dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical scores The operational evaluation was performed for each year for ozone (O 3 ) and PM. Available PM measurements include PM 10 and PM 2. 5 mass concentrations and PM 2. 5 sulfate (SO = 4 ), nitrate (NO - 3 ), and ammonium (NH + 4 ) concentrations. Although organic carbon and elemental carbon data are available at the EMEP stations for 1 yr  (Yttri et al. , 2007) , no operational evaluation was performed for these species as Po-lair3D has recently been evaluated for carbonaceous species  (Couvidat et al. , 2012). The correlation coefficient (%) and the root-mean-square error (RMSE) (µg m -3 ) are presented in Table  1  to provide a common overview of model performance for O 3 and PM. Other statistical metrics that are used routinely to evaluate model performance are presented in Table 2.
Comparison with other model evaluations in the context of AQMEII It is interesting to ensure that our model performance did not degrade when simulating a longer time period. The observational data for the AQMEII model evaluation include stations from the AirBase and EMEP databases, while we only used the latter (AirBase does not provide data for the components of PM, while EMEP does). Table  A1  summarizes the statistics obtained for PM 10 and PM 2. 5. Daily PM 10 is slightly overestimated by Polair3D over 2000-2008 (15. 9 µg m -3 measured against 16. 6 µg m -3 simulated), while the AQMEII model concentrations are on average 10 µg m -3 lower than the observations. The Polair3D average RMSE is about half the mean observed value, whereas the mean RMSE for the AQMEII models is commensurate with the observed value.
Comparison with the performance evaluation of 1 yr simulations We also compared our model performance evaluation to that of four other chemical-transport models that have been used for 1 yr simulations over Europe (see Appendix B): CHIMERE  (Péré et al. , 2010) , CALIOPE-EU  (Pay et al. , 2010) , WRF/Chem  (Tuccella et al. , 2012) , and CMAQ  (Appel et al. , 2012) , which respectively simulated  2003, 2004, 2007, and 2006. The Polair3D results are averaged over 2000-2008 for this comparison. All models simulated different years, with different models and configuration. Péré et al. (2010)  used respectively EMEP and MEGAN to generate anthropogenic and biogenic emissions. PM and trace gases (CO, VOC, NO, NO 2 ) released by the important fires in 2003 were also taken into account. Pay et al. (2010)  used the Advanced Research Weather Research and Forecasting (WRF-ARW) Model to provide meteorology, the EMEP database for anthropogenic emissions and the Dust Regional Atmospheric Model (BSC-DREAM8b) to simulate the atmospheric cycle of mineral dust.
Boundary conditions were provided by LMDz-INCA2. Appel et al. (2012)  forced WRF with boundary conditions from the North American Model from the National Centers for Environmental Prediction to generate meteorological data. Anthropogenic and biogenic emissions were respectively provided by MEGAN and TNO (http://www. tno. nl/). Fire emissions from the Moderate Resolution Imaging Spectrora diameter (MODIS) fire radiative power product were taken into account. Boundary conditions were generated via the Global and Regional Earth System Monitoring using Satellite and in situ data (GEMS) product with the help of data provided by GEOS-Chem for chemical boundary conditions. The AERO05 aerosol module and the CB05 mechanism were used. Tuccella et al. (2012)  forced WRF/Chem with meteorological initial and boundary conditions provided by ECMWF.
Local emissions from cars should not affect the measurements as there are approximatively 3 cars per day within 3 km around the station. The DE03 station is situated on a mountain in the Black Forest, at 1205 a. m. s. l. and surrounded by forests and meadows, where there is minor agricultural activity for some parts of the year. Freiburg (206 000 inhabitants) is 12 km from the station and there are approximatively 5 vehicles per day within 10 km around the station. The high altitude of the DE03 station compared to the other sites should not impact the results of the dynamic evaluation as both measured and simulated concentrations are surface data (Polair3D uses terrain-following coordinates). The IT01 station is located at 48 a. m. s. l. and is 30 km from Rome. The station has the particularity to be inside the Research Area of the National Council of Italy; therefore, the site could be influenced by the vehicles of the research personel.
