dataesr/scientific-paragraphs-categorization huggingface dataset:
The DAC data are provided in the form of 6-h sea level fields on a 1/4°9 1/4°regular grid covering the global oceans. The atmospheric correction at each tide gauge is taken from the nearest DAC grid point to the tide gauge. The amplitude, A, and phase, /, of the annual cycle are estimated by first fitting the following linear model to the sea level data: y t ¼ a þ bt þ c cos 2p 365:24 t þ d sin 2p 365:24 t þ e t where e t is the error term. And then, once the regression coefficients have been estimated, by A ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi c 2 þ d 2 p / ¼ arctan d=c ð Þ As an initial validation test, we compare de-seasoned and de-trended time series of sea level from satellite altimetry and tide gauges at each station. Because altimetry measurements are not, in general, taken at the exact location of a tide gauge, but at some point nearby, nor are they collocated in time with the tide gauge observations, some processing is necessary to obtain consistent altimeter-tide gauge pairs for the comparison.
Evolution of Sea Level Trend from the Open to the Coastal Ocean As seen in the previous sections, altimetry already provides unique observational datasets to characterize how sea level variability evolves from the open ocean to the coastal ocean, and new technologies promise further improvements. An obvious scope arises to characterize the statistics of long-term sea level variations as functions of the distance from the coast, which would help to establish a quantitative link between open-ocean and coastal sea level variations at temporal scales relevant to climate. One particularly intriguing aspect is the coastward evolution of sea level trends. No conclusive causes supporting a difference of the coastal trend from the open ocean one have been identified so far when those trends are averaged over the entire globe (see  Scharroo et al.
We use along-track nadir altimetry data from the TOPEX/Poseidon, Jason-1 and Jason-2 satellites, which share the same ground tracks and provide the longest sea level anomaly time series, over the 1993-2012 period. Three datasets are analysed and compared: • the standard AVISO dataset (version 2014 of delayed-time data,) distributed by the Copernicus Marine Environment Monitoring Service (CMEMS). These are the 'vfec' data, i. e. validated, filtered, sub-sampled and corrected for long wavelength errors; the spatial sub-sampling of data results in a spatial resolution of 14 km. • two datasets provided by LEGOS/CTOH, in which along-track altimetric data have been reprocessed using the 2011 and 2016 versions of the X-TRACK algorithms, respectively  (Birol et al. 2016). These are more adapted to coastal regions and provide data at a spatial resolution of 7 km.
The editing and extrapolation of geophysical corrections has been improved in X-TRACK2016 compared to X-TRACK2011, on two aspects: (1) the removal of land contamination in the radiometer-derived wet tropospheric correction is now performed in X-TRACK2016 with an algorithm computing the proportion p of land within the radiometer footprint of each altimeter mission and rejecting the wet tropospheric correction values for which p [ 0. Possible other outliers away from the coast are discarded by an algorithm that detects large differences between the correction values at two consecutive points along the track for each cycle. The resulting data gaps are either interpolated (away from the coast) or filled with the closest values qualified as valid (in the land/sea transition areas). A technique based on the discrete wavelet transform is then used to compute a cleaned and noise-free wet tropospheric correction; (2) the ionospheric correction filtering has been updated to more efficiently detect outliers: the filter is a median absolute deviation (MAD) threshold in X-TRACK2016, used instead of the 3r-threshold filter used in X-TRACK 2011 (r being the standard deviation).
The sea-state bias (SSB) correction was smoothed in X-TRACK2011 using Bezier curves. In X-TRACK2016, the SSB correction is filtered in the along-track direction with a Loess low-pass filter, and missing values are replaced by the nearest interpolated data. Regarding the other altimeter corrections, usually derived from models, very few values are discarded by the editing process. We choose to replace flagged corrections by their nearest valid neighbours. Along-track data from the three datasets were post-processed with various filters to remove the remaining erroneous data, using a strategy similar to that used in  Melet et al. (2010). As the coastal shelf is narrow off Western Africa (a few tens of km), we selected the sections of the altimetry tracks that are located less than 200 km off the coast (referred to as coastal sections hereafter) to characterize the evolution of sea level trends from the open to the coastal ocean.
