dataesr/scientific-paragraphs-categorization huggingface dataset:
Each animal was attributed a type of production (dairy or beef cattle) according to its breed and classified by age (0 to 7 days, 8 days to 6 months, 6 months to 2 years, 2 to 5 years, and over 5 years). The numbers of dead and live cattle were computed per department, production type and age class by aggregating daily data to obtain weekly figures, as the date of death was not very reliable. Indeed, preliminary data analyses suggested that the date reported by farmers is more likely to correspond to the date of carcass collection rather than the actual date of death. Since no carcasses are picked up on weekends or bank holidays, there can be a difference of one to two days between the date of death and the collection date. Data were managed with the Toad for MySQL development tool.
Statistical analyses The baseline mortality rate was defined as l t = m t /N t where m t is the number of observed deaths over week t and N t the number of animal weeks at risk of dying. Nt was computed by summing the number of days each animal was present during week t then dividing this total by seven. Given this definition, one animal week can represent one animal present on seven days or seven on one day, for example. Seasonal fluctuations of this baseline mortality rate were modeled over a two-year calibration period (2004-2005 inclusive) using a time-dependent Poisson regression with an overdispersion parameter. We took overdispersion into consideration since it is frequently encountered with count data when unidentified factors or clustering are not taken into account in the model. The hierarchical structure of the data (cattle grouped into herds belonging to an administrative and territorial division) was not considered because models including random effects cannot make predictions outside the population used to calibrate the model.
Discussion While multiple studies have investigated the public health impact of the heat waves that struck France in 2003 and 2006, to our knowledge this is the first study to investigate and quantify the influence of these extreme events on cattle mortality. Routinely collected data were modeled to evaluate the impact of the 2003 and 2006 heat waves on cattle mortality in France. Predictions from the different models converged to show that the 2003 heat wave had a greater impact than the 2006 one (excess mortality of 24% and 12% respectively). This excess mortality was observed during the two weeks of the 2003 heat wave and during the following weeks in certain cattle subpopulations. In contrast, excess mortality in 2006 was found during the second and third week of the heat wave only. All cattle subpopulations suffered from the heat, but there was no marked difference in excess mortality between types of production or age classes.
Acknowledgements This work was partly supported by the  Swedish Research council FORMAS  (grant  2009-224 ), which is gratefully acknowledged. Malin Hultberg ,  Swedish University of Agricultural Sciences , is gratefully acknowledged for the rhamnolipid samples. The sampling at Grenoble Les Fresnes is maintained within a French national programme of the  Laboratoire Central de Surveillance de la Qualitede l Air (LCSQA , http://www. lcsqa. org/)for the monitoring of the impact of biomass burning in urban environment. This programme is directed by  O. Favez  ( Institut National de l'Environnement Industriel et des Risques (INERIS) ; http://www. ineris. fr/),and sampling is performed by the people from  Air Rhone-Alpes  (http://www. air-rhonealpes. fr). Both are warmly thanked for their dedicated efforts.
A case study analysis during the HyMeX campaign WALI was operated during the 2012 fall campaign of the HyMeX program (Special Observing Period 1), between 17 September and 27 October. After presenting the lidar calibration and the assessment of the different error sources, we will present a study of a typical meteorological situation using a synergy between the WVMR and aerosols lidar measurements. As shown in Fig. 11 , an intense dust aerosol event was observed from 17 to 20 October. The VDR highlights two maxima, one on 18 October and the other on 19 October. This event has been sampled to follow the time evolution of the aerosol layer. The different time periods considered are given in Table  2  with the corresponding dust layers and their main optical characteristics. The values of the WVMR are also given, showing a strong link between dust layers and significant water vapor contents.
1 and 0. 19 ± 0. 01, respectively. The corresponding dust aerosol layers are associated with a rather high WVMR of ∼ 4-6. 7 ± 0. 4 g kg -1 , which may have contributed to the intense precipitation events observed during this period over southwestern Europe. Acknowledgements. This work is a contribution to the HyMeX and ChArMEx programs, sponsored by grants MISTRALS/HyMeX and project ANR-11-BS56-0005 IODA-MED. This work was also supported by the French Space Agency (CNES) and the Commissariat à l'Energie Atomique et aux énergies alternatives (CEA). We thank D. N. Whiteman for useful information for the development of the WALI instrument. We also thank M. Sicard for his help in installing the lidar station on Menorca. Claude Camy-Peyret and François Dulac are gratefully acknowledged for reviewing this paper in its final form.
