dataesr/scientific-paragraphs-categorization huggingface dataset:
The CepiDc database The death certificate is composed of two parts: administrative and medical parts. INSEE manages the administrative part that contains the name of the dead person and information relating to civil status. CépiDc manages the non-nominative medical part which contains the medical causes of death, dates of birth and death, and cities of residence and death. From January 2008 to December 2010, CépiDc registered and coded a total of 1. 6 million deaths for the entire population of France. For 241 of the deaths, 'homelessness' (street homeless and those living in homeless shelters) was reported (source B). This type of information is coded using the International Classification of Diseases (10th revision -ICD-10) as 'Z59. 0', in the section 'Factors influencing health status and contact with health services'.
According to the World Health Organization (WHO) guidelines, medical certifiers are to report homelessness if they consider that it contributed to a person's death. Information available in both databases included age, sex, birth and death dates, city of death, birth and death places (housing, hospital, street…), and causes of death. Access to the two databases is not freely available. Overall, for each database, a specific permission is needed. This study has been approved by the French Commission for Data Protection and Liberties (Commission Nationale de l'Informatique et des Libertés: CNIL).
The capture-recapture method When two sources are considered, the total number of deaths, N, is estimated with numbers of deaths in each source NA and NB and number in common NAB  [26]  as: N ¼ N A Â N B N AB Applying the capture-recapture method requires some general validity conditions: -Independence of sources (the probability that an observation is in one of the two sources does not depend on the probability of it being in the other source): Since different actors produce the sources, the independence is plausible by construction. In the absence of more than two sources, the qualitative assessment of the dependency between the sources could not be implemented. -Adequate matching (deaths designated by a source can be matched to those reported by another source without mismatched data): Given the strict rules of anonymity of the CépiDc database, this assumption can hardly be assessed.
Identification of common cases among sources Using a capture-recapture method requires matching the records from two sources in order to identify those in common. In the absence of any direct identifier in the CépiDc database, matching could only be performed indirectly with a set of variables common to both databases: age, gender, place of death, date of death. However, the CMDR database has many missing or inaccurate records that prevent an optimal matching process. As the entire CépiDc database is exhaustive, a preliminary matching of source A and the entire CépiDc database (1. 6 million records) was performed. This matching was composed of several steps. As the day of death and age constituted necessary information to avoid duplicates, the matching sample was divided into 4 groups according to the presence or absence of these two variables.
For each sample, variables were included into the combination if the completeness rate was higher than 80%. For each death reported in the CMDR database, the algorithm searched for a correspondence in the CépiDc database. If no observation with the same combination into the two files was found, the algorithm allowed partial matches: one of the variables was sequentially removed and a new matching searched one or more observations with new combinations. In the case where several observations of CépiDc were consistent with a single death of the CMDR database, only the most informative combination of matched deaths was kept. Out of the 1145 deaths of the source A, 391 were not retrieved from the CépiDc database due to missing or inaccurate data and were excluded from subsequent calculations. Thus, the capture recapture method was applied to the source A'(N = 754), excluding the 391 deaths of the 1145 of the source A.
Finally, the total number of deaths, N, was estimated from the numbers of deaths in each source N A' and N B and from the number of deaths simultaneously present in both sources N A'B  [26]. Here, A' is the set of the 754 deaths from the CMDR database (matched to the entire CépiDc database), B is the set of the 241 homeless deaths from the CépiDc database, and A'B is the set of deaths present in both sources. The data completeness for each source was calculated as the ratio of the total number of homeless deaths in each source and the total capture-recapture estimated deaths.
Results The distributions of homeless deaths differed significantly between source A' and source B (p < 0. 0001) by region only (data not shown). The main differences were observed for the Paris region and for the North where the proportions of homeless deaths were 48% and 9%, respectively, in source A' versus 24% and 20% in source B. The overall number of common cases was then estimated to be 27 deaths (Figure  1 ). According to the capture-recapture method, the total estimated number of homeless deaths in France was 6730 (95% CI [4381-9079]) over the period 2008-2010 (Table  1 ). When the estimate was stratified by region, the total estimated number of deaths was very similar to the overall estimate (6316 deaths 95% CI [3967-8665]). Overall, the data completeness of the homeless deaths in the CMDR database was 11%.