For each coastal section, the changes in sea level trend were calculated relative to the 'open ocean trend' for that section (defined as the mean trend for the part of the section located in the open ocean from 160 to 200 km off the coast; this choice was made to exclude the continental shelf). The number of valid independent measurements for which the sea level trend was computed in the coastal sections is larger in the X-TRACKv2016 than in the X-TRACKv2011, especially in the last few kilometres towards the coast, due to a better editing of the data and to a more refined processing of geophysical corrections applied to altimetric data in X-TRACKv2016 than in X-TRACKv2011  (Birol et al. 2016 ). The number of measurements is much lower in the AVISO dataset (Fig. 11 , right panels, in red) and decreases significantly in the last 30 km off the coast due to both the spatial subsampling of AVISO data, less adapted geophysical corrections in the coastal zone, and a more conservative editing of the data in AVISO than in X-TRACK datasets.
The changes in sea level trend from the open to the coastal ocean are shown in terms of percentage in Fig. 11  (left panels). In the AVISO dataset, the trend changes from the open to the coastal ocean mostly range from -20 to ?20% for the different coastal sections, with a coastward decrease of the sea level trend equatorward of 10°S/10°N, and a coastward increase of the sea level trend poleward of 25°N/25°S. When sea level trend changes are bin-averaged across all the coastal sections as a function of the distance to the coast (Fig. 11 , bottom-right panel), no robust evolution of the sea level trend from the open to the coastal ocean can be seen off Western Africa in the AVISO dataset. In the X-TRACKv2011 dataset, a substantial increase in the sea level trend can be seen for most coastal sections (Fig.
11 , middle-left panel). As more valid data are available in the X-TRACKv2011 dataset than in the AVISO dataset, the averaged coastward evolution of sea level trend can be studied closer to the coast (up to 15 km off the coast). On average across the coastal sections, the sea level trend over 1993-2012 steadily increases from the open to the coastal ocean off Western Africa in this dataset, reaching an increase of 25% 25-30 km off the coast (Fig. 11 , middle-right panel). The increase weakens in the last few kilometres off the coast and is less robust across the different coastal sections (grey envelope in Fig. 11 , middle-right panel). Results from the X-TRACKv2016 dataset are in between these from the AVISO and X-TRACKv2011 datasets. The sea level trend increases coastward for coastal sections located south of 15°S and north of 25°N (a result qualitatively robust for the 3 datasets), but less so than in the X-TRACKv2011 dataset.
Equatorward of 10°N/10°S, no robust coastward evolution of the sea level trend is seen across the different coastal sections. The greater number of valid data in the X-TRACKv2016 dataset allows quantifying the mean coastward evolution of sea level trend up to 5 km of the coast. On average, the sea level trend only slightly increases coastward in the X-TRACKv2016 dataset (by less than 10%), but this is not robust across the coastal sections (Fig. 11 , upper-right panel, grey envelope). These results show that efforts made to improve satellite nadir altimetry products in the coastal ocean allow recovering more data and obtaining more coherent long-term signals in the coastal zone. In particular, the editing and extrapolation of geophysical corrections have been updated and improved in X-TRACKv2016 compared to X-TRACKv2011.
Yet, better geophysical corrections themselves are needed to improve the accuracy and reliability of altimetry data in the coastal zone. The analysis performed here highlights that efforts are still needed on the processing of data and on the geophysical corrections applied to satellite data for studying the sea level trend in the coastal zone over the last two decades more robustly. It should also be noted that the contribution from waves to sea level variability and trend is not considered here as it is removed from satellite altimetry data since the primary focus of satellite altimetry is to study ocean circulation and dynamics. Yet, wave-induced set-up and run-up can contribute to the sea level trend at the coast  (Melet et al. 2016).
