dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectrum data European country-specific Spectrum outputs were made available to us by those with access to the data for each year from 2000 onwards. The outputs used were the number of PLHIV on ART, the number of AIDS deaths among persons on ART, and the number of non-AIDS deaths among persons on ART (estimated based on non-AIDS mortality rates published in the 2017 World Population Prospects data). Mortality rates in Spectrum's 2018 round of estimates were based on a 2013 analysis of Collaboration of Observational HIV Epidemiological Research in Europe (COHERE) data following methods used by the International epidemiologic Databases to Evaluate AIDS (IeDEA) collaboration to estimate mortality rates of PLHIV on ART in low-income and middle-income countries  [14]. Mortality rate trends in Spectrum for Western Europe were calculated by fitting incidence curves to program data on new diagnoses and vital registration data on AIDS deaths.
These previous Spectrum estimates did not take into account time trends in on-ART mortality. This study aimed to compare default assumptions and parameters in Spectrum with available cohort data. Therefore, the Spectrum data were generated removing customized adjustments to the Spectrum default ratesmodifications in the on-ART mortality rates to better match program reports of AIDS deaths. Removing these customized adjustments and using the default patterns meant the Spectrum outputs presented in this analysis were different from the published UNAIDS global estimates for Europe  [15]. For comparability with the ART-CC, the annual data were grouped into the years  2000-2003, 2004-2007, 2008-2011, and 2012-2015. Using the Spectrum data, all-cause mortality rates, AIDSrelated mortality rates (calculated as the excess mortality above that of the age-matched and sex-matched general population), and the percentage of deaths that were AIDS-related were estimated overall and by country.
Annual mortality rates were calculated by dividing the number of deaths by the number of PLHIV alive at the midpoint of that year, presented as rates per person. LTFU in Spectrum only affects the distribution of ART patients by time on ART as the input to Spectrum is the number of PLHIV on ART. High rates of LTFU mean that more people need to start ART to match the program reports of numbers on ART. As on-ART mortality is higher in the first year of treatment, higher LTFU rates are associated with higher overall ART-related mortality. Analyses ART-CC data were reshaped to separate each calendar year of follow-up for each individual person. Age (grouped as 16-24, 25-34, 35-44, and !45 years) was updated for each calendar year of follow-up for each person. The duration on ART for each person was updated for each calendar year and categorized firstly as 0-6, 7-11, and at least 12 months, then secondly as 0 to less than 1, 1 to less than 2, 2 to less than 3, 3 to less than 4, and at least 4 years, to see which categorization best fitted the data.
Results The ART-CC dataset contained 94026 PLHIV: 25 534 (27. 2%) women and 9357 (10%) with IDU transmission. During 585 784 person-years of follow-up after 2000, there were 5515 deaths. At the time of starting ART, median age was 37 years (IQR 31-45), median CD4 þ count was 252 cells/ml (IQR 126-377), median HIV-1 viral load was 63 160 (IQR 11 900-199 800) copies/ml and 17 127 (18. 2%) PLHIV had been diagnosed with AIDS. When LTFU was defined as no clinical follow-up a year before the administrative censoring date, 20 763 (22. 1%) PLHIV were considered lost, reducing to 15513 (16. 5%) and 6850 (7. 3%) for gaps of 1. 5 and 2 years, respectively.
The decrease in mortality rates observed in the ART-CC occurred earlier in some countries than others, possibly because of earlier introduction of guidelines and policies for treating HIV  [22]. Another explanation could be variation in the demographic characteristics of PLHIV across the countries  [8]. Mortality rate decreases observed in ART-CC were not linear, for example, the decrease for Switzerland appeared to level off over time and so may have reached a steady state with little room for further improvement in the survival of PLHIV. The increase in the proportion of deaths in the ART-CC coded as unknown/unclassifiable in 2012-2015 compared with earlier years could be because of various reasons, one being that for some cohorts these later deaths had not been linked to cause of death information at the time of the data update.
Since the average speed of the ship was 8 knots, the average spatial resolution of FCM and FRRf measurements was 7. 5 km. The Ferrybox sensors stored data every minute. During the cruises the high-resolution methods  (FRRf, FCM, and Ferrybox)  were combined with lower-resolution methods, consisting of measurements at 13 to 19 stations. At these stations, surface samples were taken for nutrient and chlorophyll a analyses (see Sect. 2. 2) using a rosette sampler equipped with a conductivity-temperature-depth (CTD) instrument and Niskin bottles.