Discussion The capture-recapture method using two sources enabled estimation that about 6000 homeless people died in France from January 2008 to December 2010, i. e. around 2000 deaths per year, with a large confidence interval. The number of homeless deaths was greatly underestimated by the two sources, CMDR and the CépiDc, taken separately. The CépiDc could not probably enhance the completeness of the collection of the homeless deaths since the only solution would be to introduce a specific location for the homeless status on the death certificate. On the contrary, in view of the missing data in the CMDR database, the CMDR has planned to develop its network to increase the completeness and standardization of its database. The estimation could be more accurate by enhancing the matching rates and therefore by improving the quality of the CMDR data.
In particular, in recent years, homeless families become the fastest growing segment of the homeless population in Paris area  [27]. Thus, estimates are only valid for the dates they are conceived for. In addition, the definition of the homeless population in the present study was not the same as for the INSEE study: it did not include people sleeping rough and not using institutional facilities, or homeless people living in towns with a population of less than 20,000. For this reason, the mortality rate of the homeless population could not be estimated in this study or compared with the international literature. Considering the small number of deaths common to both sources, the estimates of the number of deaths are imprecise and should be considered with caution. However, the low data completeness of each source is plausible, first because the information network of the CMDR is highly dependent on local initiatives and does not cover the whole of France homogeneously, and secondly because medical death certifiers rarely consider homelessness to be relevant information to be reported with the causes of death.
Some of the general conditions that validate the use of a capture-recapture method were not completely fulfilled by this study. First, the definition of homeless people was not the same for the two sources. While the homeless deaths reported in CMDR included all forms of homelessness (defined by INSEE), the homeless deaths reported in the CépiDc database are those in which the physician considered that homelessness played a role in the death. The CépiDc homeless death definition is likely to be a subcategory of the overall definition of homeless death. However, the proportion of deaths with ICD code 'Z59. 0' (homelessness) that was not retrieved in the CMDR database was unexpectedly large (89%). In addition, we supposed that homelessness plays a role in the majority of cases even if it is not the underlying cause of death.
However, a positive dependence would imply that the total number of homeless deaths was underestimated. The condition requiring perfect matching could not be met in this study. The matching algorithm was implemented in order to minimize the number of false matches by reducing the number of possible multiple matches between the CMDR and the entire database. Excluding the 391 unmatched records from the calculation is equivalent to assuming that the proportion of death certificates with the 'homeless' code in the CépiDc record was the same as that for the matched records, which seems the most reasonable option. This study represented the first part of a broader project whose objective was to describe the mortality among the homeless population in France. The next step is to describe the deaths characteristics (age, location, season and causes of deaths) and to compare to the mortality in French population, in order to implement efficient strategy, at national level, provided by policymakers and public health professionals serving this population, and not only through local initiatives.
Acknowledgments The authors would like to express their gratitude to all the volunteers of the  Collectif Les Morts de la Rue  for participating in the collection of data on homeless deaths. We also are very grateful to  Sahand Mozafari ,  James Zaki ,  Andy Mullarky  and  Ilaria Montagni  for their skilful assistance in the preparation of the English version of this manuscript. This study was supported by the  National Institute of Health and Medical Research (Inserm)  and by the  National Observatory for Poverty and Social Exclusion (ONPES).
An optical switch was used to either select the output of the OPC device (conjugated signal) or the signal which bypasses the OPC device before launching the selected data into the remaining 200-km SSMF link. The coherent receiver consists of a local oscillator (100-kHz linewidth) whose signal was combined with the received data signal in a 90° optical hybrid. Four balanced photo detectors (BPD) were connected to the hybrid outputs, and a real-time sampling scope (40-GS/s sampling rate, 20-GHz 3-dB bandwidth) was used as analog-to-digital converter (A/D). Offline processing was performed on a desktop computer including resampling, front-end correction, electronic dispersion compensation in the frequency domain, frequency-offset compensation, blind adaptive time-domain equalization using a constant-modulus and multimodulus algorithm, carrier-phase estimation by blind phase search, de-mapping and error counting.
