dataesr/scientific-paragraphs-categorization huggingface dataset:
The interactions of physical activity measures with sex and age (continuous and median-based classes) were also tested. In sensitivity analyses, we repeated the analysis for the association with MVPA using a more stringent (120 mg instead of 100 mg) cut-off for accelerometry. The main analyses were performed using SAS v 9. 3 (SAS Institute, Inc, Cary, NC). Analyses based on fractional polynomial models and figures were undertaken with STATA 13 statistical software (StataCorp LP).
Sample Description Among the 4880 participants to whom the accelerometer was proposed, 210 had contraindications, 4282 agreed to wear it, and 4040 participants had valid accelerometer data (!16 hour/day) for at least 2 weekdays and 2 weekend days. Of those, 3940 participants also had data on questionnaire-assessed physical activity, BMI, waist circumference, and all covariates, constituting the main analytic sample of this study. Compared with the 940 participants not included in the analysis, the analytic sample did not differ by age [mean age ¼ 69. 3, SD ¼ 5. 7; 95% confidence interval (CI) ¼ 69. 2, 69. 5] vs 69. 1 years [SD ¼ 5. 7; 95% CI ¼ 68. 7, 69. 5), P ¼. 20] but was composed of more men (74. 2% vs 66. 8%, P <. 0001) and fewer participants from the lowest occupational position (10.
Potential reasons for these differences include sexual dimorphisms in energy metabolism  48  and the type of physical activity undertaken, with women being more likely to choose activities that help maintain their weight. 49 t least 2 previous studies have examined associations between physical activity and adiposity markers in older adults using both questionnaire and accelerometer measures. 41, 50 Our study is based on a much larger sample (previous analyses were on 238 and 636 persons) and shows a consistently stronger association of all adiposity markers with accelerometer rather than questionnaire assessedphysical activity. We used a waterproof accelerometer worn on the wrist instead of the waist or the hip, allowing us to assess physical activity during 8 full days (24 hours). Furthermore, compliance of a wrist-worn accelerometer is better than one worn on the waist or the hip,  51  thus, reducing misclassification because of missing data over nonwear periods.
Thus, the expected bias of the estimate for the physical activityadiposity association is toward the null, which may explain the weaker association with questionnaire-based assessments. 53 Physical activity will continue to be assessed by questionnaire in many largescale studies because of the ease of administration and lower costs. Increasing recognition of the importance of physical activity for health  54  suggests that studies will attempt to measure it better, making accelerometers an important research tool. Our study has several strengths including the large study sample, analysis of raw rather than "counts" data (metric generated by the monitor based on algorithm specific to each accelerometer brand),  32  and high compliance for accelerometer wear among participants. It also has some limitations.
Acknowledgments The authors thank all of the participating civil service departments and their welfare, personnel, and establishment officers; the  British Occupational Health  and Safety Agency; the  British Council of Civil Service Unions ; all participating civil servants in the Whitehall II study; and all members of the Whitehall II study team. The Whitehall II Study team comprises research scientists, statisticians, study coordinators, nurses, data managers, administrative assistants and data entry staff, who make the study possible.
Data sets and method Our study is based on the following data sets: -1-hour averages of geomagnetic indices Dst and AE; -3-hour averages of geomagnetic index Ap; -1-hour averages of the solar wind parameters (velocity V , IMF magnitude B and components B x , B y , B z in GSM coordinate frame of the IMF vector B); -time-frequency electric field spectrograms during the plasmasphere crossing. The planetary geomagnetic activity index, Ap, the stormtime disturbance index, Dst, and the auroral electrojet index, AE, are downloaded from ftp://ftp. ngdc. noaa. gov/STP/ GEOMAGNETIC_DATA/INDICES/KP_AP and http://wdc. kugi. kyoto-u. ac. jp/dstae/index. html. For more detailed information about the indices, we refer to  Prölss (2004b)  and  Verbanac et al. (2010 Verbanac et al. ( , 2011)). Among the available geomagnetic indices, these three indices have been chosen for describing the L PP as a function of geomagnetic activity because their variations can be physically interpreted and related to the specific magnetospheric current system (e.