Variable fluorescence Variable fluorescence was measured with a FastOcean fast repetition rate fluorometer (FRRf) and Act2-based laboratory system (Chelsea Technologies Group Ltd, UK). The temperature was controlled by connecting a LAUDA Ecoline cooler (LAUDA-Brinkmann, LP. , USA) to the water jacket of the Act2 system. The acquisition protocol consisted of 100 excitation flashes with a flash pitch of 2 µs and 40 relaxation flashes with a flash pitch of 60 µs. Excitation flashes were performed with the blue LED (450 nm), and the strength of the LEDs was automatically adjusted to the phytoplankton concentration by the FastPro software. A loop of simultaneous blue and green flashes (450 nm + 530 nm) was performed after the acquisition loop of only blue LEDs in the case that the blue LEDs were not able to reach saturation (for instance, with high cyanobacteria concentrations), but as this was not the case, only the parameters measured by blue LEDs were used for further calculation.
Gross primary productivity (GPP) was estimated by fitting JV PII (µmol photons m -3 h -1 ) to Eq. (  1 ) (the exponential model of  Webb et al. , 1974)  to derive a volumetric P max and α. GPP (µg C L -1 h -1 ) was then calculated using Eq. (  1 ) and incident surface irradiance. To avoid the effects of changing incident surface irradiance (E surface ) on the spatial pattern and to be able to compare GPP between regions we used monthly average surface irradiances (E surface ) in our calculations of primary productivity. From 2010 to 2016 irradiance (400-700 nm) was measured at the roof of the NIOZ building in Yerseke using an LI-190 quantum photosynthetically active radiation (PAR) sensor and hourly averages stored using an LI1000 datalogger. E surface was then calculated by averaging all irradiance data from the years 2010-2016 for the respective month.
The chosen cluster criteria were based on expert judgment  (SeaDataNet, 2018)  and corresponding to other studies  (Sieburth et al. , 1978; Vaulot et al. , 2008). The clustering was done using the software Easyclus 1. 26 (Thomas Rutten Projects, the Netherlands) according to these criteria. Noise, air bubbles, and other potential outliers were removed. The acceleration of the particles in the sheath fluid positions the cells along their long axis, which allows for size estimation based on the FWS pulse shape. A linear relation was found between length FWS and the measured length of diverse phytoplankton species (length FWS = 0. 92•measured length-1. 57; R 2 = 0. 98; Rijkeboer, 2018). Size estimation is limited by the width of the laser beam (5 µm), so estimations of cell sizes <5 µm are not possible based on the FWS.
Data analysis Outliers of the complete dataset were removed after visual inspection of pair plots made with the pair-plot function of the HighstatLib. V4 script  (Zuur et al. , 2009). For the FRRf data, quality control of the FLC fits was done based on the quality ratio of the induction curve fit per FLC light step and the r 2 of the FLC fit. The quality ratio of the induction curve fit was calculated as the ratio of F v or F v to the standard error (SE) of the linear regression of the saturation phase. FLC fits with an r 2 <0. 75, or with over 30 % of the data points with a quality ratio below 6, were visually inspected and removed based on expert judgment. This led to the removal of 1 % to 7 % of the FLC fits per month. Unsatisfactory fits occurred when the auto-LED settings misadjusted the maximum irradiance or when fluorescence was too low to retrieve a reliable fluorescence signal.
Especially at low biomass, FLCs became noisy, and therefore a minimum fluorescence signal was set for calculations of photosynthetic parameters. Below this blankcorrected instrument-specific fluorescence signal F q /F m became noisy and often reached above the biologically unlikely limit of 0. 65  (Kolber and Falkowski, 1993). The datasets of the high-resolution measurements (FRRf, FCM, and Ferrybox) were linked using corresponding time stamps. When multiple measurements were performed within one FLC, the average was used. To test whether environmental conditions (as present in the different months) had a significant effect on fluorescence as a predictor for Chl a concentration, an analysis of covariance (ANCOVA) was performed with the month as a factorial predictor. To find regions with similar phytoplankton communities, data were spectrally clustered using the uHMM R package  (Poisson-Caillault and Ternynck, 2016)  in the statistical software R (version 3.