ECMWF data used in this study were obtained from the ECMWF data server. Meteo-France is gratefully acknowledged for the rawindsounding data and the output of the AROME-WMED model (10. 6096/HYMEX. AROME_WMED. 2012. 02. 20). Edited by: J. -L. Attie The publication of this article is financed by CNRS-INSU.
Acknowledgments We thank  S. Ke  ´fi for early discussions;  A. Akhmetzhanov  for advice on mathematical issues; and  M. Bryant ,  A. Duncan , and  T. Fukami  for their helpful comments on different versions of the manuscript. We also thank  J. Bronstein ,  S. R. Connolly , and two anonymous reviewers for their comments and suggestions. The simulations of the project largely benefited from the  Institut des Sciences de l'Evolution de Montpellier  computing cluster platform. I. G. and E. C. were supported by a grant from the  Ministere de l'Enseignement Superieur et de la Recherche. I. G. also benefited from the  Frontenac program  ( Fonds Quebecois de la Recherche sur la Nature et les Technologies and French consulate at Quebec ). N. M. was funded by the  Centre National de la Recherche Scientifique.
The full details of the DNA extraction, PCR amplification and DGGE analysis are provided in the Supplementary material. To analyse the matrix obtained from DGGE band profiles, the total band intensity was normalised for each sample (i. e. each band intensity was divided by the total band intensity of each sample). The relative abundance data from the DGGE matrix was then square root transformed and a similarity matrix from DGGE profiles was generated using the Bray-Curtis method. A dendrogram was produced from the similarity matrix using the group average linking method implemented in the software PRIMER v6 (PRIMER-E Ltd, Plymouth, UK). To test for significant differences between bacterial communities of the different soil fractions, and to correlate variation in bacterial communities to OC concentration and C:N ratio, ANOSIM and RELATE tests from PRIMER v6 software were performed, respectively (see Supplementary materials).
The amplicons were resolved by DGGE using 8% acrylamide gels (acrylamidebisacrylamide 40%, 37. 5:1) (Sigma-Aldrich, St. Quentin Fallavier, France) and a gradient of 45-70% denaturant  (Muyzer et al. , 1993)  in 1x TAE buffer with the Ingeny phorU system (Ingeny International, Goes, The Netherlands) at 60 °C and 50 mA-100 V for 17 h. Because of the number of samples, 2 DGGE gels were required for the analysis (Fig. S1 ). In order to compare gels and normalise gels differences, the same marker was used on both gels and PCR products of the unfractionated soil samples from four DNA extractions were used on both gels (Fig. S1 ). Hence, eight PCR products were obtained during the same PCR run from the unfractioned soil samples (i. e. 2 PCR per sample). Gels were stained with Ethidium Bromide and images were captured using Bio-capt software (Ets Vilbert Lourmat, France).
Statistical analyses The statistical analyses of the distribution of organo-mineral and POM soil fractions, C concentrations, and C:N ratios were performed using mean comparisons by paired student t-tests. The normality and homoscedasticity of data were checked prior to statistical analysis. To test any significant differences between the bacterial communities of the different soil fractions or between communities from outside vs. inside macroaggregates, one-way ANOSIM (analysis of similarity: all possible permutations were done) were performed on the DGGE similarities matrix obtained using the Bray-Curtis method. ANOSIM give the significance levels, i. e. P value, and R value, i. e. the strength of the factors on samples. R values close to 1 indicate a high separation between groups (e. g.
between soil fractions), while R values close to 0 indicate a low group separation. Correlations between the bacterial community structure and the OC concentration and C:N ratio were performed using the permutation-based test (rank correlation method: Spearman, 999 permutations) RELATE from the PRIMER software. The similarity matrix of the DGGE profiles obtained using Bray-Curtis method was correlated to the similarity matrices of OC concentration or C:N ratio obtained by Euclidean distance  (Clarke and Ainsworth, 1993). Significance levels, i. e. P values, and correlation strengths, i. e. Spearman coefficient ρ were obtained. The ρ values close to 1 indicate a strong correlation, while ρ values close to 0 indicate a weak correlation. size (Table  S3 ). All the organo-mineral soil fractions had close OC concentrations and C:N ratios with the exception of the 50-200 µm fractions (Table  S3 ).
