dataesr/scientific-paragraphs-categorization huggingface dataset:
Although our study requires replication, our observed effect together with the high prevalence of physical inactivity (fewer than 7% of the ALSPAC children achieved the WHO recommendation) may result in a considerable impact on lung function at the population level. 39 urthermore, given the tracking of physical activity over time, 2 the promotion of physical activity in childhood could help better respiratory health in adult life. 40, 41 his study had several strengths. It was carried out in a large population, which limits random variation in the findings. We used data from a prospective cohort, which allowed the assessment of temporality between physical activity and lung function. Moreover, we minimized potential reverse causation by including the levels of lung function at 8 years (measured prior to the first physical-activity assessment) as a covariate.
Acknowledgements We are grateful to all the families who took part in this study, the midwives for their help in recruiting them and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. We thank  Amanda Hill  for acting as our ALSPAC data buddy. ISGlobal is a member of the  CERCA Programme, Generalitat de Catalunya.
Funding The present analyses are part of the Ageing Lungs in European Cohorts (ALEC) Study (www. alecstudy. org), which has received funding from the European Union's Horizon 2020 research and innovation programme (grant agreement no. 633212). The UK Medical Research Council, the Wellcome Trust (grant: 102215/2/ 13/2) and the University of Bristol provide core support for the Avon Longitudinal Study of Parents and Children (ALSPAC). Elaine Fuertes is supported by a Marie Skłodowska-Curie Individual Fellowship (H2020-MSCA-IF-2015; proposal number 704268). Celina Roda is the recipient of a European Respiratory Society Fellowship (RESPIRE3-201703-00127, under H2020-Marie Skłodowska-Curie actions COFUND). These funders did not have any role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.
Acknowledgements The authors acknowledge Spencer Brennan,  Juan-Carlos Rojas-Sanchez , and  Mohammed Salah El Hadri  for assisting device fabrication and measurements. The authors thank  Ulrich Nowak ,  Joerg Wunderlich ,  Amalio Fernández-Pacheco ,  Alex Welbourne , and  Dedalo Sanz-Hernandez  for useful discussions. J. -W. L. acknowledges the support from the  Newton International Fellowship scheme NF150217. U. A. acknowledges financial support by the  Deutsche Forschungsgemeinschaft  through  SFB/TRR 227 "Ultrafast Spin Dynamics ," Project  A08. L. O'B. acknowledges support from the  UK EPSRC  (Grant No. EP/P005713/1 ). P. V. , M. H. , G. M. , and S. M. were supported by the  Institut Carnot ICEEL  for the project " Optic-switch " and Matelas and by the  French PIA  project " Lorraine Université d'Excellence," (Ref.
No. ANR-151DEX-04-LUE ). Experiments were performed using equipment funded by  FEDER (EU) ,  ANR ,  Région Grand Est , and  Métropole Grand Nancy. The work by V. R. and E. M. was supported by Project No. MAT2017-87072-C4-1-P  from the  (Ministerio de Economia y Competitividad) Spanish government  and Project No. SA299P18  from  Consejeria de Educacion Junta de Castilla y Leon.
Apart from the detailed cloud microphysical structure, which provides key information for evaluating the impact of clouds on climate, information on whether a cloud is present with good temporal resolution is already valuable for an atmospheric observation station. For example, this information can be useful for: - The validation of cloud sensors aboard satellite missions; - The organization of ongoing or future field measurement campaigns. The statistical analysis of cloud presence depending on the days and seasons is valuable information for choosing a suitable date for a field campaign focused on cloud studies or, on the contrary, to avoid the presence of clouds for field campaigns that require clear sky conditions; - The constitution of sets of metadata to analyze other observations (gas, aerosol, etc.
These cameras capture 600 × 800 pixel images every ten minutes in an uncompressed JPEG format. More technical details on the cameras are available on the Axis manufacturer's website  [16]. The images taken by the Cézeaux and PUY cameras are available in real time on the website of the Observatoire de Physique du Globe de Clermont-Ferrand  [17]. The percentage of available network camera images related to the maximum number of images theoretically available varied from 72% to 100%, except for May 2018 (55%, Table  1 ). Images were missed due to problems while writing the images, data transmission, electrical shutdown of the station, or accumulation of snow in front of the camera during winter. In addition, an all-sky camera has been in operation at Cézeaux since December 2015. The all-sky camera is an EKO SRF-02, a fully automatic imaging system that captures 1024 × 768 pixel JPEG images of the total sky at different exposures every 2 min.