Summary and Conclusions In this contribution, we have reviewed the status of the measurements of sea level in the coastal zone. First we have summarized the centennial-scale observations that we get from tide gauges, and then we have aimed at showing how improved satellite nadir altimetry products in the coastal ocean are now complementing those observations by allowing meaningful measurements and the retrieval of coherent long-term signals in the coastal zone. The trend towards better altimetric data at the coast comes not only from improved processing and corrections, but also because of technological innovations such as Ka-band altimetry and SAR altimetry, and we have discussed the main advantages deriving from those two innovations that can be now appreciated thanks to the AltiKa altimeter on SARAL and the SIRAL altimeter on CryoSat-2 (in turn a precursor of the Sentinel-3 altimeter, which has recently been launched and is being operated in SAR mode over the entire ocean).
We observed that different products give different answers regarding the coastward evolution of the sea level trend, and we cannot yet robustly deduce the quantitative evolution of sea level trend from the open to the coastal ocean. The clear recommendation stemming from what we have presented is that further efforts are still needed to study sea level trends in the coastal zone from past and present satellite missions. Further improvements are expected from more refined processing and screening of altimetric data, but in particular from the constant improvements in the geophysical corrections applied to them, such as wet tropospheric, tides and dynamical atmospheric corrections, which all become noisier when coming near shore. It is worth noting that such improvements in corrections should enable the full coastal exploitation of the data now flowing in from Ka-and SAR altimetry, and in particular the global SAR altimetry data now coming from Sentinel-3.
Acknowledgements The AVISO altimeter products were produced by SSALTO/DUACS and distributed by AVISO, with support from  CNES  (http://www. aviso. altimetry. fr/). The X-TRACK altimetry data used in this study were developed, validated and distributed by the  CTOH/LEGOS, France. This work has been partially supported by the  Natural Environment Research Council (NERC) National Capability  funding. Some results were produced in the framework of the  ESA Sea Level CCI  project (ESRIN Contract No. 4000109872/13/I-NB ) and  ESA CryoSat Plus for Oceans  ( CP4O )  project (ESRIN Contract No. 4000106169/12/I-NB ). Results presented in Sect. 5 were produced in the framework of the  Sea Level SpaceWatch  project, funded by the  UK Space for Smarter Government Programme  through a grant from the  UK Space Agency.
b Fig. 11 (Left): Relative changes in SLA trend (mm/year) over 1993-2012 in percentages along coastal sections of altimetry tracks offshore Western Africa for the (top) X-TRACKv2016, (middle) X-TRACKv2011 and (bottom) AVISO along-track datasets. Only the sections of altimetry tracks located less than 200 km off the African coast are studied. Changes are relative to the open-ocean trend defined here as the trend of sea level anomalies averaged over the sections of altimetry tracks located from 160 to 200 km offshore the African coast. (Right): Relative changes in SLA trend (in %) were averaged as a function of the distance to the coast, using 5 km wide bins (blue line, left axis) for the (top) X-TRACKv2016, (middle) X-TRACKv2011 and (bottom) AVISO along-track datasets. The grey envelope shows plus and minus one standard deviation from the average.
6) TUDelft Widely used dataset, mirrored by tens of sites worldwide, with continuously updated corrections, but no specific coastal processing PODAAC Experimental products from the ALES processor included in SGDR-type files alongside the standard products and corrections Coverage Global ? European regions ? Arctic ? SW Indian Global ? European regions Global Global 23 regions Global \50 km from Global, coast Altimeter Product level Posting rate e1, tx, e2, en, j1, L2, L3, L4 1 Hz j2, c2 also L4 (LRM/PRLM), sa, h2 e1, tx, e2, en, j1, L3 1 Hz j2, c2 (LRM/ L3 for PRLM), sa assimilation (s3a to be added soon) j2 L2 20 Hz sa, (j2 to be L2 40 Hz added soon) tx, j1, j2, gfo, en L2, L3 1 Hz 20 Hz (test) gs, e1, tx, pn, L2 1 Hz e2, gfo, j1, n1, j2, c2, sa j2, n1, (j1, j3 to L2 20 Hz be added soon) ID Produced by AVISO CLS, CNES CNES CMEMS CLS CNES PISTACH CLS CNES PEACHI CLS CNES X-TRACK LEGOS- CTOH RADS EUMETSAT, NOAA, TUDelft ALES NOC.
