (Table  2 ).
It is suggested that the methodology and the findings from the present study at the Gueldaman GLD1 Cave be applied and tested at other sites. The Supplement related to this article is available online at doi:10. 5194/cp-12-1-2016-supplement. Acknowledgements. Radiocarbon dating was analyzed by Jean-Pierre Dumoulin at ARTEMIS (LMC14, Saclay). We thank Edwige Pons-Branchu, Monique Pierre for the U/Th dating of the base part of stalagmite GLD1-stm2 during the earlier stage of this study. We also thank Lijuan Sha at the Xi'an Jiaotong University for assistance with the U/Th dating. We appreciate Russell N. Drysdale, Giuliano Zanchetta and Miryam Bar-Matthews for sharing the isotopic data shown in Fig. 5. Thanks to Cecilia Garrec for editing assistance. We are thankful to Giuliano Zanchetta and an anonymous referee for their constructive comments which improved this paper.
Meta-Analysis of Vaccine Effectiveness We estimated the average short-term (less than 1 y) effectiveness of one and two doses of OCV using published data from both randomized and observational (intervention) studies. We included estimates for both internationally licensed vaccines, Shanchol and Dukoral, because of the similar composition in the main antigens and the similar efficacy estimates from the literature, and to make the results of this analysis more generalizable. We estimated the average short-term one-and two-dose efficacy using a random intercept linear regression model  [10]. Confidence intervals for the ratio of these two efficacy estimates were constructed following Newcombe  [11].
In order to assess whether the high number of parameters is a concern, we compare this new prediction performances with those of a Landmark Cox model. As in the development step, we apply the proposed prediction on breast cancer cases, here from two population-based registries, in West Midlands (England) and the Netherlands. Section "Methods" of this paper explains the prediction probabilities within the framework of a Landmark Cox model and a joint frailty model, as well as the tools to validate them. The validation of the prediction on the West Midlands and Netherlands registry datasets is developed in section "Results". Finally, sections "Discussion" and "Conclusion" contain concluding remarks.
Ethical approval and availability of supporting data For the French hospital series, ethical approval from the national ethics committee (Commission Nationale de l'Informatique et des Libertés) was obtained for this study, which allowed the use of data recorded in this clinical and pathological database. In this comprehensive cancer center, each patient was informed that medical data can be use in observational research. The procedure follows the French law for medical research. We hold statutory and ethical approvals to analyse the data from West Midlands Cancer Intelligence Unit and Comprehensive Cancer Centre The Netherlands registry data. All data used are confidential. Researchers may access the data by sending a formal request to the appropriate institution (Institut Bergonié for the French series, West Midlands Cancer Intelligence Unit for the UK registry data and Comprehensive Cancer Centre The Netherlands for the Dutch registry data).
1989-1993). In the two validation samples, a high rate of missing data was observed. Out of the 3168 cases recorded in the year 1996 in West Midlands, 1196 (38%) had non-missing values for all of the five studied prognostic factors. In the Dutch sample, information about peritumoural vascular invasion was not recorded. Of the 41,676 recorded patients, 31,075 (75%) had non-missing values for the four remaining factors. In our validation sample, we included only patients with complete information in the two datasets. This decision is discussed in the last part of the paper. Table  1  compares the repartition of the prognosis factors in the three samples, as well as the number of relapses per patient, and the overall survival. Patients in both validation samples had more severe disease, i. e.
In the Dutch sample, relapse data was obtained directly from patient files; both clinically and pathologically confirmed relapses were recorded. The recording was limited to relapses occurring during the first five years after diagnosis and, in some regions, to the first relapse of each type (local relapse, regional relapse or distant metastasis). In the French cohort, relapses (loco-regional recurrence or distant metastasis) were recorded following a clinical examination. That resulted in 75% of the patients without registered relapse in the West Midlands, 88% in the Dutch sample, and 66% in the French cohort.