When the sky images are captured, a real time cloud fraction (CF) is automatically calculated by the acquisition software, which offers great flexibility and functions. For example, the software is able to define the area of interest by horizon masking. A more optimized CF evaluation was also performed by the software ELIFAN  [18] , which is used in this study. The SRF-02 is connected to a computer network through a standard Ethernet interface. The all-sky camera is equipped with an air pump and drying cartridge. In this paper, the methodology is based on camera images from the PUY camera, and PVM Gerber has been used for validation. In this work, 524 days of operation of the all-sky cameras have been used from December 2015 to December 2018.
CATS (Cloud-Aerosol Transport System) The NASA cloud-aerosol transport system (CATS) is a lidar onboard the International Space Station (ISS) that provides vertical profiles of atmospheric aerosols and clouds  [21]. CATS was in operation from February 2015 to October 2017. The CF parameter is deduced from the calibrated vertical profiles of the backscattered signals at 1064 nm  [22] , with an algorithm separating low level clouds and aerosols similar to the cloud-aerosol lidar and infrared pathfinder satellite observation (CALIPSO) algorithm  [23]. More details on the CATS inversion algorithms and data can be found in the CATS data product catalog  [24]. In the present work, we have used cloud detections from the CATS level 2 operation cloud layer product (L2O-CLay). This product includes cloud layers detected horizontally and averaging between 1 to 60 km, reported every 5 km along-track.
Methodology of Network Camera Image Processing The camera produces images in the joint photographic experts group (JPEG) format. A JPEG image is a matrix of pixels ranging from the value 0 (for low light) to 255 (for high brightness) in 3 channels for red green and blue colors. It is possible to access and manipulate the digital accounts of the images' pixels with scientific software. We used the commercial MATLAB software environment [30], but the code can easily be adapted for open access software, such as Python  [31]. The methodology is based on the detection of contrast in specific areas of the image. The detection of contrast is based on the standard deviation of the pixel values on an area of the image. When the camera is in a cloud, the image has little contrast, and the standard deviation value is low.
We also had to remove incorrect images with rain drops or snow on the lens of the camera. Figure  2  shows some examples of these images and MATLAB processing in cloudy, sea of clouds, and clear sky conditions. Finally, it is necessary to define a threshold value for the standard deviation, which separates cloudy and clear sky situations. Figure  3  presents a sensitivity analysis applied to the total set of images (2013-2018). The maximum agreement is obtained with a threshold value of 6 for the two zones (Clermont and Livradois-Forez). Agreement is obtained by dividing the number of cases where the two systems (camera and PVM) obtain the same result (cloudy or clear sky) by the total number of coincidences (the time difference between the image taking and the PVM measurement is less than 2 min).
CATS Figure  6  shows the camera COF averaged over the CATS measurement periods (2015-2017), compared to the CF obtained from CATS. The lower limit of the camera cloud occurrence (black curve) is obtained with a threshold value of 5. 5, and the upper limit is obtained by adding the cases classified as "uncertain" (with a threshold value between 5. 5 and 6. 5). We found that the closest CATS CF values to the camera COF are those of the 1-2 km layer (green curve), which is consistent with the altitude of the PUY roughly in the middle of this altitude range. The results are presented by separating the database into two seasons, summer for the months of June to August (Figure  6a ) and winter for the months of December to February (Figure  6b ). The camera CF is of the same order of magnitude for both types of data, but CATS shows a larger diurnal variation, with CF values larger than the camera cloud occurrence values from 3 to 9 UT during winter and the opposite during the rest of the day.