4. 1; R Core Team, 2017). The package default settings normalize data before clustering and automatically find the number of clusters based on spectral classification and the geometry of the data. This new methodology is more robust than the classical hierarchical and k-means technics  (Rousseeuw et al. , 2015). Phytoplankton parameters were first tested for collinearity and predictors with a variance inflation factor (VIF) over 6 were removed  (Zuur et al. , 2009 ; see the Supplement for pair plots). This left for the cluster analysis the FCM parameters pico-red, nano-red, micro-red, and Synechococcus and the FRRf parameters σ PSII , F v /F m , a LHII , 1/τ , and E k. Data points were then labeled per cluster and plotted on a map to visually identify regions. Principal component analyses (PCAs) were performed to find which variables contributed most to the cluster results.
The PCAs were based on correlation matrixes with scaled parameters to correct for unequal variances and were carried out with the prcomp() function in R (version 3. 4. 1; R Core Team, 2017). The PCA visualization was done using the supplemental R package factoextra (Kassambra and Mundt, 2017). Maps were made using QGIS v2. 14. 2, and other figures were made with ggplot2 in R  (Wickham, 2009). 3 Results.
This suggests that cluster 11 was a temporal cluster instead of a spatial cluster. To test this we repeated the analysis for the month of August but only including the measurements performed within an 8 h timeframe around noon (12:00 ± 4 h LT; Fig. S4 ). In this timeframe, the southern coastal zone was distinct from the rest of the Dutch North Sea and corresponded to cluster 10 in the analysis of the complete dataset (Fig. 7d ), so this cluster was defined by spatial variability. Clusters 12 and 13 were grouped together in the 12:00 ± 4 h timeframe as cluster 1. Cluster 11 was not recognized as a cluster within the 12:00 ± 4 h timeframe, so it indeed seemed controlled by temporal rather than spatial variability.
Discussion The objective of this study was to evaluate the added value of FRR fluorometry and flow cytometry for monitoring purposes. During four cruises spread over 5 months, a wide variety of environmental conditions and phytoplankton community states were sampled. Here, these data are used to evalu-Figure  7. Overview of the spectral cluster analysis based on the non-collinear phytoplankton parameters (FCM: pico-red, nano-red, microred, Synechococcus; FRRf: σ PSII , F v /F m , a LHII , 1/τ , E k ) separated per month (a to d: April, May, June, and August). On the left the clusters are visualized on maps and in the middle are the bi-plots of the PCA of the data with confidence ellipses per cluster (confidence 95 %). In all graphics clusters are visualized by different colors as shown in the legend inset.
For the confidence ellipses the border lines (and not the fill) correspond to the clusters. In the bi-plot overlapping confidence ellipses suggest a high similarity between groups, while the size of the ellipse is a measure of variability within the group. On the right is the PCA table with contributions in percent of the different variables; in bold are the three variables that contribute most to the principal component. Maps were made with QGIS v2. 14. 2 using EEZ zone data from the Flanders Marine Institute (2018). The PCA visualization was done in R using the supplemental R package factoextra  (Kassambra and Mundt, 2017). ate the potential of this approach to be developed as a novel method to improve existing monitoring (OSPAR, MSFD). Biomass is an important parameter to understand the role of phytoplankton in the ecosystem and biogeochemical cycles.
Its direct measurement using high-resolution methods is challenging. Chlorophyll a concentration is often used as an estimate for biomass, although the carbon : Chl a ratio is dependent on abiotic conditions and species-specific phenotypic plasticity, and chlorophyll a is therefore not directly related to biomass  (Flynn, 1991 (Flynn, , 2005;; Geider et al. , 1997; Alvarez-Fernandez and Riegman, 2014; Halsey and Jones, 2015). In this study, chlorophyll concentrations are estimated by red fluorescence, which results in a good fit for both the FRRf (adjusted R 2 = 0. 66) and the FCM (adjusted R 2 = 0. 90). The impact of abiotic conditions on fluorescence as a predictor for chlorophyll a content was tested by comparing the relationship in the different months. Only the flow cytometer data were significantly affected by environmental conditions.
(Rijkeboer, unpublished). Marrec et al. (2018)  manually separated up to 10 phytoplankton groups from the data of the CytoSense flow cytometer. Yet, most of these groups comprise many taxonomic genera, which, apart from the size or pigment composition, hinders further interpretation of their role in the ecosystem or biogeochemical cycles. However, the distinction between different pigment groups can provide useful information on food web functioning. Chlorophyll c containing algae (Chromista) contain long-chained essential fatty acids like docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), which are lacking in green algae (some Prasinophyceae excepted) and cyanobacteria  (Dijkman and Kromkamp, 2006). Thus, information about food quality can be obtained from FCM. For the detection of nuisance phytoplankton, distinct clusters are lacking.