General Procedures The melting points were measured using a Buchi Melting Point B-540 apparatus and they are uncorrected. The FT-IR spectra were recorded on Horizon MB TM FT-IR, over a 500-4000 cm -1 range, after 32 scans at a resolution of 4 cm -1. The spectra processing was carried out with the Horizon MB TM FTIR Software. The 1 H-NMR (400 MHz) and 13 C-NMR (101 MHz) spectra were obtained on a Bruker Avance 400 MHz spectrometer using tetramethylsilane as internal standard and DMSO-d6 as solvent. The chemical shifts were shown in δ values (ppm). The mass spectra were registered using a Bruker MaXis Ultra-High Resolution Quadrupole Time-of-Flight Mass Spectrometer. The progress of the reaction was monitored on TLC, using pre-coated Kieselgel 60 F254 plates (Merck, Whitehouse Station, NJ, USA) and the compounds were visualized using UV light.
The EPICA project has led to more than 250 publications; for a recent compilation that includes these EPICA publications, among other scientific outcomes, the reader is referred to the joint Climate of the Past-The Cryosphere special issue Climate of the Past (http://www. clim-past. net/special_issue55. html), an outcome of the International Partnerships in Ice Core Sciences (IPICS) First Open Science Conference held in Giens, France, on 1-5 October 2012. At that conference  Jouzel (2013)  presented a detailed overview of ice-core projects during the past 50 years. The scientific data archive for the EPICA project may be found at http://www. pangaea. de/search?q=project:epica.
The drilled depth is typically $12. 6 m (the offset between the snow surface at the start of the drilling project and the top of the casing) shallower than the logging depth, as the drill's depth counter is zeroed before starting a run, when the drill is freely hanging above the casing top. This must be borne in mind when comparing any depth reference presented here with core data that refer to the logging depth. The complete drilling protocol and other supplementary information can be found at http://doi. pangaea. de/10. 1594/PANGAEA. 841035, and is summarized in the remarks attached to the curve in Figure  1. Table  1  gives an overview of the seasonal progress of the deep drilling operation. The excursion of the penetration is discussed below in a short diary; experiences that led to a fundamental change in procedure are highlighted and discussed further below.
We then installed a pan under the winch drum to collect the liquid drawn out of the hole by the cable. The effectiveness of that system is demonstrated with the data listed in Table  2 , which compiles the liquid consumption for the different seasons and across the entire drilling operation. The two components D40 and HCFC 141b are mixed and filled into the hole. Some liquid was lost when we topped up the casing after the active drilling season to exert full compensation pressure over the winter, but the casing was not yet fully sealed. As calculated in Section 4. 5, some liquid remains in the chips after spinning and is discarded with the chips. We calculate this volume as a fraction from the weighed chips by dividing by the density. During the 2003/04 season we collected 1. 4 m 3 of liquid under the winch drum; this reduced the difference between the consumed liquid and known losses (assumed to be the result of spray or evaporation).
ACKNOWLEDGEMENTS We thank  Pavel Talalay  and  Olivier Alemany  for careful reviews and valuable comments that improved the clarity of the paper. We thank the late  Niels Gundestrup ,  Laurent Augustin ,  Heinrich Rufli  and  Jakob Schwander  for continuing support when preparing the drilling seasons. We thank the logistic staff, particularly  Jens Köhler ,  Holger Wohltmann ,  Ralf Witt ,  Günter Stoof ,  Adolf Ackermann ,  Holger Schubert ,  Andreas Brehme ,  Jochen Krischat  and  Markus Weynand , for providing a reliable camp infrastructure. We thank  Hans Oerter  for pre-site surveying of the area, coordinating the preparation and implementation of the EPICA DML drilling project and for his valuable comments that improved the paper. Marzena Kaszmarska  participated in the drill team. This work is a contribution to the European Project  for Ice Coring in Antarctica (EPICA ), a joint  European Science Foundation/European Commission scientific programme , funded by the the  European Union  ( EPICA-MIS ) and by national contributions from  Belgium, Denmark, France ,  Germany, Italy , the  Netherlands, Norway, Sweden, Switzerland  and the  United Kingdom.