g. , the ring current and polar electrojet). In this context, the widely used Kp index is difficult to interpret. However, it is related to the Ap index, which is more convenient to use since it is based on a linear scale. The solar wind data were obtained from the Solar Wind Electron Proton and Alpha Monitor (SWEPAM;  McComas et al. , 1998)  and the magnetometer (MAG;  Smith et al. , 1998)  on board the Advanced Composition Explorer (ACE;  Stone et al. , 1998). We used the merged hour-averaged level-2 ACE data given at http://www. srl. caltech. edu/ACE/ASC/level2/. We note that different coupling functions between the solar wind and the magnetosphere have been investigated by many authors (e. g. ,  Gonzalez et al. , 1994, and references therein). Their relative importance has often been revised (e.
The quantity d mp /dt combines different physical processes responsible for the magnetospheric activity, such as the rate at which IMF field lines are convected toward the magnetopause, the fraction of field lines impacting the magnetosphere that merge, the amount of the opened flux, the length of the merging line. Newell et al. (2007)  showed that among 20 employed coupling functions, d mp /dt represents the interaction between the solar wind and magnetosphere best for a wide variety of geomagnetic activity, even better than BsV (Bs is zero for B z >0) which is one of the most widely used coupling function, and thus is used in the present study. The analyzed L PP are determined from data provided by the WHISPER instrument on board the Cluster satellites. The Cluster mission consists of four identical spacecraft (C1, C2, C3, and C4) launched in 2000 on similar elliptical polar orbits with a time period of 57 h.
The uncertainty on the plasma frequency measurements is 163 Hz, which gives a relative error on electron density of the order of 0. 5-5 % at densities higher than 20 cm -3  (Darrouzet et al. , 2013). Before 2007, the plasmapause was sometimes never encountered by Cluster, as the perigee was located around 4 R E (i. e. , sometimes at higher radial distances than the plasma- pause). During the investigated time period  (2007) (2008) (2009) (2010) (2011) , the satellites orbit with the perigee located inside the plasmasphere, as close as 2 R E , allowed us to determine the electron density inside and outside the plasmasphere from WHISPER  (Darrouzet et al. , 2013). We have used two different and complementary data sets of plasmapause positions determined from WHISPER data, during two different time periods and two different orbitographies.
Note that we used all satellites available during this second time period if the data were available. The L values were extracted from the CLWeb software (http://clweb. cesr. fr) using the IGRF2000 model for the internal magnetic field  (IAGA, 2000)  and the Tsyganenko T89 model for the external magnetic field  (Tsyganenko, 1989). To determine the L PP , we follow  Darrouzet et al. (2013) , and we searched for the sharp increase in the electron plasma frequency, looking for a density increase of at least a factor of 3 over an L distance of 0. 5 R E or less with an increase up to a density larger than 20 cm -3. The location of the upper value of the density ramp is considered the position of the plasmapause. Note that the upper limit of the instrument is indeed low and induces limitations in our plasmapause determination technique.
Characteristics of the analyzed samples In addition to showing the minimum and maximum plasmapause extension, Table  1  contains the number of determined L PP for the two solar activity periods: the low (2007-2009) and the increasing phase (2010-2011). To determine the L PP , we processed the data collected by Cluster. Sometimes there are no data because the instrument is off, the satellite is off or because there are eclipses. Some-times, the plasmapause is crossed at too high density so that its position cannot be clearly identified. This results in the unequal data distribution through considered years. Note that most of the determined L PP come from measurements obtained in 2011. The L PP ranges between 2. 9 and 8. 8 R E. The mean L PP obtained by averaging all 311 determined L PP values amounts to 5.