ECMWF ERA-5 We have also compared the COF deduced from the camera analysis to the fraction of the cloud cover parameter of the ERA-5 dataset recently provided by ECMWF, interpolated on the closest point to PUY at a 850 hPa pressure level. Figure  9  shows the time series of the monthly averages of these two parameters. The two time series follow highly correlated variations (correlation coefficient 0. 94) but with a mean shift of 19% (up to 40% for some winter months). This result suggests that the local cloud cover over a mountain site is probably underestimated by the ECMWF ERA-5 reanalysis. However, the monthly average plus one standard deviation is very close to the COF camera curve.
The boundary between East and West Antarctica is the transect between the Ross and Filchner-Ronne ice shelves. We excluded ice shelves because they do not contribute to sea-level rise. We smoothed the shape of the domain (red line in Fig. 1 ) to avoid making the direction normal to the boundary too dependant on the local shape of the coastline (black line). The largest gaps between the stations are at the boundaries between the Ross and Filchner-Ronne ice shelves and around Dumont d'Urville. Radiosondes can measure moisture advection directly as it is the product of humidity with the horizontal wind vector. The Integrated Global Radiosonde Archive (IGRA) version 2 provided the sounding data  (Durre et al. , 2006). IGRA stores quality-controlled soundings from more than 2700 stations worldwide dating as far back as 1905.
We applied additional climatological checks to the wind and specific humidity variables, similarly to  Dufour et al. (2016). Based on monthly distributions, they exclude values further than 3 times the interquartile range from the median. Due to the persistence of unrealistic values, we removed the data from Syowa above 400 hPa. The hygrometers on board radiosondes suffer from known biases some of which can be compensated retrospectively  (Miloshevich et al. , 2004). For instance, at low temperatures,  the hygrometer can no longer adjust to sharp variations in humidity, leading to the so-called time-lag bias. Moreover, the default calibration model of the sensor is inaccurate below -20 • C (temperature-dependence error). Unfortunately, the suggested corrections apply only to specific radiosonde models and require metadata and high-resolution vertical data absent from IGRA.
(2006). West Antarctica and the peninsula are more exposed to moisture fluxes, and indeed the convergence is higher for the whole continent. The ranking between reanalyses remains the same. The interannual variability in reanalyses is doubtful due to the discontinuities introduced by changes in the observation system  (Bengtsson et al. , 2004). This is especially the case in Antarctica, where analyses rely heavily on satellite data as opposed to conventional observations  (Nicolas and Bromwich, 2011). The changes in the components of the moisture budget over time are summarized as linear trends in Fig. 4b. Generally speaking, the trends are weak and inconsistent. Precipitable water has not changed significantly in the modern reanalyses, yet it is the purported mechanism for the future increase in moisture fluxes.
The values for South Pole Station are shown at the abscissa of the closest point on the domain boundary; the direction of fluxes normal to the boundary is also defined at this point. At the South Pole, the difference between profiles and dots is also a consequence of the distance to the boundary. Otherwise, the subsampled reanalyses are quite representative of the year-round values, including the summer-only data at Mario Zucchelli Station, surprisingly. This is likely a coincidence: the annual cycle of moisture convergence experiences a two-fold increase during the austral winter (Fig. 6a ). In the Southern Hemisphere, the lower winter humidity is more than compensated by the stronger storm activity  (Oshima and Yamazaki, 2006). NCEP DOE R2 has a much weaker convergence cycle perhaps due to the unrealistic evaporation in summer.
When the reanalyses are interpolated on the stations and masked in time to match the radiosonde launches, the annual cycle is quite different (Fig. 6b ). The transition from summer to winter is no longer as sharp in most data sets and it is altogether absent from NCEP DOE R2 and IGRA. In the vertical, the moisture flux profiles demonstrate two different regimes (Fig. 7a ). In most data sets, including observations, the transport changes polarity around 900 hPa: into the continent above and away from it below. The level of the transition is higher in ERA-Interim (850 hPa) and higher still in NCEP DOE R2 (750-700 hPa). The 1000 hPa level in NCEP DOE R2 was too often below the ground to be shown. The surface fluxes are weaker above stations (solid lines) than in the continental average (dashed lines).