Increases in free-tropospheric (FT) ozone based on ozonesonde records from the early 1990s through 2008 over two subtropical stations, Irene (near Pretoria, South Africa) and Réunion (21 • S, 55 • E; ∼ 2800 km NE of Irene in the Indian Ocean), have been reported. Over Irene a large increase in the urban-influenced boundary layer (BL, 1. 5-4 km) was also observed during the 18-year period, equivalent to 30 % decade -1. Here we show that the Irene BL trend is at least partly due to a gradual change in the sonde launch times from early morning to the midday period. The FT ozone profiles over Irene in 1990-2007 are re-examined, filling in a 1995-1999 gap with ozone profiles taken during the Measurements of Ozone by Airbus In-service Aircraft (MOZAIC) project over nearby Johannesburg. A multivariate regression model that accounts for the annual ozone cycle, El Niño-Southern Oscillation (ENSO) and possible tropopause changes was applied to monthly averaged Irene data from 4 to 11 km and to 1992-2011 Réunion sonde data from 4 to 15 km.
, 2011; Schoeberl et al. , 2007; Stajner et al. , 2008; Thompson et al. , 2012). Satellite data for the ozone precursors NO x  (van der A et al. , 2008; Lourens et al. , 2011; Zien et al. , 2014)  and CO  (Worden et al. , 2010)  are becoming available, but only for the period after 1995. These studies also focus on the Northern Hemisphere. In some cases, satellite trends are based on multiple instruments with differing algorithms and sampling characteristics and thus are highly uncertain. Data from a handful of South American megacities  (Gallardo et al. , 2012)  and rapidly growing cities in subequatorial Africa represent most of our information about the Southern Hemisphere (SH). Trends in South American and African cities are hard to establish because a variety of ozone measurement techniques have been employed, many stations are only a few years old, and calibration checks are sometimes irregular.
, 2014). Oltmans et al. (2013)  recently reported that the WMO/GAW (World Meteorological Organization/Global Atmospheric Watch) Cape Point station displayed a 15-20 % ozone increase from 1990 to 2000, followed by a period of zero-to-low growth. In order to examine possible ozone trends in the FT in the J-P region,  Clain et al. (2009)  used sonde data from the SHADOZ (Southern Hemisphere Additional Ozonesondes;  Thompson et al. , 2003 Thompson et al. , , 2012) )  station at Irene (25. 9 • S, 28. 2 • E). Clain et al. (2009)  also studied FT ozone variability and trends at Réunion (20. 8 • S, 55. 5 • E; data from 1992 to 2008), a SHADOZ station ∼ 2800 km northeast of J-P (red star in Fig. 1 ). A regular linear regression approach was employed to compare the 1990-1993 Irene record with SHADOZ-period  (Thompson et al.
Due to the strength of UT ozone increases over Irene and Réunion, we look for evidence of dynamical changes, using potential vorticity as a proxy for stratospheric influence. Similarly, assuming that growth in ozone precursors like NO x and VOCs might account for the distinct ozone increases in May-August, standard emissions databases are consulted. Section 2 describes data sources and analytical methods. Section 3 presents results and discussion. Section 4 provides a summary and conclusions.
MOZAIC data selection MOZAIC sampling with Johannesburg landing and takeoffs began in 1995 and ended in 2009; coverage after 2001 was less regular than in the 1990s, with some years skipped altogether. The general coherence of the MOZAIC Johannesburg and Irene ozone profiles up to ∼ 12 km (200 hPa) has been described by  Diab et al. (2003)  and  Clain et al. (2009). More detailed sonde-MOZAIC comparisons reported by  Thouret et al. (1998)  found that, above 400 hPa, ozonesondes exhibit higher values than the Airbus instrument. The MT and UT air sampled by the aircraft at beginning of descent and end of ascent is north of Johannesburg, largely over Zambia and Zimbabwe. Those profile segments correspond to a lower background ozone amount than over Irene, where most sondes travel over the SA Highveld and often encounter higher ozone from mid-latitude air and STE events (see, for example, a comparison of soundings over Lusaka (Zambia) and Irene in September 2000;  Thompson et al.