Numerical modeling A numerical model has been implemented using VPItransmissionMaker v8. 7. The model is developed such that it fits to the experimental results. In order to do so, the back-to-back performance of the model is first fitted to the back-to-back performance of the experimental setup. The OSNR at the transmitter output was experimentally measured to be 34. 5 dB. In the model, additive Gaussian noise was added to the output of an ideal transmitter such that the same OSNR was achieved at the transmitter output. Shot noise and thermal noise at the receiver are taken into account, and then the BER vs. OSNR plots of the model and the experiment are fitted. Two independent pseudo-random binary sequences (PRBSs) are used to generate the data bits of the PDM signal in the two polarizations.
While the model did not capture the magnitude of vapor d-excess variations observed at NEEM, it showed that high dexcess events coincided with inflows of moisture originating from the Arctic (north of 70 • N). This finding demonstrated that large-scale atmospheric circulation changes drive dayto-day variations of NEEM near-surface water vapor. During clear-sky days, CRDS measurements conducted at heights from 1 to 13 m showed a strong diurnal variability in humidity and δ 18 O, interpreted to reflect the fact that the snow surface acts as a moisture source (sink) during the warming (cooling) phase. Altogether these preliminary findings have qualitatively evidenced interactions between the atmospheric water vapor and the snow surface, which has motivated further observations. Here, we report new data acquired at NEEM in summer 2011 and spring-summer 2012, which include a systematic monitoring of surface snow and water vapor, precipitation (only for 2011), and the first 17 O-excess measurements simultaneously conducted during 4 days on water vapor and surface snow.
, 1996). The estimated mean summer (JJA) temperature at NEEM is ∼ -11 ± 5 • C (1σ based on 3-hourly observations during the summers 2006-2011; the 1σ on the mean summer temperatures 2006-2011 is ∼ 1 • C), but the summer of 2012 was found to be significantly warmer (∼ -7. 5 • C) than average. The annual mean accumulation rate from 1964 to 2005 is estimated to 20 cm a -1 (water equivalent), with a large part (between a factor of 2. 5 and 4. 5) of precipitation occurring in JJA compared to DJF  (Steen-Larsen et al. , 2011). During the field campaigns of 2009-2011, a thermistor string was installed in the top snowpack between the surface and two meters depth. Extra thermistors were installed in the top snow layer throughout the season. The resistances of the thermistors were recorded using a Pico Technology 24 bit data logger.
For the 2011 campaign, each level was measured for 15 min, of which the first 5 min were discarded to rule out memory effects of the inlet tubes. For the 2012 campaign the 3 m level was measured continuously for 45 min of every hour, with the other 15 min used for measuring the 20 cm level. Values are therefore reported with an hourly resolution for the 2011 data, and 15 min resolution for 2012 data, and with similar accuracy and precision as reported by  Steen-Larsen et al. (2013)  (Table  1 ). The measurements of the drift standard with known isotopic composition used to correct the drift of the instrument are shown in the Supplement Figs. S1 and S2. We do not expect that the data gaps due to drift calibrations produce any significant effect on the analysis. When the vertical gradients of water stable isotopes were investigated in  Steen-Larsen et al.
17 O-excess of snow surface and atmospheric water vapor During the period from 11 to 14 July 2011, a specific sampling was conducted to explore day-to-day variations in 17 Oexcess of snow surface and atmospheric water vapor. Using a cryogenic trapping system similar to Steen-Larsen et al. (  2011 ), water vapor from both 1 and 10 m above the snow surface was collected with the aim of measuring 17 Oexcess. Vapor trapping was conducted over 6 h, leading to two samples per level per day. Because the results are very similar at the two heights, we only report the 10 m data here. 17 O-excess measurements were also conducted on the corresponding subset of surface snow samples. The 17 O-excess of the snow surface and atmospheric water vapor samples were measured at Laboratoire des Sciences du Climat et de l'Environnement (LSCE) using the fluorination technique  (Barkan and Luz, 2005; Landais et al.