Calibration was not better than as observed on the entire West Midlands data. A second subgroup analysis was performed comparing the performance of the proposed predictions between subjects who relapsed at least once before the prediction time of five years and those who did not (Figure  5 ). As expected, in the subgroup of patients without relapse, the results were very similar to those in the entire sample. However, no high-risk subject groups were identified, as observed in the main analysis. In the subsample with relapses, the prediction ignoring the relapses underestimated the observed probabilities of events (all the points hazard, and both approaches gave very similar results (data not shown). The landmarking approach allows the use of a simple and robust model, such as the Cox model.
Acknowledgements This research was funded by the  Institut National du Cancer  and BR is funded by a  Cancer Research UK   Program grant  ( C1336/A11700 ). We thank  Catherine Lagord ,  Jackie Walton  and  Christopher Lawrence  from the  West Midlands Cancer Intelligence Unit , and  Mirian Brink  from the  Comprehensive Cancer Centre The Netherlands  (IKNL), for their help with the data preparation and sharing. We thank  Tsion Solomon  and and  Pippa McKelvie-Sebileau  for editorial review.
g. due to snow fall)  [9] [10]. In the case of waterbirds, changes in migratory phenology have been reported in relation to predation risk  [11] , or climate change, the latter including both the advancement of spring migration [e. g. [12] [13] [14] [15] [16]  and delay of autumn migration  [17]. Changes in the distribution of wintering populations have also been recorded, usually representing a northward shift of geographical ranges [e. g. 18, 15, but see  [19] [20]. These changes are thought to be mainly related to climate change, especially rising temperatures [e. g. 5, 21-23]. However, changes in land-use have also played an important role and some migratory waterbirds have responded positively to the intensification of agriculture or the creation of refuges [e. g. [24] [25] [26]. Wintering waterfowl populations have been monitored for decades across Europe, producing long-term datasets on bird numbers and distribution (http://www.
Geese data National totals for January count data from Sweden, Denmark, Germany, The Netherlands, Belgium and France during 1980-2009 were obtained from the International Waterbird Census (IWC, Wetlands International). Information from Spain during the same period was provided by the Monitoring Team of the Estación Biológica de Doñana (Guadalquivir marshes, which includes the Doñana National Park and surrounding areas), collected by the authors (Villafáfila, Nava, Boada and Pedraza lagoons, and Guadiana ricefields), or obtained from SEO/BirdLife (rest of Spain). No specific permissions were required, as the study relies on census data collected during general surveys of wintering birds carried out in each location for other purposes, and not for the purpose of this paper. The study species is not endangered or protected, and no birds were collected or sampled, only counted from a distance.
Climate and land use data As a measure of the variation in winter temperatures along the flyway we used the annual mean national temperature in January from 95 meteorological stations with complete datasets, located at altitudes below 700 m, and spanning the latitudinal range 36. 5-58. 4°N (http://www. cru. uea. ac. uk/data/, see S1 Table ). According to linear regression there are positive, although not statistically significant, temperature trends in all countries, with increments ranging from 0. 6°C in Spain, to 1. 8°C in Denmark during 1980-2009 (Fig  1 ). Agricultural land use data were extracted from Eurostat database statistics (http://epp. eurostat. ec. europa. eu/portal/page/portal/agriculture/agricultural_production/database, see S2 Fig). The main crops used by wintering geese were winter cereals (common winter wheat and winter barley), potatoes and sugar beet in Sweden  [41] , winter cereals and oilseed rape and sugar beet in Denmark  [27] , winter cereals and oilseed rape in Germany and France  [27] , winter cereals, potatoes and sugar beet in the Netherlands  [42] , winter cereals and potatoes in Belgium  [38] , and cereals and rice in Spain  [43] [44] [45].
All possible sub-models were generated from the general model using the MuMIn package in R (RCore Team 2014). We followed a model selection procedure based on Akaike's Information Criteria (AIC;  [47] ). When several models differed in AIC by less than 2 we generated an averaged full model using MuMIn. Tests confirmed the normality and homoscedasticity of the residuals (only the model for France showed violation of these assumptions). We also performed an analysis of partial autocorrelation of the residuals from each model to determine if there was any temporal structure. No temporal autocorrelation was detected and hence we did not include any autoregressive terms in the models. To test relationships between pairs of variables, we used Pearson correlations. These analyses were performed using STATISTICA software (version 11; StatSoft, Tulsa, OK).
