dataesr/scientific-paragraphs-categorization huggingface dataset:
Separated DNA samples were visualized using a UV transilluminator (wavelength 254 nm) and photographed. Alternatively, DNA was transferred from the polyacrylamide gel onto a nylon membrane by Southern ''squash'' blotting, and hybridized with a radiolabeled CAG•CTG-containing probe, as previously described  (Gomes-Pereira et al. , 2004). The AmpliSize TM molecular ruler (50-2000 bp ladder; BioRad) and the Kodal Digital Science 1D software (Kodak) were used to quantify electrophoretic mobility and DNA sizing.
F. Results on Jason-2 Real Data This section is devoted to the validation of the proposed SSE denoising algorithm when applied to a parameter estimation task on the oceanic Jason-2 dataset. The data represent a 36-min time series and consist of 45 000 real signals that were extracted from pass 30 of cycle 35. The signals contain K = 104 temporal gates, where each gate corresponds to a distance of 46. 8 cm. Fig. 7  shows a sequence of 800 Jason-2 signals before and after SSE filtering. Note first that this sequence shows a reduced variation in the altimetric signals, which justifies the use of the proposed strategy. Moreover, it clearly shows a reduction in the noise affecting the signals after the application of the SSE algorithm especially in the tail of the signal (i. e. , the decreasing part), which was most affected by the speckle noise.
The turbulent heat flux decreases further, which then causes a larger imbalance (positive feedback). Finally, as a result of the intense stratification, turbulent activity becomes very weak and the VSBL sets in. The magnitude of the sensible heat flux maximum strongly depends on the ambient wind speed. It is this sensitivity, which causes the transition toward the VSBL to be sharply defined in terms of wind speed. In VdW12b an expression for this threshold, coined ''minimum wind speed for sustainable turbulence,'' was given. Van Hooijdonk et al. (2015, hereafter VH15)  applied the framework to a large climatological dataset from Cabauw, Netherlands. By comparing the ambient wind to its threshold value, the transition between the VSBL and the WSBL was predicted in agreement with observations.
Interestingly, the same dataset was analyzed by  Monahan et al. (2015). Although they followed a different route, using hidden Markov models, they arrived at similar conclusions regarding the classification of the data in terms of the maximum sustainable heat flux. The viewpoint of the MSHF was used by  Donda et al. (2015)  in order to understand regime transitions in direct numerical simulations of flux-driven stably stratified channel flows. In  Nieuwstadt (2005)  and  Flores and Riley (2011)  a sudden transition of turbulent to nonturbulent flow was reported in cases when the surface heat extraction exceeds a certain limit. Donda et al. (2015)  showed that the transition can be understood in terms of the MSHF theory and that the critical point can be predicted in terms of external forcing parameters.
They will eventually offset the initial positive feedback, so that even in case with no turbulence, a new thermodynamic equilibrium will emerge  (Duynkerke 1999; Steeneveld et al. 2006; Edwards 2009; Acevedo et al. 2012; Bosveld et al. 2014b). As shown below, here we will tackle the limitations addressed above and generalize MSHF theory by explicitly accounting for surface feedbacks. As an illustration of the scope of this paper, an observational example is given in at the 40-m level at Cabauw. Data between 0000 and 0300 UTC are shown as to represent reasonably stationary conditions (for more details refer to section 2). A typical ''S shaped'' dependence is observed: For strong winds the dependence of the inversion on wind speed is rather weak. When the wind speed becomes less than a certain threshold (;6-7 m s 21 ), the inversion strength sharply increases until, for very low speeds, it levels off to ;10 K.
As expected, inversions are weak for strong winds, whereas large inversions are found at weak winds. However, the transition between those states occurs over a rather narrow wind range. The present models explain this fascinating behavior. It is shown that the mechanism for sudden transition can be understood in terms of the maximum sustainable heat flux mechanism. The existence of the S shape in observations appears to be robust in a sense that it is observed in Cabauw data (moderate climate) as well as for data obtained at Dome C, Antarctica. Differences between the two sites were explained by a dissimilar strength of atmosphere-surface coupling, being much weaker in the latter case. Interestingly, qualitative support on generality of the results was obtained by running a more realistic, multilayer column model for strong and weak coupling, which leads to similar conclusions.