However, at high L PP indicator values, we do not have sufficient data points to make a general conclusion. We plan to continue this study by enlarging our L PP data set, possibly during a period of higher solar activity. This will allow us to verify the obtained results and to more reliably constrain the lower limit of L PP for various MLT sectors. Further, the possibility to perform the analyses looking at narrow MLT sectors will enable us to more precisely identify both the MLT in which the plasmapause is formed and the displacement of the bulge during the active magnetic period. With the extension of our study, we hope to get a better insight into the physical mechanisms responsible for the plasmapause formation. This is very important since the plasmapause plays a crucial role in the propagation of the mass and energy distribution within the inner magnetosphere.
Acknowledgements. The results presented in this paper are based on data from Cluster and ACE satellites and from the  Kyoto World Data Center  for geomagnetism. We thank all the staff involved for providing high-quality data. The presented work was initiated during  G. Verbanac 's visit to the  Belgian Institute for Space Aeronomy , which was supported by the  European Union Seventh Framework Programme  (FP7/2007-2013) -COMESEP. G. Verbanac is especially thankful to N. Crosby (Project Coordinator and  Team Leader  of the  COMESEP  project) and  B. Vrsnak  ( Croatian COME-SEP Team Leader ) and to  J. De Keyser. V. Pierrard  thanks the  STCE (Solar-Terrestrial Centre of Excellence ) and the  Belgian Federal Science Policy (Belspo)  regarding the  Interuniversity Attraction Pole program , project  P7/08 CHARM.
The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w. e. ) and 150 mm, respectively, for both MOD10A1 and MYD10A1.
κ coefficients are within 0. 74 and 0. 92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97 % (κ = 0. 85) for MOD10A1 and 96 % (κ = 0. 81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96 %, κ = 0. 77; MYD10A1: 95 %, κ = 0.
, 2013). Apart from these hydrological services, the snow cover is also critical for the tourism sector in the Pyrenees. In particular, ski resorts are an important source of income and local employment  (Rived et al. , 2013). Given the importance of the snow cover in the Pyrenees, it is necessary to monitor its evolution. The snow depth is recorded daily at 19 stations at least across the whole Pyrenees mountain range. Some stations were already installed in the 1980s but most of the data are available since the 2000s. Yet, these ground measurements are essential but insufficient to describe the snow cover dynamics in the various topographic and climatic contexts of the Pyrenees mountain range. On the other hand, remote sensing data are spatially consistent and therefore can be very useful to complement in situ measurements.
Since the early 1980s, it has been shown that space-borne sensors operating in the visible and near-infrared region of the electromagnetic spectrum are very effective to map snow cover  (Bowley et al. , 1981; Dozier and Marks, 1987; Baumgartner et al. , 1987). As of today, only low-to mid-resolution sensors such as AVHRR, PROBA-V or MODIS allow for global observations of the snow cover at a daily time step (without cloud obscuration) with a spatial resolution of 1 km to 250 m. Higher-resolution snow cover maps (30 m) are typically extracted from the Landsat program images, but they provide data at a lower frequency (16 days), which is generally inappropriate to monitor the snow cover as large snow area variations can occur within a few days during the melt season  (Rango, 1993; Gómez-Landesa and Rango, 2002).
They found large errors during the snowmelt period and in forest areas and conclude that MODIS snow products still require to be used with caution. The main objectives of this study are twofold. -First, we aim to evaluate the value of the MOD10A1 snow product to generate a daily snow cover climatology in the Pyrenees for the period 2000-2013. -Second, we use this new data set to characterize the variability of the snow cover in the Pyrenees. The first objective is a necessary step even though many studies have already assessed the MODIS snow products accuracy in other mountainous regions. Indeed, continuing validation is important to make the information reliable, since the combination of topography, land cover and climate varies from one region to another  (Rittger et al. , 2013). In our case, the Pyrenees present a large physiographic variability in a rather small area and is under the influences of both Mediterranean and North Atlantic climates.