A mean sea surface (MSS) is the level of the sea due to all those contributions that can be assumed constant in time and can be computed as the temporal mean of sufficiently long time series. More accurate MSS models (for instance DTU15) are built from combinations of multiple altimetric and gravimetric missions. The choice of a specific MSS over another is critical in the coastal zone, and there may be biases between datasets if they are referred to different MSS.
We chose to collect information concerning these conditions in order to compare their prevalence within our sample of cramp sufferers with the prevalence of these conditions in a population of patient without cramps. The control population was extracted from the database of another study conducted in 2013 in the GMD PBRN aiming to evaluate the association between sedentary lifestyle and nocturnal leg cramps  [13, 14]. The English version of the questionnaire is available in Additional file 1.
Data analysis We computed overall prevalence of cramps, cramps occurring more than 3 times a month, and cramps that led to awakenings. Results are reported with 95 % confidence intervals adjusted for clustering within practices. We computed proportions of medical conditions and general medication known to be associated with cramps (anti-hypertensive treatment, lipid-lowering treatment, beta mimetic)  [10]  and compared them with data collected from patients not experiencing cramps within a similar population, as part of a study conducted in 2013 in the GMD PBRN. We performed chi-square tests to compare the gender and age groups between our sample and the health insurance population and to compare the proportions of medical conditions and general medications between our sample of cramp sufferers and our sample of patients not experiencing cramps.
Limitations Our study was conducted in only one French region, in 25 practices, members of a regional research network. Although our results are consistent with the literature, replication of our study in other settings would be useful to assess the extent to which they are generalizable. Our age groups differ slightly from the reference population, but seem representative of a population of patients attending GPs. Although we constructed our questionnaire from a review of literature, we did not validate it, and therefore cannot exclude some level of measurement bias. The medical conditions and medications were extracted from the GPs' medical records. The extent to which these were complete is unknown. As we had not collected clinical data for patients not experiencing cramps in the study, we tried to compensate for this weakness by comparing our sample to another sample extracted from the same setting.
Fauna Macroinvertebrate data recorded during the period 2006-2013 were retrieved from the water agency 'Confederación Hidrográfica del Ebro' (CHE) website (http://www. datossuperficiales. chebro. es). The initial faunistic data set comprised 414 sites, 1508 sampling events (i. e. , site × date) and covered 148 taxa. We removed sites without information about river identity or type. We also discarded some faunistic data because they were collected using a sampling protocol different from that applied in the majority of cases. Our final faunistic data set covered 411 sites and 1505 sampling events. Spanish monitoring of macroinvertebrates was mostly performed using family level identification and in rare cases using genus or species level or even class level identification. We harmonized the taxonomic levels to the family level and discarded 25 taxa due either to insufficient taxonomic resolution (i.
Traits We used available information on 11 biological traits for European taxa (mainly genera;  Tachet et al. , 2010)  with some adaptation for the Mediterranean region  (Bonada and Dolédec, 2011; Bonada et al. , 2007a; Statzner et al. , 2007). These biological traits describe morphology, life history, feeding habits, resistance or resilience potential and respiration types. The different traits were described by 61 categories, and the affinity of each taxon for each trait category was quantified using fuzzy-coding  (Chevenet et al. , 1994). Taxa trait profiles were generated as frequency distributions of categories within each trait. For taxa with a higher level (i. e. , family) than the one given in the trait database (usually the genus), we calculated the trait profiles of each taxon by averaging the profiles of the genera reported as present in Spain.
For each sampling event, we further calculated the communityaggregated trait profile by averaging the taxa trait profiles weighted by their corresponding log10-transformed abundances as commonly performed (see e. g. ,  Gayraud et al. , 2003). We then applied arcsinesquare root transformation to improve the normality of trait affinity distribution  (Ahrens et al. , 1990). Stressors -We defined a stressor in this study as any environmental factor that exceeds natural levels of variation and causes a measurable biological or ecological response  (Crain et al. , 2008; Statzner and Bêche, 2010). The following six stressors were investigated: nutrients, suspended organic matter (SOM), insecticides, water abstraction, dams and physical alteration of the stream. The water quality data retrieved from the CHE website (see fauna section) covered three stressors (nutrients, SOM and insecticides) and 37 individual parameters.