Acknowledgments. The scientific comments on the original manuscript by  Dick McNider ,  Adam Monahan , and  John Wyngaard  are highly appreciated. The research is supported by a Consolidator Grant of the  European Research Council  ( 648666 ) and Grant  82301010  from the  Dutch National Science Foundation (NWO) , which is gratefully acknowledged. The Dome C observatory of the surface atmospheric boundary layer is supported by  IPEV  (program CALVA 1013),  INSU/LEFE  ( GABLS4  and  DEPHY2 ), and  OSUG (GLACIOCLIM). We thank the  BSRN network  and particularly  C. Lanconelli  for the dissemination of radiation data at Dome C.
Acknowledgements The core of this paper was developed during the  International Council for the Exploration of the Sea (ICES)  Working Group on Aquaculture (WGAQUA) workshops that were held in Vigo, Spain, in April 2014,  Rhode Island, USA , in March 2015, and Yerseke, The Netherlands, in April 2016. Thanks go to the WGAQUA. This paper has also been improved during a workshop held in Palavas in February 2016. MC and CWM would like to thank the  GDRI RECHAGLO (International Research Group in the field of fisheries and aquaculture 'Responses  to the global population change and fisheries and aquaculture communities and their habitats, 2015-2019') for financial support.
The FLEXICIENCY IT system, as presented in the Figure  1 , consists of three building blocks: the Market Place, the DSO platforms (mainly Data Provider) and the energy service provider platforms (mainly Data Requesters). The authors are consortium partners and acknowledge project members at the end of this paper. For more about FLEXICIENCY project, we refer to its website  [5]    This paper defines the technical scope of common areas to be shared within the whole System. Towards plug and play B2B platform connections, a list of common services and common data exchange interactions is extracted from Use cases to make the best reuse of different demo contributions. Standardization in processes, content, formats and communication protocols are adopted for 5 main components of the system. Requirements will in particular address the system matching with high level Architecture principles of FLEXICIENCY objectives.
FLEXICIENCY -BUSINESS ARCHITECTURE The FLEXICIENCY-WP2 aims at defining a common architecture framework and requirements for data and service exchange at EU level, addressing data privacy and Paper 0225 CIRED 2017 interoperability, among regulated and unregulated energy players. This framework enables organizations to effectively address critical business needs, mainly by:  Ensuring that everyone speaks the same language;  Avoiding lock-in to proprietary solutions by standardization.
WHOLE PROCESS ILLUSTRATION The following diagram, illustrates the main interactions between different sub systems, from consumer service acceptance to service delivery, while crossing FLEXICIENCY system for Metering data exchanges between DSO and Energy Service Company and their respective platforms, more information is given in  [1]. Guidelines, roles & responsibilities have been provided towards the stakeholders (hence towards their IT Platforms) to support and contribute to the success of the system as a whole. Common areas to be shared between the subsystems have been defined, allowing a standardization of services and data exchanges. During implementation, this standardization process must carry on, using these guidelines and further deeper description. Content of messages will be more developed to support the interactions between the main users of FLEXICIENCY either from Regulated or Unregulated side of Energy Market.
Acknowledgments FLEXICIENCY  project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement n°  646482. The Consortium Members are hereby acknowledged for their contribution and participation in the project, in particular: Endesa,  Verbund AG ,  Siemens ,  Joule Assets ,  Cybergrid GMBH ,  University Ljubljana, Kiwi Power Ltd, EDSO  for smart grids.
Methods The PanCare Childhood and Adolescent Cancer Survivor Care and Follow-Up Studies The PanCare Childhood and Adolescent Cancer Survivor Care and Follow-Up Studies (PanCareSurFup) consortium pools data from across Europe to form the largest collaborative study to date to investigate adverse health outcomes in long-term survivors of childhood and adolescent cancer (referred to as childhood cancer henceforth). The PanCareSurFup cohort comprises data from 13 European cohorts, within 12 countries, of childhood cancer survivors, with contributions from both population-based cancer registries and major treatment centers. Ethical approval was obtained separately for each cohort from the appropriate bodies within each specific country. Written consent from participants or appropriate legal permission to process individual patient data without individual consent was obtained within each specific country as well.