There is no relevant industry near the area, but there is a highway situated 1. 7 km from the station. The proximity to a large city and a highway could lead to greater concentrations of primary PM, as well as some lesser influence on concentrations of secondary PM (i. e. , SO = 4 , NO - 3 , and NH + 4 ), but those should be taken into account by the model. The NO01 station is surrounded mainly by forest, meadows, freshwater lakes, and low-intensity agricultural areas; it is located at 190 a. m. s. l. There are some local known emissions, which have minor or negligible influence on the air quality of the site. Some local agricultural activities occasionally yield elevated ammonia concentrations. For the observation data set, we used the EMEP observations for the pollutants, the ENSEMBLES 2 observations for temperature and precipitation (horizontal resolution of 0.
The values of the best correlation coefficients are also close in both simulation and observation within 30 % at 17 out of 23 stations, for example, at CH02 (around -0. 33), DE44 (around -0. 23), ES11 (around -0. 23), and IT01 (around -0. 25). The differences between the values of the regression coefficients in simulated and observed data mainly come from a difference between the values of the correlation for lag = 0 and 1 (at ES10 for example). At other stations the profile is the same between observation and simulation, but with a slight constant difference (at NO01 for example). The evolution of the correlation between temperature and PM 2. 5 concentrations is not as well reproduced by the model. The results are best at the AT02, ES10, ES14, GB36, IT01, and SI08 stations, where the curves are nearly identical.
At these stations the regression coefficients are either -0. 02 or -0. 03 and are nearly the same in simulation and observation. The best observed correlation is around -0. 45 at AT02, +0. 15 at ES10, -0. 07 at ES14, -0. 12 at GB36, -0. 18 at IT01, and around -0. 10 at SI08 for both simulation and observation. Other stations present similar regressions, but with a constant difference between the values of the correlation between the observed and the simulated data (AT48, DE02, DE03, DE44, ES07, ES08, ES09, ES12, and GB48). For some other stations, the correlation is not well reproduced by the model (CH02, DE04, ES11, ES13, ES15, IT04, and NO01). For these stations the best correlation is of opposite sign in the simulation compared to the observation. The evolution of the correlation between wind speed and PM 2.
5 , sulfate, nitrate, and ammonium), ranging up to 10 days after at AT02  (2003) (2004) (2005) (2006) (2007) (2008). Simulated data are represented with a star, while observational data are represented with a dot. meteorology is well represented. As for AT02, the model reproduces well the evolution of PM concentrations as a function of changes in precipitation. Similarly, the observations show correlations between PM and wind speed that have similar profiles but are greater than those of the simulation. The evolution of PM 2. 5 as a function of changes in temperature is not as well represented by the model (-0. 10 for the simulation against -0. 25 for the observations) as for AT02 because of the difficulty of the model to correctly represent changes in NO - 3 and NH + 4 (correlations of -0.
10 for SO = 4 , NO - 3 , NH + 4 , and organic matter, -0. 20 for sea salt, and -0. 30 for black carbon), except mineral dust (around 0. 15). As for AT02, profiles for wind speed and precipitation are identical for all species, except for sea salt. Fig. 5 : Correlations between meteorology (temperature in red, precipitation in blue, wind speed in green) on a given day and pollutant concentrations (PM 2. 5 , sulfate, nitrate and ammonium), ranging up to 10 days after at  DE02 (2006 DE02 ( -2008)). Simulated data are represented with a star, while observational data are represented with a dot. 8 Fig. 5. Correlations between meteorology (temperature in red, precipitation in blue, wind speed in green) on a given day and pollutant concentrations (PM 2. 5 , sulfate, nitrate, and ammonium), ranging up to 10 days after at  DE02 (2006 DE02 ( -2008)).
Simulated data are represented with a star, while observational data are represented with a dot. The AT02 and DE02 stations show negative correlations between temperature and SO = 4. SO = 4 concentrations depend on 2 emissions (shown in Fig. 6 ), which may vary by season, and on the conversion rate of SO 2 to SO = 4 , which is greater in summer when oxidant concentrations are greater and kinetics faster. SO 2 emissions are greater over Poland in winter than in summer (i. e. , when temperatures are low, which suggests that SO = 4 concentrations at these two stations are governed more by SO 2 emissions than by the kinetics of SO 2 to SO = 4 conversion). A first reason comforting this hypothesis is that the SO 2 emission impact is more visible at the AT02 station (correlation of around -0. 3), which is closer to the SO 2 emission sources, than at the DE02 station (correlation of around -0.