So far, we do not have data available to constrain the nature of the process at play. For surface snow subjected to a temperature gradient of 0. 5 • C cm -1 ,  Pinzer et al. (2012)  showed using X-ray-computed tomography that the characteristic residence time for a snow crystal to stay in place before being sublimated is 2-3 days. This corresponds to a mass turnover time of ∼ 60 % of total ice mass per day  (Pinzer et al. , 2012). Based on model outputs from CRO-CUS and our in situ temperature observations, such a temperature gradient occurs in the top 5 cm of the NEEM snowpack on clear-sky days. Larger temperature gradients in the top 1-2 cm of the snowpack cannot be excluded. We speculate that wind pumping  (Clarke and Waddington, 1991; Neumann and Waddington, 2004)  can cause a continuous replacement of the interstitial water vapor in the top snow layer.
Acknowledgements. This paper is dedicated to the memory of our friend and mentor  Sigfús J. Johnsen  (1940Johnsen ( -2013) ) for being an unlimited source of inspiration and encouragement. NEEM is directed and organized by the  Centre for Ice and Climate at the Niels Bohr Institute  and  US NSF, Office of Polar Programs. It is supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO),  Canada (GSC), China (CAS) ,  Denmark (FIST), France (IPEV, INSU/CNRS  and  ANR VMC NEEM) ,  Germany (AWI) ,  Iceland (RannIs), Japan (NIPR) ,  Korea (KOPRI) , the  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland (SNF) , the  UK (NERC)  and the  USA (US NSF, Office of Polar Programs). The numerical simulations with LMDZiso were performed on the NEC-SX8 of the IDRIS/CNRS computing center. The work was supported by the  Danish Council for Independent Research -Natural Sciences  grant number  09-072689  and  10-092850 , by the  French Agence Nationale de la Recherche  (grants VMC NEEM and CEPS GREEN-LAND), and by the  AXA Research Fund.
The neutron sensitivity of the C 6 D 6 detector setup used at n TOF for capture measurements has been studied by means of detailed GEANT4 simulations. A realistic software replica of the entire n TOF experimental hall, including the neutron beam line, sample, detector supports and the walls of the experimental area has been implemented in the simulations. The simulations have been analyzed in the same manner as experimental data, in particular by applying the Pulse Height Weighting Technique. The simulations have been validated against a measurement of the neutron background performed with a nat C sample, showing an excellent agreement above 1 keV. At lower energies, an additional component in the measured nat C yield has been discovered, which prevents the use of nat C data for neutron background estimates at neutron energies below a few hundred eV.
On the contrary, this component can actually provide important information on the integral cross section of the (n, p) reaction on 12 C. The paper is organized as follows. The simulations are described in Section 2, Section 3 deals with the analysis of the output data, and Section 4 presents the simulated neutron sensitivity of the whole experimental setup based on C 6 D 6 detectors commonly used for neutron capture cross section measurements at n TOF. The energy dependence of the neutron sensitivity is analyzed, together with the origin of various components of the sensitivity. In Section 5, the Monte Carlo results are validated by comparing the results of the simulations with experimental data for a nat C sample. Finally, examples of the effect of the simulated neutron background for two different samples are discussed.
Capture G4NeutronHPCapture G4NeutronRadCapture <20 MeV >20 MeV G4NeutronHPCaptureData e G4HadronCaptureDataSet g Fission G4ParaFissionModel c G4LFission <60 MeV >60 MeV G4NeutronHPFissionData e G4HadronFissionDataSet g a Substitute: G4NeutronHPInelastic (<20 MeV) and G4NeutronHPThermalScattering (<4 eV) b Using: G4StringChipsParticleLevelInterface, G4QGSMFragmentation and G4QGSModel Substitute: G4GeneratorPrecompoundInterface, G4LundStringFragmentation and G4FTFModel c Substitute: G4NeutronHPFission (<20 MeV) d Declared range: < 4 eV e Declared range: < 20 MeV f Declared range: 3 GeV g Declared range: < 100 TeV h Substitute: G4HadronElasticDataSet i Closest substitute: G4NeutronInelasticCrossSection Additional substitute: G4HadronInelasticDataSet.