Acknowledgments Wetlands International provided census data for France,  Belgium ,  Netherlands ,  Germany, Denmark and Sweden , and  SEO/Birdlife  for many Spanish locations. Thanks also to the  Doñana Biological Monitoring Team , especially  Luis García  and  Héctor Garrido  who conducted most of the aerial counts in the Guadalquivir marshes,  José Luis del Valle  who provided data about geese arrival, and  Miguel Ángel Rendón  who provided statistical advice.
With respect to the literature review itself, different sources were used, including PubMed and Web research. As this report is based on the current level of evidence and practice, the authors chose not to ignore unpublished data such as communications presented during congresses, especially when abstracts were available (mainly on pCLE). The group leaders suggested a first list of references, and during the vote sessions, all 26 participants had the possibility to suggest and highlight new references to be included in the report.
Acknowledgements The authors are grateful to  Dr Joshi Virendra  ( Oschner Medical Centre, Oschner, LA, USA ) and  Prof Bernard Filoche  ( Hoˆpital St Philibert, Lille, France ) for providing samples of pCLE pictures, and to  Nolwenn Gaudin  for her tremendous work in coordinating the whole process of this consensus report. The logistical support of  Mauna Kea Technologies  was very much appreciated.
A search for the bb decay of the Standard Model Higgs boson is performed with the ATLAS experiment using the full dataset recorded at the LHC in Run 1. The integrated luminosities used are 4. 7 and 20. 3 fb -1 from pp collisions at √ s = 7 and 8 TeV, respectively. The processes considered are associated (W/Z)H production, where W → eν/µν, Z → ee/µµ and Z → νν. The observed (expected) deviation from the background-only hypothesis corresponds to a significance of 1. 4 (2. 6) standard deviations and the ratio of the measured signal yield to the Standard Model expectation is found to be µ = 0. 52 ± 0. 32(stat. ) ± 0. 24(syst. ) for a Higgs boson mass of 125. 36 GeV. The analysis procedure is validated by a measurement of the yield of (W/Z)Z production with Z → bb in the same final states as for the Higgs boson search, from which the ratio of the observed signal yield to the Standard Model expectation is found to be 0.
The CDF and D0 experiments at the Tevatron reported an excess of events in their search for associated (W/Z)H production in the H → bb decay mode at a significance level of 2. 8σ for m H = 125 GeV  [16]. Recently, the CMS experiment reported an excess of events in the H → bb decay mode with a significance of 2. 1σ for m H = 125 GeV  [17]. In this paper, a search for associated (W/Z)H production of the SM Higgs boson in the bb decay mode is presented, using the full integrated luminosity accumulated by ATLAS during Run 1 of the LHC: 4. 7 and 20. 3 fb -1 from proton-proton (pp) collisions at centre-of-mass energies of 7 and 8 TeV in 2011 and 2012, respectively. An analysis of the 7 TeV dataset has already been published by ATLAS  [18]. In addition to the increase in the amount of data analysed, the update presented in this paper benefits from numerous analysis improvements.
Some of the improvements to the object reconstruction, however, are available only for the 8 TeV dataset, which leads to separate analysis strategies for the two datasets. 1 The analysis is performed for events containing zero, one, or two charged leptons (electrons or muons), targeting the Z → νν, W → ν, or Z → decay modes of the vector boson, respectively. In addition to Z → νν decays, the 0-lepton channel has a smaller but not insignificant contribution from leptonic W decays when the lepton is produced outside of the detector acceptance or not identified. A b-tagging algorithm is used to identify the jets consistent with originating from an H → bb decay. To improve the sensitivity, the three channels are each split according to the vector-boson transverse momentum, the number of jets (two or three), and the number of b-tagged jets.
Because the latter information is not available in similar detail for the 7 TeV dataset, the MVA is used only for the 8 TeV dataset. In both analyses, dedicated control samples, typically with loosened b-tagging requirements, constrain the contributions of the dominant background processes. The most significant background sources are (W/Z)+heavy-flavour-jet production and tt production. The normalisations of these backgrounds are fully determined by the likelihood fit. Other significant background sources are single-top-quark and diboson (W Z and ZZ) production, with normalisations taken from theory, as well as multijet events, normalised using multijet-enriched control samples. Since the MVA has higher expected sensitivity, it is chosen as the nominal analysis for the 8 TeV dataset to extract the final results.