The correlation between SO = 4 and temperature is positive as for DE03 and therefore shows a relationship opposite to that obtained at AT02 and DE02. The model reproduces this correlation perfectly. The model overestimates the strength of the anticorrelation between wind speed and NH + 4 or NO - 3. Simulation data show that the negative correlation between temperature and PM 2. 5 is driven by NH + 4 (-0. 2), NO - 3 (-0. 35), black carbon (-0. 5), sea salt (-0. 15), and organic matter (-0. 2). Correlations between NO - 3 /NH + 4 and wind speed present large differences between the observations and the simulation at the AT02, DE02, DE03, and IT01 stations. Simulated NO - 3 is overestimated, which is a recurring issue in PM modeling over Europe. Moreover, there is less NO - 3 in summer than in winter in the simulation, and significantly less at some stations (-63 % AT02, -20 % at DE02, -14 % at DE03, and -56 % at IT01, see Table  4 ).
These different responses are correctly reproduced by the model. The correlation profiles for observed data at the NO01 station differ from those at the other stations; the model also reproduces these differences correctly. This dynamic evaluation is limited by the amount of data on PM 2. 5 and its composition over large periods at European stations. For example, the lack of PM 2. 5 chemical composition data in the western part of the domain (i. e. , the countries near the Atlantic Ocean: Ireland, France, the United Kingdom, Spain, and Portugal) is a limitation of this study. The results of studies on the impact of meteorology/climate change on PM 2. 5 compositions on that westernmost part of Europe should therefore be treated with caution. Analyses of correlation between observed temperature and NO - 3 concentrations in the US have shown results that differed from modeled responses of NO - 3 to temperature  (Tai et al.
, 1986)  in order to assess realistic variations in the pK CR2 of the second dissociation of cresol red at 25 °C (Table  1 ). In soil, dissociations of acid and alkaline substrates were taken into account: reactions considered for substrates containing more than one acid or alkaline functional group were chosen based on their pK and the pH of the soil (Table  2 ). For calcareous soils, the solubility constant K S for calcite was set to 8. 3, mirroring data reported by  Stumm and Morgan (1996)  at 25 °C. The simulated increase of [Ca 2+ ] during incubations makes it possible to assess the release of abiotic CO 2. Geochemical simulations were performed on soils at various CO 2 air fractions for different substrates and amounts of substrates. pH was adjusted so as to cancel the sum of charges of ions in solution.
Acknowledgments This work received support from the  CleanWast  Project that features as part of the  French ANR [National Research Agency] PRECODD Programme. We thank the  INRA's Département Environnement et Agronomie  and the  ADEME  for providing support through  M. Ben-Sassi 's PhD grant. We also thank  Ghislain Sévenier  and  Cécile Péru  for valuable technical assistance, and  Glen McCulley  for English editing and proofreading.
In order to construct a chronology for the gas phase, the ice age-gas age difference ( age) has been reconstructed using a coupled firn densification-heat diffusion model. Temperature and accumulation inputs to the age model, initially derived from the water isotope proxies, have been adjusted to optimize the fit to timing constraints from δ 15 N of nitrogen and high-resolution methane data during the abrupt onset of Greenland interstadials. The ice and gas chronologies and the corresponding thinning function represent the first chronology for the NEEM core, named GICC05modelext-NEEM-1. Based on both the flow and firn modelling results, the accumulation history for the NEEM site has been reconstructed. Together, the timescale and accumulation reconstruction provide the necessary basis for further analysis of the records from NEEM.