Relative excess risks can be interpreted as the ratio of absolute excess risks adjusted for other explanatory factors. Cumulative incidence for the first occurrence of a subsequent STS was computed by years since five-year survival by means of the stcompet command in Stata  (25). Death due to any cause prior to developing a subsequent STS was treated as a competing event. Expected cumulative incidence was calculated using the Ederer II method  (26, 27). All statistical analyses were conducted in Stata statistical software, version 14. 1. Likelihood ratio tests comparing the deviance of a model containing the variable of interest (eg, age at diagnosis), which was coded such that it had the median value of the variable at each level, with the deviance of a model without the variable of interest were used to calculate P values for linear trend and heterogeneity.
Funding This work was supported by the  European Union 's  Seventh Framework Programme  for research, technological development, and demonstration under grant agreement No. 257505. Additional financial support was received from  The Italian Association for Cancer Research  and the  Compagnia San Paolo , The  Fondo Chiara Rama ONLUS, The Swedish Childhood Cancer Foundation , the  French Association for Cancer Research (ARC) , The  French National Agency For Research (ANR ;  Hope-Epi  project), the  French National Cancer Institute (INCA) ,  Pfizer Foundation for Children and Adolescent Health ,  Slovenian Research Agency , the  Swiss Paediatric Oncology Group , The  Swiss Cancer League  ( KLS-3412-02-2014 ), The  Swiss Cancer Research foundation  ( KFS-02783-02-2011 ), The  Swiss National Science Foundation  Grant Number ( PDFMP3_141775 ),  The Dutch Cancer Society , and  The Norwegian Childhood Cancer Foundation.
The other meteorological forcing variables were derived from ECMWF deterministic forecasts and the downwelling longwave and shortwave radiative fluxes are from the Land Surface Analysis Satellite Applications Facility (LSA SAF) project  (Trigo et al. 2011). The default input soil and vegetation parameters for the ALMIP2 experiment are derived from the ECOCLIMAP2 Africa database  (Kaptué Tchuenté et al. 2011) , which takes into account interannual variability of the vegetation parameters [leaf area index (LAI)] over West Africa. The soil data are based on the Harmonized World Soil Database (HWSD; Given the importance of shallow and rocky soils to the ecohydrological functioning of the Gourma region, a specific soil map, which includes shallow soils and coarse-textured soils, was also made available to the ALMIP2 participants for the Mali site.
This map was derived by supervised classification of Landsat images at 30-m spatial resolution, which allowed the identification of different classes based on the soil spectral properties. The corresponding soil texture (coarse and fine fraction, the latter separated into clay, loam, and sand) and depth was assigned to each spectral class based on field knowledge. Finally, eight new classes, characterized by the same soil textures and depths, were defined (Table  1 ), and the dominant class in each ALMIP2 grid was selected (Fig. 3 , bottom). In addition to the coarse-texture soil information, which is not available in the ECOCLIMAP2 database (although indirect information can be derived from the land-cover maps and particularly the rock outcrop fraction), the alternative soil map spans a.
2) ET PRODUCTS DERIVED BY REMOTE SENSING Three different ET products were considered for comparison with the ALMIP2 results at the mesoscale. The Atmosphere-Land Exchange Inverse model (ALEXI) evapotranspiration product  (Anderson et al. 2007 (Anderson et al. , 2011) )  uses thermal infrared information by different satellite sensors (geostationary plus polar orbiting satellites) to estimate land surface temperature and combines it with a land-cover map and a land surface scheme to estimate ET. The product used here is based on a version of ALEXI using MODIS-derived day-night temperature differences, following the approach described in  Anderson et al. (2015). In this version of the dataset, ET is reported as 7-day average values. The MODIS ET product (MOD16) estimates ET globally every day at 1 km, using global surface meteorology data to compute the atmospheric demand (potential evaporation) combined with fraction of photosynthetically active radiation (FPAR)/LAI data from MODIS and the MODIS land-cover map  (Mu et al.