The inter-data-set spread also increases near the surface in the case of humidity (Fig. 7b ). The IGRA observations exhibit a shallow inversion that is also present in the modern reanalyses but deeper and stronger. There is practically no mean wind above 800 hPa, but below the prevailing winds blow out from the ice sheet. This is the signature of katabatic flows. There is some difference between the original reanalyses and when they are both sampled in time and interpolated in space. We suggest that the stations are not representative of their surroundings because they were generally built in places sheltered from the winds. Only the highresolution data sets can represent both the katabatic outflow and the relative haven of the station. What is more, the radiosonde launch is more likely to be cancelled or the balloon may be lost during a strong katabatic event, which biases the observations against these conditions.
Interannual variability Time series from reanalyses reflect modifications to the observation system in addition to changes in the climate. The radiosonde record also has its homogeneity issues, e. g. when instruments are upgraded. These events are recorded in the IGRA metadata and we found no association with discontinuities in the times series. We will therefore interpret sudden divergences between observations and reanalyses as artefacts of the data assimilation. From Fig. 9 , the most conspicuous ones are in NCEP DOE R2, followed by JRA 55, but they do not take the form of the shifts we would expect with, say, the introduction of a new satellite. Rather, anomalies are amplified intermittently compared to observations or one reanalysis departs for several years from the ensemble. At Amundsen Scott Station (South Pole), observations exhibit variations in higher amplitude than the reanalyses.
The extreme climate in our only inland station must have been a test to both measurements and reanalyses. We now examine the long-term evolution of moisture transport in terms of linear trends, this time analysed along the horizontal dimension (Fig. 10 ). Most longitudes show no statistically significant change in the advection. Among those that do, even fewer display an agreement between data sets. Mirny Station and its surroundings to the east have seen less moisture travelling inland according to the observations, NCEP CFSR and MERRA 2. The other reanalyses agree in polarity but not in significance. There has been a similar decrease over the Amery Ice Shelf between Davis and Mawson stations. The radiosondes and the interpolated reanalyses at Mawson Station present an opposite trend, which is greater in magnitude than the uninterpolated reanalyses, indicating a sampling bias.
The decreasing accumulation in Victoria Land from  Thomas et al. (2017)  is not apparent in the time series of McMurdo and Mario Zucchelli stations, presumably because the ice core sites were located further north. On the scale of the ice sheet, we know from Fig. 4  that the transport series from reanalyses will be offset by up to 30 mm yr -1 and that they will present contradictory trends. In Fig. 11a , ERA-Interim, JRA 55 and MERRA 2 are grouped together, especially after 2000. NCEP DOE R2 is once again the outlier. It is the only data set to not peak   When we restrict the reanalyses to the locations and times of radiosonde launches, they display a more consistent behaviour (Fig. 11b ): a downward trend in the 1980s and an upward trend in the two following decades. Observations follow the same pattern with comparatively higher fluxes in the 1980s and 2000s.
Conclusions We have compared moisture transport to Antarctica in five reanalyses and over the 1980-2016 period. For East Antarc-tica, estimates of moisture convergence range from 65 to 96 mm yr -1 and net precipitation from 60 to 123 mm yr -1 (respectively 72-96 and 91-123 mm yr -1 when excluding NCEP DOE R2). The polarity of the linear trend for moisture convergence is not consistent. In any case, changes are not driven by precipitable water increases. The scarce situ observations and the unreliable satellite observations  (Bouchard et al. , 2010)  make Antarctica a fitting object of study for reanalyses. The differences we found between the data sets remind us of their familiar limitations: model dependence in the absence of observations and unreliable time series  (Bromwich et al. , 2011; Nicolas and Bromwich, 2011).