Section 7 discusses the background composition in the various analysis regions, while the systematic uncertainties are addressed in section 8. The statistical procedure used to extract the results is described in section 9. For the 7 TeV data, only a dijet-mass analysis is used, and differences with respect to the 8 TeV data analysis are specified in section 10. The results are presented and discussed in section 11, and a summary of the paper is given in section 12.
Data and simulated samples The datasets used in this analysis include only pp collision data recorded in stable beam conditions and with all relevant sub-detectors providing high-quality data. The corresponding integrated luminosities are 4. 7 and 20. 3 fb -1  [20]  for the 7 and 8 TeV data, respectively. Events in the 0-lepton channel are selected by triggers based on the magnitude E miss T of the missing transverse momentum vector. The E miss T trigger configuration evolved during data taking to cope with the increasing luminosity, and the trigger efficiency was improved for the 8 TeV data. The dependence of the E miss T trigger efficiency on the E miss T reconstructed offline is measured in W → µν+jets and Z → µµ+jets events collected with single-muon triggers, with the offline E miss T calculated without the muon contribution.
W W powheg+pythia8 W Z powheg+pythia8 ZZ powheg+pythia8 Table  1. The generators used for the simulation of the signal and background processes. ( ) For the analysis of the 7 TeV data, pythia8 is used for the simulation of the gg → ZH process, and herwig for the simulation of diboson processes. brief period of data-taking in which the E miss T triggers were not available for the first bunch crossings of two bunch trains, the integrated luminosity for the 0-lepton channel in the 7 TeV dataset is reduced to 4. 6 fb -1. Events in the 1-lepton channel are primarily selected by single-lepton triggers. The E T threshold of the single-electron trigger was raised from 20 to 22 GeV during the 7 TeV data-taking period, and to 24 GeV for the 8 TeV data. The p T threshold of the single-muon trigger was similarly increased from 18 GeV for the 7 TeV data to 24 GeV at 8 TeV.
As the single-lepton triggers for the 8 TeV data include isolation criteria, triggers with higher thresholds (60 GeV for electrons and 36 GeV for muons) but no isolation requirements are used in addition. Single-lepton trigger efficiencies are measured using a tag-and-probe method applied to Z → ee and Z → µµ events. In the 1-muon sub-channel, E miss T triggers are also used to compensate for the limited muon trigger-chamber coverage in some regions of the detector. Events in the 2-lepton channel are selected by a combination of single-lepton, dielectron and dimuon triggers. The thresholds of the dilepton triggers are 12 GeV for electrons and 13 GeV for muons. Monte Carlo (MC) simulated samples are produced for signal and background processes using the atlfast-II simulation  [21] , which includes a full simulation of the ATLAS detector based on the geant4 program  [22] , except for the response of the calorimeters for which a parameterised simulation is used.
Object reconstruction In this section, the reconstruction of physics objects used in the analysis of the 8 TeV data is presented. Differences relevant for the analysis of the 7 TeV data are reported in section 10. Charged-particle tracks are reconstructed with a p T threshold of 400 MeV. The primary vertex is selected from amongst all reconstructed vertices as the one with the largest sum of associated-track squared transverse momenta Σp 2 T and is required to have at least three associated tracks. Three categories of electrons  [53, 54]  and muons  [55]  are used in the analysis, referred to as loose, medium and tight leptons in order of increasing purity. Loose leptons are selected with transverse energy E T > 7 GeV. Loose electrons are required to have |η| < 2. 47 and to fulfil the "very loose likelihood" identification criteria defined in ref.