Suspended organic matter concentration was directly measured, and we log10-transformed values to approach a normal distribution of variables. The number and identity of the remaining parameters, which were actually measured for nutrient and insecticide stressors, were highly variable depending on the sampling sites and dates. To consolidate our data set, we synthesized the values of the different water quality parameters in one global measure for nutrients and one for insecticides. This was performed by adapting the 'Toxic Unit' (TU) principle  (Sprague, 1970)  to a more general situation than the evaluation of toxic effects (pseudoTU, Eq. (  1 )). pseudoTU ¼ log10 observation threshold: ð1Þ The selected threshold depended on the stressor. For nutrients we used the High-Good ecological quality threshold obtained from the French water quality grids  (SeqEau v2; Oudin and Maupas, 2003) ; for insecticides, we used the lowest value among (i) NOEC values from the INERIS (http://www.
ineris. fr/substances/fr/) and (ii) LC50 from either INERIS or  Malaj et al. (2014) , further divided by ten to approximate NOEC values. To summarize the water quality for each stressor, we calculated the maximum pseudoTU among all the parameters measured at each site. The maximum was used instead of the sum because of the different number and identity of measured parameters across sites. Moreover, maximum TU has been reported to have explanatory power at least similar to the summed TU regarding community endpoints  (Liess and Von der Ohe, 2005; Schäfer et al. , 2011). The available descriptors related to the hydrological regime in our study sites were derived from GIS data obtained from the CHE geoportal (http://iber. chebro. es/geoportal/) and GLOBAQUA partners (the Catalan Institute for Water Research, Girona, Spain and Ludwig-Maximilians-Universität, Munich, Germany).
For each site we considered precipitation (mm/year), and we took into account the total area of dam reservoirs, the area of lands irrigated with different intensities (low, medium and high) and the numbers of different types of water abstraction points (spring, well, in-stream and others) within the catchment to which a site belonged. To allow a relevant comparison among sites with different catchment sizes, the data were transformed by (i) dividing the count data (water abstraction points) by the log10-transformed area (in km 2 ) of the corresponding catchment and (ii) dividing the area data (dam's reservoirs, irrigated cultures) by the area of the corresponding catchment and then applying the arcsine-square root transformation to improve the normality of their distributions. The transformed dam's reservoir area was considered to be an individual stressor whereas precipitation and the other transformed hydrological variables were further processed by Principal Component Analysis (PCA) to synthesize the information into one variable describing water abstraction.
Modelling and data analysis As a preliminary step, the stressor data were matched against the fauna data by their site codes and, eventually, the dates at which the different analyses were performed. If abiotic data were available for a site in a one-year period around the faunistic sampling event (six months before and after), we used the median of the observations over this one-year period; otherwise, we used the median of all observations made for the site over the period 2006-2013. For each stressor, we built a boosted regression tree (BRT) model using sample stressor intensity as the response and a selection of trait categories associated with their a priori defined constraints (see Table  S1 ) as predictors. BRT modelling uses the technique of 'boosting' to combine large numbers of rather simple tree models to adaptively optimize predictive performances of a global model.
For each stressor in this application, the BRT algorithm looks for the trait category combinations that best respond to a given stressor intensity. As a result, the model output may be interpreted as a score of community tolerance to a given stressor related to specific biological trait combinations. Because of discrepancies in data availability among the different stressors (Table  1 ) and the need for data with complete stressor information to develop the models, we considered one data set per stressor type by selecting the largest sample collection having no missing stressor information. As a result, these stressor-specific data sets differed both in size and in the identity of the samples included. For each stressor-specific data set, we used river types and stressor intensity as strata to randomly select 90% of the sites for training the corresponding model and kept the remaining 10% of the sites for testing.