Moreover, this assessment is important because the MODIS snow products are getting more and more attention from the regional agencies in charge of the water and tourism management in the Pyrenees  (Parra et al. , 2006). In particular, an important question for the water practitioners is the effective snow detection threshold, i. e. , the value of the snow water equivalent or snow depth for which a pixel is statistically marked as snowcovered in a MODIS product. We used both in situ and remote sensing data to assess both Aqua and Terra MODIS daily snow cover binary products (snow/no snow) in the Pyrenees. For the first time, we assembled a French-Spanish data set of continuous snow measurements from the Ebro Basin agency in Spain and Météo-France. This data set was used to validate the MODIS snow products and to determine the optimal snow detection threshold.
For this study we allowed the window size to be incremented up to 9 days in order to fill a long gap from 20 March 2002 to 28 March 2002. The longest data gap lasted 17 days in summer 2001 due to a power supply failure of the MODIS instrument on board Terra (15 June 2001 to 2 July 2001). This gap was not filled because this period is not critical for snow cover monitoring. 4. The sparse remaining no-data pixels were reclassified using a classification tree (Matlab Statistics Toolbox;  Breiman et al. , 1984). For each date, the snow and nosnow pixels were used to fit a classification tree on four predicting variables derived from the geographic position and the topography. The variables were pixel elevation, aspect, easting and northing (i. e. , x and y coordinates in Lambert-93). The tree was used to predict the class of the no-data pixels for this date.
Landsat vs. MODIS products The results show that 81. 5 % of the Landsat snow-covered pixels are correctly classified in MOD10A1 (Table  4 ). For Here only the telenivometers data and the MOD10A1 product were considered. The snow depth value giving the maximum κ was extracted for each month and plotted against the month (inset). The linear fit was calculated with log-transformed values of the SD (Pearson's correlation R = 0. 90, p = 0. 002). MYD10A1 the result is similar (81. 6 %), but the number of pixels that were compared is lower and the fraction of snowcovered pixels in this sample was also lower (Sect. 2. 2). As a consequence, the performances are slightly better for MOD10A1 (Table  5 ). Figure  6  illustrates the higher dispersion of MYD10A1 with respect to Landsat snow cover area. It also shows that the snow coverage tended to be underestimated by MOD10A1.
The confusion matrices also indicate that MODIS has a lower snow detection than Landsat since the false negative rate is close to 18 % (Table  4 ). Yet, the κ coefficients indicate a good agreement for both MODIS data sets (MOD10A1 κ = 0. 85, MYD10A1 κ = 0. 81, both statistically significant at the 1 % level). There is no evident dependency between the comparison results and the observation season (Fig. 6 ), which suggests that the MODIS snow detection is not significantly deteriorated by the snow properties (e. g. , lower reflectance of ripe snow cover in late spring). For both MOD10A1 and MYD10A1, there is a lower agreement with Landsat when the comparison is made only over the forested areas (Table  5 ). However, the loss of accuracy is higher for MYD10A1. This is consistent with the previous studies (Sect.
Gap filling A virtually gap-free snow cover product with a daily time step from 1 September 2000 to 1 July 2013 was generated from MOD10A1 and MYD10A1. The gap-filling reduced the fraction of no-data pixels from 49 to 0. 38 % (Fig. 7 ). The fraction of no-data decreased by a factor of about 10-20 % at every iteration from the previous stage, except for the spatial deduction step, which had a low effect, and the classification tree, which reduced drastically the no-data fraction by 90 %. The classification tree allowed for a complete removal of the no-data values for all the dates in which some no-data pixels remained after the previous steps. The 0. 38 % missing values correspond to the 17-day gap in June 2001 for which the classification tree was not applicable. We found a substantial agreement between this gap-filled product and all in situ snow depth data.
In situ data vs. MODIS products For the validation with in situ data, it was useful to merge the Ebro Basin and Météo-France databases because it enabled expanding the range in station elevation and thus to obtain a more robust conclusion. κ coefficients are within 0. 74  and 0. 92 depending on the product and the variable, but the highest accuracy was obtained between MOD10A1 (Terra) and the SWE measurements. As expected, the agreement was a bit lower when considering MYD10A1 (Aqua). The inclusion of Météo-France measurements resulted in a significant decrease of the agreement (Table  3 ). These stations have a lower mean elevation (Table  1 ) than the Ebro Basin telenivometers; hence, the snow cover is more discontinuous or "patchy" in their vicinity. For example, three stations from the Météo-France data set are located in valley bottoms (near hydroelectricity plants); hence, their spatial representativeness at the scale of the MODIS product pixel (about 500 m) is limited.