d. ALMIP2 models The 20 ALMIP2 models used in this study and the different options employed to perform the simulations are listed in Table  2. Although almost all models employ the ECOCLIMAP2 database to derive soil and vegetation characteristics, not all models use the whole set of parameters provided, some having intrinsic formulations or specific data for some variables. In particular, three models were run with a dynamic LAI formulation (CLM_CN, ORCHIDEE_ dyn, and STEP). Five models (hereafter indicated by adding _Spec to the model acronym) have employed, in addition to the ECOCLIMAP2 soil information, the specific soil map prepared for the Mali site. Precipitation and meteorological forcing are the same for all models.
6 ) becomes more related to soil properties (see, e. g. , the difference between ORCHIDEE_f and ORCHIDEE_f_Spec in Fig. 7c ). The case of STEP is different, as it hardly produces runoff with the ECOCLIMAP2 soil map. The specific soil map allows runoff to occur over the shallowest soils (Fig. 6 ), and the correlation increases, although to a small extent. As for runoff, for the majority of the ALMIP2 models, the precipitation spatial structure is also translated into the modeled ET spatial distributions (Fig. 8 ). An exception is LISMosaic, which shows very low values of ET over the central-east area because the partitioning between ET and drainage greatly favors drainage over these sandy soils. Also, MATSIRO shows low ET values in grids corresponding to a high fraction of rocks in the ECOCLIMAP2 database (see Fig.
1 ) that should not generate runoff at distances exceeding the ALMIP2 grid. An example for 2007, when rainfall over the two areas was roughly the same (302 mm over Agoufou and 285 mm over the Z area), is reported in Fig. 10. Mean annual runoff values for the ALMIP2 models ensemble over the Agoufou watershed and the sandy Z area are equal to 46. 1 and 36. 8 mm, respectively, which are considerably above the observed runoff values of 22. 4 and 0 mm, respectively. When employing the ECOCLIMAP2 soils, all the ALMIP2 models are approximately aligned on the diagonal, meaning that they are unable to differentiate between the two runoff systems typical of the study region. This is most likely due to the missing information on shallow soils in the forcing database (see Fig. 3 ). In contrast, models employing the specific soil maps are farther away from the diagonal, showing more runoff over the Agoufou watershed than over the Z area, except for ISBA, which is not sensitive to the soil map.
2014; Grippa et al. 2011). GLEAM results are in agreement with the ALMIP2 multimodel mean (Fig. 12 , Table  4 ), but the seasonal ET evolution is smoother, which is probably an effect of its coarser spatial resolution. In contrast, the seasonal ET evolution during the wet season is strongly underestimated by MODIS during the wet season, and the ALEXI product underestimates the seasonal contrasts between the wet and dry seasons. The averaging time scale of 7 days reduces the amplitude of variation in ALEXI ET relative to ALMIP and GLEAM (Fig. 12 ), but agreement is good when the daily datasets are averaged to weekly time steps (not shown; see seasonal mean values reported in Table  4 ). The ALEXI ET also shows some noise during the dry season, likely related to undetected cloud contamination.
(2014)  and  Grippa et al. (2011) , remains an issue that has not been resolved by the passage from the regional (ALMIP1) to the mesoscale (ALMIP2). Deriving firm conclusions on which formulation is the best suited to simulate water fluxes in the study area is a challenging task, given the difficulty of attributing the different model behaviors to the model physics, its parameterizations, and the way the entry datasets are taken into account. In addition, there is a need for reinforcing the acquisition of high-quality in situ measurements and to sample the different landscapes typical of the study region, which would allow a more detailed evaluation. However, the analysis carried out in this study highlighted some of the characteristics that need to be taken into account to provide good simulations in this area.