Event selection In this section, the event selection applied in the analysis of the 8 TeV data is presented. Differences in the analysis of the 7 TeV data are reported in section 10. The analysis is optimised for a Higgs boson mass of 125 GeV. Events are first categorised according to the numbers of leptons, jets, and b-tagged jets. Events containing no loose leptons are assigned to the 0-lepton channel. Events containing one tight lepton and no additional loose leptons are assigned to the 1-lepton channel. Events containing one medium lepton and one additional loose lepton of the same flavour, and no other loose leptons, are assigned to the 2-lepton channel. In the 1-and 2-lepton channels, for at least one of the lepton triggers by which the event was selected, the objects that satisfied the trigger are required to be associated with the selected leptons.
Variables used in the multivariate analysis for the 0-, 1-and 2-lepton channels. similarly good agreement is found for the correlations between pairs of input variables, as can be seen in figure  4. The Toolkit for Multivariate Data Analysis, TMVA  [71] , is used to train the BDTs. The values for the training parameters are found by determining the configuration with the best separation between signal and background in a coarsely binned multi-dimensional training parameter space, followed by more finely grained one-dimensional scans of individual training parameters. In order to make use of the complete set of simulated MC events for the BDT training and evaluation in an unbiased way, the MC events are split into two samples of equal size, A and B. The performance of the BDTs trained on sample A (B) is evaluated with sample B (A) in order to avoid using identical events for both training and evaluation of the same BDT.
For the ∆φ(jet 1 , jet 2 ) reweighting, a systematic uncertainty amounting to half of the correction is assigned to the Zl component, while an uncertainty amounting to the full correction is assigned to the Zc+Zb components. This is done separately for 2-and 3-jet events, and all these uncertainties are treated as uncorrelated. For the p Z T reweighting, uncorrelated systematic uncertainties of half the correction are assigned to the Zl and Zc + Zb components. The notation Zc + Zb is meant to indicate that a systematic uncertainty is treated as correlated between the Zc and Zb components. The normalisation and the 3-to-2-jet ratio for the Zl background are determined from data in the 0-tag region of the 2-lepton channel, both with an uncertainty of 5%. The normalisations of the Zcl and Zbb backgrounds are left free in the global fit.
The uncertainties on the 3-to-2-jet ratios for the Zcl and Z+hf components are assessed through a comparison of alpgen with the nominal sherpa generator in the 2-tag region of the 2-lepton channel; these are 26% for Zcl and 20% for Z+hf. The same procedure is used to estimate uncertainties on the flavour fractions within Z+hf events, yielding 12% for each of bl/bb, cc/bb and bc/bb, with bl/bb uncorrelated between 2-and 3-jet samples. The shape of the m bb distribution is compared between data and simulation in the 2-tag region of the 2-lepton channel, excluding the 100-150 GeV range, from which a shape uncertainty is derived that, when it increases the dijet-mass distribution by 3% at 50 GeV, it decreases it by 5% at 200 GeV. This uncertainty is applied uncorrelated to the Zl and Zb + Zc components.
Systematic uncertainties The differences with respect to the 8 TeV data analysis arise mainly from experimental systematic uncertainties. Many of them are evaluated using independent data samples (7 TeV data vs. 8 TeV data), e. g. , E miss T trigger efficiencies or JES. Others refer to different identification algorithms, e. g. , electron identification or b-tagging. The uncertainty on the integrated luminosity is 1. 8% for the 2011 dataset  [20]. The uncertainties affecting the signal and background simulation are estimated in a similar way as for the 8 TeV data, i. e. , from comparisons between the baseline and alternative generators. For V +jets, the V bc and V bb backgrounds are merged into a single component. For dibosons, the baseline generator is herwig instead of pythia8; systematic uncertainties on the 3-to-2-jet ratios and on the p V T distributions are estimated at generator level for the different diboson processes by comparison with mcfm at NLO.
For the signal, the gg → ZH samples are generated with pythia8 instead of powheg; for all processes, the alternative generators used are pythia6 and herwig. Due to these differences, and because the phase space within which the systematic uncertainties are evaluated is more restricted than for the MVA applied to the 8 TeV data, all systematic uncertainties, except for the theoretical uncertainties on the signal, are treated as uncorrelated between the analyses of the 7 TeV and 8 TeV data in the global fit to the combined dataset, in which the MVA is used for the 8 TeV data.