S. O. Rasmussen et al. : A first chronology for the NEEM ice core The present-day annual snow accumulation at the NEEM site is 0. 22 m ice equivalent and, therefore, the core has annual layering similar to the GRIP and GISP2 ice cores. Once high-resolution measurements from the NEEM core become available, annual layer identification in the NEEM core may be undertaken either by the manual identification of annual layers as performed on data from the DYE-3, GRIP, and NGRIP cores in the construction of the Greenland Ice Core Chronology 2005, hereafter GICC05,  (Vinther et al. , 2006; Rasmussen et al. , 2006) , or by automated methods  (Winstrup et al. , 2012). At this point, however, data for annual layer identification are not available. Thus, to provide a first timescale for the NEEM core, the GICC05 timescale and its model extension GICC05modelext is adopted as is from the NGRIP core using linear interpolation between common reference horizons, or match points, as identified from the electrical conductivity measurement (ECM), dielectrical profiling (DEP), and tephra records of the two cores.
9 ka, the robustness of the annual layer count is adversely influenced by the fact that the GRIP CFA set consists of only two independent parameters at moderate resolution. The section from about 10. 3 ka b2k (deeper than which NGRIP CFA measurements are available) to 7. 5 ka b2k is the weakest section data-wise, and although this time period is partially overlapping with the brittle zone in the NEEM core, there is potential for a strengthening of the annual layer identification across this interval once NEEM data are available. Another potential addition to the GICC framework based on NEEM data is semi-automatic and objective annual layer identification using VS and CFA data, possibly in parallel between the NEEM and NGRIP cores, for example, using the method developed by  Winstrup et al.
Steady movement of the ECM instrument over the ice surface prevents the ice from being polarized leading to a reduced current over time  (Hammer, 1980). Based on comparisons to high-resolution chemistry measurements, it has been established that the recorded ECM signal is almost entirely a response to the acidic components of the ice, even in the presence of large concentrations of neutral salt  (Moore et al. , 1992). Hence, ECM is a fast and non-destructive technique for providing a high-resolution acidity profile for the ice core. The ECM technique is often used as one of the primary tools for establishing an event stratigraphy for volcanic eruptions, which are detectable as high-acidity layers in ice cores. ECM was recorded continuously along the NGRIP and NEEM ice cores. For NGRIP, the upper part (down to 1372 m) of the ECM profile was measured on the NGRIP1 core, whereas the lower part (1346-3085 m) is based on data from the NGRIP2 core.
A total of 383 match points were identified between the NEEM and NGRIP1 cores with an additional 423 match points tying together the NEEM and NGRIP2 cores (of which 19 are in the zone of overlap between NGRIP1 and NGRIP2 measurements, leading to a total of 787 match points). The match points are provided in the data file accompanying this paper and a typical sequence of ECM and DEP data with match points is shown on Fig. 3. During both phases of match-point identification, the Matchmaker tool is used to continuously evaluate whether the proposed match is glaciologically realistic (i. e. compatible with reasonable assumptions about the flow and accumulation regimes of the two core sites). As outlined in  Rasmussen et al. (2008) , the most useful diagnostic variable is the ratio r ab = (D a -D b ) / (d ad b ) of annual layer thicknesses between neighbouring match points a and b found at depths D a and D b in the NEEM core and d a and d b in the NGRIP core.
5-2 during most of the glacial. The (NGRIP depth, NEEM depth) relation has a small kink at the 2087. 698 m match point and clear kinks at the 2166. 449 m and 2195. 630 m match points. The first two kinks probably represent the boundaries between sections that have undergone different amounts of strain due to complex ice flow patterns that lead to overturned folds deeper in the ice (NEEM community members, 2013), but we believe that the ice stratigraphy is still undisturbed. Below the 2195. 630 m match point, the match between the cores is less robust, and there is a significant risk that the ice below this depth is stratigraphically disturbed. However, we do not have enough data of significant resolution at this point to shed light on this issue, and we therefore transfer GICC05modelext to NEEM down to a depth of 2203.
The relation between tephra horizons and the ECM synchronization As described in Sect. 2. 4 and Table  1 , fifteen tephra horizons common to the NGRIP and NEEM cores have been located. All of these are fully consistent with the ECM-based synchronization. The tephra particles are found in ice samples with a typical length of 15-20 cm, but their precise locations have not been determined by higher-resolution sampling and the tephra layers are not visible. In contrast, the ECM record has a much finer depth resolution and precision. More than half of the tephra horizons sit on top of (or very close to) one or more ECM-based match points, in which case only the more precise ECM-based match points are used for the timescale transfer. Five tephra horizons are located in areas without adjacent ECM-based match points and are included in the  timescale transfer data set using the middle of the tephra sampling intervals (see Table  1  for details).