Acknowledgments. The authors thank  Diego Miralles  for advice and interesting discussions on the GLEAM data. In situ data (rainfall and Agoufou pond's level used to estimate runoff) were provided by the  African Monsoon Multidisciplinary Analysis-Coupling the Tropical Atmosphere and the Hydrological Cycle (AMMA-CATCH) observatory. Financial support for this work has been granted by the  French Initiatives Structurante Ecosphère continentale et côtière  ( EC2CO ) and  Les Enveloppes Fluides et de l'Environnement (LEFE)  programs. ALMIP2 has been labelled by the  GEWEX  and  Global Land/Atmosphere System Study (GLASS)  project. Simulations by the SWAP model were supported by the  Russian Science Foundation  (Grant  16-17-10039 , where  O. Nasonova  is the recipient). APPENDIX The ALMIP2 Working Group Here is a list of the ALMIP2 working group members:  S.
Climate model simulations and observational references of the Earth's climate are the two primary sources of information used for climate related decision-making. While uncertainties in climate models and observational references have been assessed thoroughly, it has remained difficult to integrate these, partly because of the lack of formal concepts on how to consider observational uncertainties in model-observation comparison. One of the difficulties dealing with observational uncertainty is its propagation to the space-time scales represented by the models. This is a challenge due to the correlation of observational errors in space and time. Here we present an approximation which allows to derive propagation factors to different model scales and apply these to uncertainty estimates provided by the Climate Change Initiative (CCI) sea-surface temperature (SST) dataset.
The European Space Agency (ESA) Climate Change Initiative (CCI) has placed a special focus on estimating uncertainties in climate data records  (Merchant et al. 2017). This is an important contribution towards mutual uncertainty assessment of models and observations. This study aims to support this practice by illustrating simple ways to propagate uncertainties to scales used in seasonal forecast verification of the El Niño Southern Oscillation (ENSO) relying on the CCI seasurface temperature (SST) gap-free analysis (L4 product)  (Merchant et al. 2014). The propagated observational uncertainties are subsequently confronted to two other uncertainties present in the context of forecast verification: the limited ensemble size and the limited record length of the datasets. The comparison allows to understand how important the observational uncertainty is in the practice of seasonal forecast verification.
a. Observational references and seasonal forecast verification The role of observational uncertainty is explored in this study using the SST CCI gap-free analysis v1. 1  (Merchant et al. 2014 ) and three alternative SST data sets which use different data and techniques to represent observed SSTs namely: the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) data set v. 1. 1  (Rayner et al. 2003) , the ERA-Interim re-analysis  (Dee et al. 2011) , and the Extended Reconstructed Sea Surface Temperature (ERSST) v. 4 data set  (Huang et al. 2015). The observational references are hereafter called ORs. HadISST uses in-situ data (Met Office Marine Data Bank (MDB) and Comprehensive Ocean-Atmosphere Data Set (ICOADS) release 2. 5 and satellite data from Advanced Very High Resolution Radiometers (AVHRR) data.
Acknowledgments. We thank  C. Merchant  ( University of Reading ) and  N. Rayner  ( UK MetOffice ) for their fundamental advise in understanding observational uncertainties. We are also indebted to  C. Ferro  ( University of Exeter ) for the indication on the attenuation of error in correlation skill. This work has been supported by  EU   Seventh Framework Programme FP7  projects  SPECS  ( GA 308378 ),  EUCLEIA  ( GA 607085 ) and the  European Space Agency Living Planet Fellowship Programme  under the project  VERITAS-CCI.
PoS(ICRC2017)1026 days has been considered. Neutrino events are generated using the GENHEN  [4]  software, developed in the ANTARES Collaboration, and they are weighted with the Honda atmospheric neutrino flux [5], if not otherwise specified. For the simulation of atmospheric muon bundles, instead, the MUPAGE  [6]  software is used. Atmospheric muons wrongly reconstructed as up-going represent the main background source for this study. This program uses parametric formulas to describe the energy, zenith and multiplicity distributions of muons arriving at the detector  [7].
Conclusions and Outlook A study on the sensitivity of the ANTARES neutrino telescope to the standard atmospheric neutrino oscillation parameters has been performed. A parallel study on the sensitivity to sterile neutrino parameters, in two different energy ranges, has been conducted. Further improvements of the analysis before the unblinding of real data sets will include the treatment of different sources of systematic effects, in order to study their impact on the final result and a refinement of the quality cuts for selecting events.