, 2002). Below 400 hPa, agreement between MOZAIC and Irene profiles is usually within 5 %, the stated uncertainty of the sonde instrument  (Thouret et al. , 1998). Because ozonesonde data over Irene are missing for most of the 1994-1998 period, we want to fill the gap with 4-11 km measurements from the MOZAIC record. A check for instrument bias between the Irene and MOZAIC ozone profiles appears in Fig. 5 , where the difference between the Irene ozonesonde climatology combined from 1990 to 1993 and 1999 to 2007 and mean ozone from MOZAIC in 1995-1999 is presented. Hatched areas represent times and regions where, within the standard deviation for each data set (sonde and MOZAIC), the means of the two climatologies do not overlap. As expected, most of the statistically significant differences are above 9 km.
, 1997). The model can be presented as where α is the seasonal cycle fit, β is the trend coefficient, t is time in months, γ is the regression coefficient for the time series ENSO(t), δ is the regression coefficient for the PV(t) time series, and finally (t) is the residual that is calculated by subtracting the modeled time series T (t) from the actual ozone time series T (t). For ENSO, the Southern Oscillation index (SOI, http://www. esrl. noaa. gov/psd/data/ correlation/soi. data) is used as a proxy. Monthly PV anomalies are calculated from the ECMWF 330 K PV fields over Irene (box bounded by 25. 5-32. 2 • S and 24. 8-31. 5 • E) and from the ECMWF 350 K PV data over Réunion (box within 18-25 • S and 49. 5-65. 25 • E)  (Dee et al. , 2011). Note that including a PV term in Eq. (  1 ) should partially account for any influence of a changing tropopause height  (Seidel et al.
Acknowledgements. This study was begun during a  Fulbright Scholar  grant that allowed  A. M. Thompson  to spend 8 months in South Africa during 2010-2011, with extraordinary support and hospitality from  North-West University Potchefstroom  ( D. J. J. Pienaar  and his group), the  CSIR Pretoria (V. Sivakumar) , the  University of the Witwatersrand Climatology Research Group  ( S. J. Piketh ) and co-author G. J. R. Coetzee (South African Weather Service). Helpful comments on the manuscript were received from  D. Waugh  ( Johns Hopkins Univ. ),  J. -L. Baray  ( LaMP/OPGC, France ) and from  L. D. Oman  and  S. Strode  at  NASA/GSFC. Irene sondes are made possible by the  South African Weather Service. The sonde program at Réunion is supported by  CNRS  and SHADOZ with technical assistance from  NOAA/Global Monitoring Division  (GMD;  S.
Sampling e aboveground On each sampling date, before soil core samples were collected, 20 cm Â 20 cm grids were centered over each of the sixty individual sampling points. Vegetation data and above ground biomass were collected from all grids. Above-ground biomass was harvested by cutting all plants at ground level. Biomass samples were sorted into litter (dead leaves and plant matter on the soil surface), grasses (Poaceae), legumes, forbs, bryophytes and Rhinanthus minor. The latter was separated because this species parasitizes other plants, and thus may affect the productivity of grasslands  (Stein et al. , 2009). Plants that remained rooted but had senesced were included in living plant biomass. The biomass samples were dried for 48 h at 80 C and weighed to the nearest 10 mg. From these data total above ground biomass as a measure of grassland productivity was calculated.
Statistical analyses All statistical analyses were carried out in the R environment, (R Development Core Team, 2012). Cell count data were logtransformed for all analyses to achieve homogeneity of variance. To test whether plant, microbial and abiotic variables exhibited seasonal changes, univariate, one-way ANOVAs with sampling date as a factor were calculated, followed by Tukey's HSD as post hoc test (P < 0. 05). To test whether variables exhibited spatial structure at a given date, semivariogram analyses were assessed using the gstat 2. 4. 0 Package  (Pebesma, 2004). Where non-random spatial patterns prevail, spatial structure can be measured by plotting empirical geostatistical functions (i. e. semivariogram functions). Semivariances tend to increase with distance of the sampling points until a plateau (sill) is reached, after which values fluctuate randomly about the sill.
The second approach consisted in examining how the whole community data varied as a function of spatial separation and how the community data was related to multivariate descriptors of the local environment. This was achieved using Mantel tests  (Franklin and Mills, 2009)  with the package 'vegan'  (Oksanen et al. , 2013). The Mantel test tests for the association between distance matrices. Distance or similarity matrices were calculated between all pair-wise combinations of samples for PLFA profiles, environmental conditions, plant biomass, plant species and geographic location. The distance matrices were constructed using the Euclidean distance. Euclidean distance was used because it is the metric that is usually used for PLFA and environmental data, as PLFA profiles generally have a linear response to environmental gradients.