The physical models selected for a given process are listed in Table  1 , together with the selected cross section datasets. The energy range for all models was set in accordance with the declared or the recommended range. The determination of the energy range of cross sections in a database was left to internal GEANT4 procedures, since not all of them contain data for all known nuclides. GEANT4 offers a multitude of models for handling a physical process within a given energy range. Therefore, the choices made for this work are listed in Table  1  explicitly. For all processes the G4NeutronHP package offers separate highprecision models, based on a direct sampling of the detailed cross section data. In general, for neutron-induced reactions up to 20 MeV, cross sections from the evaluated ENDF/B-VII library are used  [10, 11].
In this context, inelastic and (n,charged particle) reactions represent special cases. Based on the comparison with the experimental data, in particular for the carbon sample, a combination of G4BinaryCascade and G4CascadeInterface (the so-called Bertini cascade) was selected within the entire energy range below 10 GeV. For fission processes, G4ParaFissionModel was preferred over G4HeutronHPFission because the latter does not produce fission fragments. However, it should be kept in mind that -according to the online documentation [12] -G4ParaFissionModel does not produce as much of the delayed neutrons as G4NeutronHPFission model, which is not of a concern in the present work. In Table  1  the cross section datasets used in different energy ranges are also listed. The Barashenkov-Glauber-Gribov parameterization of inelastic cross sections has been preferred over that of Laidlaw-Wellisch, as suggested in Ref.
Analysis of the simulations For consistency, the output data of the GEANT4 simulations are analyzed in the same way as the data collected during the measurements at n TOF. The first step is to assign to a given energy deposition in the C 6 D 6 detector the corresponding timeof-flight. To this end, since neutrons in the simulations are generated at the entrance of the experimental area rather than at the spallation target, the time information provided by the simulation is added to the time-of-flight calculated from the original neutron energy, assuming the known distance between the spallation target and the experimental area (182 m). This total time-of-flight allows one to later assign the background event to a reconstructed neutron energy, in the same way as in the real measurement. In the analysis of the simulations the timeof-flight was limited to 96 ms, consistent with the actual width of the acquisition window used for the flash-ADC system at n TOF.
The γ-ray cascades The reliability of the γ-ray cascades generated within GEANT4 in a neutron capture reaction is an important issue that needs to be addressed. The main question is whether the generated cascades are realistic, i. e. if they are generated according to tabulated data (instead of being sampled from an artificial continuous distribution) and if the correlations between γ-rays are correctly taken into account. A detailed investigation was performed in order to address this issue. Dedicated GEANT4 simulations were performed for the neutron irradiation of a given isotope. Several isotopes, e. g. 23 Na, 28 Si, 56 Fe, 58 Ni, were selected for the present neutron sensitivity study. The energies of the γ-rays generated in the simulations of a capture reaction were recorded immediately upon their production, before they could interact with the surrounding material.
Validation of the simulations with nat C A high purity (99. 95%) nat C sample 1 cm in thickness and 2 cm in diameter was used to estimate the neutron background during a recent measurement of the capture cross section of 58 Ni    [14]. The purity of the nat C sample was checked by chemical analysis performed at Paul Scherrer Institute, and contamination by isotopes with high cross sections at thermal neutron energy was found to be below detection limit of few µg/g. Data collected on the nat C sample were taken as a benchmark for evaluating the accuracy of the GEANT4 simulation of the neutron background. In the simulations, a neutron beam with the same spectral and spatial characteristics of the n TOF beam impinged on a software replica of the nat C sample used in the measurement. The output data of the simulations were analyzed in exactly the same manner as the real data.