Acknowledgements. This work was supported by the project " Mechanisms of moisture advection in high latitudes in the present and future climate " funded by  CNRS ,  IGE  and  UGA  and partly through the  BELMONT Forum Fund ARCTIC-ERA  project funded by  ANR. Olga Zolina  also benefited from the project  14. 613. 21. 0083 (ID RFMEFI61317X0083 ) funded by the  Ministry of Education and Science of Russia. We are grateful to the  logistics crew at Cap Prudhomme ,  Vincent Favier  and the  TA64 Météo France team , particularly  Didier Lacoste  for investigating the station's present weather archives on our behalf. Finally, we thank the editor and the two anonymous reviewers for their consideration and constructive comments. Edited by:  Christian Beer Reviewed  by: two anonymous referees.
| Explanatory variables A set of eight explanatory variables was selected to explain the should cool streams  (Isaak et al. , 2017). One variable was determined to characterize the riparian vegetation. A shading factor (SF), corresponding to a coefficient of reduction of the overall incident radiation, was estimated by  Valette et al. (2012). SF gives the averaged vegetation cover (%) derived from a buffer of 10 m of vegetation polygons on both sides of reaches from the BD TOPO ® database, provided by Institut national de l'information géographique et forestière (IGN). SF has been calculated between 7 a. m. and 9 p. m. over the summer period between June 1 and September 30 over the period 2008-2012, when the effect of shading is at its annual seasonal maximum for the North Hemisphere. The model of  Li, Jackson, and Kraseski (2012)  was implemented in its simplest version, that is, considering rectangular trees, located at the edge of the bank, without overhang.
| Thermal regimes clustering To identify natural thermal regimes of stations sharing similar Ta-Tw relationship, an agglomerative hierarchical clustering (AHC) has been used. The AHC is based on the four metrics described above (TS, b, ΔT Jan , and ΔT Aug ). The Euclidean distance is used to measure the dissimilarity, and clusters are found with the Ward's minimum variance method. The stability of clusters is assessed through a bootstrap approach with the R package "fpc"  (Hennig, 2019) , and the similarity between each new cluster set and initial cluster was assessed with the Jaccard index  (Hennig, 2007; Maheu et al. , 2016). The Jaccard coefficient ranges from 0 to 1, and a cluster with a coefficient larger than 0. 75 can be considered as stable  (Maheu et al. , 2016). Each thermal regime identified is described in terms of magnitude (mean Tw over a month) and amplitude (differences between the maximum and minimum values of MTw) and compared with MTa.
| Identification of environmental drivers in thermal sensitivity A CART is used to examine the relationship between TS and the set of explanatory variables described above. CART analysis  (Breiman, Friedman, Stone, & Olshen, 1984 ) is nonparametric and non-linear and does not introduce an a priori structure of the link between explanatory variables and the variable to be explained contrary to generalized linear models implicit assumption  (Breiman et al. , 1984; Ripley, 1996). CART recursively partitions observations in a matched data set, consisting of TS (response) and the eight explanatory variables, into progressively smaller groups  (De'ath & Fabricius, 2000). Each partition is a binary split. During each recursion, splits for each explanatory variable are examined, and the split that leads to the most homogeneous subgroups with respect to the dependent variable is chosen.
The interpretation of results summarized in a tree with series of logical ifthen conditions (tree nodes) is very simple. We used the R package which is constituted by selecting randomly 80% of the observations (80% of 127 stations × eight explanatory variables × TS), and the test set consists of the remaining 20%. We used the implementation in the R package "randomForest"  (Liaw & Wiener, 2002). The explanatory variable importance is given directly by the "randomForest" algorithm, which determines how much the mean square errors in prediction increases when that covariate is randomly permuted within the tree. The random selection is performed 100 times, and the explanatory variables importance for each test set was then averaged.
CART analysis results showed that streams with a Q Aug value higher than 5 L s -1 km -2 , associated to an SF less than 30% and a BFI less than 0. 8, have a lower TS than others stations (C2 vs. C3; Figure  7 ), which seems to confirm our assumption. However, the importance of Q Aug remains applies to a subset of stations and the BFI remains the main variable representing the influence of groundwater inflows in our dataset. Sinokrot  & Stefan, 1993). The riparian vegetation captures solar radiation and leads to reduced Tw resulting in a decrease of TS. This effect is particularly visible in summer when the solar radiation is the strongest and represents the main source of energy inputs (e. g. ,  Hannah, Malcolm, Soulsby, & Youngson, 2008). In addition, the riparian vegetation of the Loire basin is mainly composed of deciduous trees, which considerably limit the effect of shading in winter.