Transfer of the GICC05modelext timescale from NGRIP to NEEM The transfer of the timescale is based on the ECM and DEP-based match points and the five tephra horizons located away from ECM-based match points. Using the match points' NGRIP depths and the NGRIP GICC05modelext (depth, age) relationship, the ages of the 787 ECM-based match points and 5 tephras are calculated. This set of mark-ers defines the (depth, age) relation for NEEM, named GICC05modelext-NEEM-1. The accuracy of the timescale at these points depends on three factors: -The NEEM timescale inherits the maximum counting error (MCE) of the NGRIP GICC05 timescale. -Differences between the shape of peaks and inaccuracies in the depth registration of the ECM data set introduce synchronization uncertainty on the order of centimetres.
Acknowledgements. We thank the many individuals and organisations involved in logistics, drill development, drilling, as well as ice-core processing and analysis in the field and in our laboratories. NEEM is directed and organized by the  Centre of 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 (NRCan/GSC), China (CAS) ,  Denmark (FI) ,  France (IPEV, CNRS/INSU, CEA and ANR), Germany (AWI), Iceland (RannIs), Japan (NIPR) ,  South Korea (KOPRI) , The  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland (SNF) , the  United Kingdom (NERC)  and the  USA (US NSF, Office of Polar Programs)  and the  EU Seventh Framework programmes Past4Future  ( FP7/2007-2013  grant agreement no.
I think we need to contrast the surface diversity of language substitution with content-driven diversity involving language difference that is facilitated by particular kinds of technologies. In this context, for example, Skype which enables a transnational linguistic community to be constituted seems to me to be a lot more significant than say translating iPhone instructions into whatever languages are spoken by iPhone customers. In the past when Irish emigrants went to the United States the party organised for them was called an American Wake and the chances were that people would never actually see them again. Not only that, but the contact they had with people back home, was also quite limited. So for many Irish speakers when they went to the United States the ability to speak the language disappeared.
However, data on the age of peak performance for Ironman triathletes are lacking. Gender differences in performance received considerable attention in recent decades. Several studies analyzed the differences in performance between the sexes in different disciplines, mainly running over different distances  [5, 11, 12]. Over the past four decades, female participation in endurance events increased considerably, and during the same period, their endurance performances improved substantially  [6, 13]. These improvement rates exceeded those of men, leading to the hypothesis that gender difference would disappear in the future  [14]. Moreover, it has been suggested that this difference should disappear in running as the distance increases, particularly beyond the marathon distance, because of advantages in fuel utilization and a greater fatigue resistance in women compared to men  [15].
Longitudinal data analysis From Ironman Switzerland, an official qualifier for Ironman Hawaii, the age of the annual top ten women and men was determined between 1995 and 2010. The data set from this study was obtained from the race website  [20]. Ironman Switzerland is an important Ironman event in Europe because of the numerous qualifying places for the Ironman World Championship in Hawaii, the high participation rates, and its long history. It is the third oldest Ironman in Europe behind Ironman Europe (Germany) and Ironman Lanzarote (Spain). Since 1995, the event has been held in July each year in the city of Zurich. For the first 2 years, it was called Euroman and changed to Ironman Switzerland in 1997. Course records are 8 h 12 min for men (Oliver Bernhard in 2000) and 9 h 00 min for women (Karin Thürig in 2010).
Cross-sectional data analysis A cross-sectional analysis of the age of peak performance was performed for all Ironman triathlons held in 2010. In 2010, a total of 22 Ironman triathlons, including 21 qualifier races and the Ironman World Championship, the Ironman Hawaii, were held. Complete data from the Ironman World Series 2010 were available for Ironman Hawaii and for all qualifier races held in 2010, with the exception of Memorial Hermann Ironman Texas, Ironman Wales, and Ford Ironman Cozumel  [21]. From these three races, the complete rankings were not publicly available online, and the race directors were not able to provide us the data needed for analyses. Therefore, from 19 qualifiers and Ironman Hawaii, the exact ages of the top ten women and men were available. Since no information about age of the athletes was given for 2 out of the 21 qualifier races, these two races were excluded from analysis.