This assumption may not be always be valid, although the snow classification method for Landsat is well established. Indeed we could also observe snow commission errors in our Landsat snow maps data set along the snow cover edges. This may artificially increase the agreement with MODIS products. However, we consider that Landsat misclassifications are less frequent than MODIS. Another possible cause for this error, which was not specifically investigated here, is the effect of the MODIS sensor view angle. This is illustrated by the image of 19 March 2009 in Fig. 9. On this day, the Pyrenees are on the edges of both input granules from the MOD10_L2 swath product (i. e. , the input data used to generate the MOD10A1), where the MODIS instrument "bow-tie" effect is the most pronounced  (Gómez-Landesa et al.
2 ), we extracted monthly energy production data in Spain from the Spanish Statistical Office database (Instituto Nacional de Estadistica, Ministerio de Industria, Energía y Turismo, 2013). The hydropower pro-duction dropped in early 2012 in comparison with the mean value over 1995-2012 (Fig. 14 ). The annual production was also lower in spite of a recovery in April-May. At the national scale the total production was higher than the 1995-2012 average (all sources of energy included), which means that the energy demand was high. Hence, it is likely that the 2012 drop in hydropower production was caused by the 2012 winter drought in the Spanish Pyrenees, which we also observed in the snow cover data (Fig. 13 ). Further analysis is necessary to establish if and how the snow deficit contributed to this drop.
Conclusions We found an overall very good agreement between the MODIS Aqua/Terra products and two independent snow cover data sets generated from in situ (stations) and remote sensing observations (Landsat). Landsat data confirmed that the snow cover edges are prone to commission (snow not detected), particularly when the sensor view angle is large. Also, the uncertainties in the final snow product increases due to the interpolation of the cloud-covered pixels, particularly in the northwestern Pyrenees and during the winter and spring months. In spite of these limitations the results of this study support the conclusion that the MODIS snow products provide valuable information on snow cover at the scale of the Pyrenees range. Using all in situ data we could determine a statistically optimal detection threshold, i.
e. , the snow depth or snow water equivalent value from which it is very likely that a pixel is classified as snow-covered in MODIS products. We found that an acceptable SWE detection threshold is between 20 and 60 mm w. e. and a SD threshold between 100 and 120 mm for both MOD10A1 and MYD10A1. We recommend to consider these ranges of values to convert the snow depth simulated by a snowpack model into snow cover area at the MODIS resolution in the Pyrenees, e. g. , for model validation or data assimilation. The MODIS snow products should be used more carefully for hydrology because it is less accurate in the transition areas where the snowmelt is fast. However, they can provide meaningful insights for climatological studies provided that the missing values are interpolated. This is particularly revealing for a transboundary mountain range like the Pyrenees where hydroclimatic observations are collected by various agencies without a joint framework or a common data depository.
Acknowledgements. This work was done as part of the  Pyrenees Climate Change Observatory (OPCC ,  POCTEFA EFA 235/11 ). The authors thank the coordinators of the  FLUXPYR  project ( POCTEFA EFA 34/08 ),  Maria-Teresa Sebastià  at  CTFC and Eric Ceschia at CESBIO  for initiating and supporting this study. This work was also partly supported by the  TOSCA program  from the  Centre National d'Etudes Spatiales. We acknowledge  ESA  and  USGS  for providing the Landsat data. This manuscript was improved considerably through the suggestions of  Juraj Parajka  and an anonymous reviewer. The first author would also like to thank  Stef Lhermitte  for the fruitful discussions about snow remote sensing on the Pino hill. Edited by:  C. De Michele.