Nevertheless, low-resolution methods remain a necessity for more detailed taxonomic analysis, extrapolation over the entire water column, and to calibrate and correct for blanks. Data analysis is a challenge when implementing high-resolution methods, whereby cluster methods could simplify and standardize analysis. The cluster analysis of flow cytometric data has potential for improvement to increase the informative value of the method. The identification of phytoplankton clusters with a functional quality, such as nitrogen fixers, calcifiers, DMS producers, or clusters with high food quality, would particularly be helpful for the interpretation of ecosystem dynamics and biogeochemical fluxes. Regarding the FRRf, the main challenge is converting the electron transport rate to gross primary productivity in carbon units.
Acknowledgements. We want to thank the captain and crew of the RV Zirfaea and the shipboard Eurofins employees for their hospitality and great help during the cruises. We thank  Annette Wielemaker  for assistance with the GIS maps of primary production,  René Geertsema  for assistance with the flow cytometry data analysis, and  Ralf Schiebel  for useful comments on the paper. Furthermore, we would like to thank the  Rijkswaterstaat laboratory  for conducting the nutrient and chlorophyll measurements and  Rijkswaterstaat  for the opportunity to perform measurements alongside the regular monitoring program. Financial support. This research has been supported by the  European Union 's  Horizon 2020 research and innovation program  (grant no. 654410 ,  JERICO-NEXT ). The article processing charges for this open-access publication were covered by the  Max Planck Society.
Data availability. Data are publically available at 4TU. ResearchData: https://doi. org/10. 4121/uuid:84406786-219d-4f33-a60a-a0f5301032d0 (Aardema et al. , 2019). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/os-15-1267-2019-supplement.
Our analysis includes derivations of analytical expressions for the variances and wavenumber spectra of the errors of the derived variables, which will be useful to other studies based on simulated SWOT and WaCM estimates of velocity and vorticity. We also discuss limitations of the geostrophic approximation that must be used for SWOT estimates of velocity. The errors of SWOT and WaCM estimates of velocity and vorticity at the full resolutions of the measured variables are too large for the unsmoothed estimates to be useful. It will be necessary to smooth the data to reduce the noise variance. We assess the resolution capabilities of smoothed estimates of velocity and vorticity from simulated noisy SWOT and WaCM data based on a highresolution model of the California Current System (CCS). By our suggested minimum threshold signalto-noise (S/N) variance ratio of 10 (a standard deviation ratio of 3.
Introduction Presently available global sea-surface height (SSH) fields constructed from satellite altimeter data by Collecte Localis Satellites (CLS) and archived by Archivage, Validation, Interprétation des données des Satellite Océanographiques (AVISO) are able to resolve time scales of about a month and wavelength scales of about 200 km, corresponding to feature radius scales of about 50 km (see Appendix A. 3 of  Chelton et al. , 2011). The 25+ year CLS/AVISO record of SSH fields  (Pujol et al. , 2016)  has been extremely useful for studies of mesoscale eddies, large-scale ocean circulation variability and sea level rise (e. g. ,  Fu et al. , 2010; Lee et al. , 2010; Willis et al. , 2010; Chelton et al. , 2011). Notwithstanding the many successful applications of this dataset, it is clear from high-resolution numerical simulations and satellite infrared and visible observations of sea surface temperature (SST) and ocean chlorophyll that energetic variability exists on smaller scales than can be resolved by the CLS/AVISO SSH fields.
The improved resolution that is anticipated from the two satellite technologies considered in this study is likely to advance understanding of mesoscale variability in a manner similar to the advances achieved from the CLS/AVISO multi-altimeter merged SSH dataset compared with the single-altimeter observations obtained from TOPEX/Poseidon (see, for example, Fig. 1  of  Chelton et al. , 2011). Our particular interest here in satellite estimation of surface velocity and vorticity is motivated in part by their effect on local wind-driven vertical velocities (Ekman pumping). It is well known that surface currents contribute to Ekman pumping in mesoscale eddies through their effects on the relative wind and hence the surface stress. The surface velocity within a rotating eddy generates a curl of the surface stress with sign opposite that of the vorticity of the eddy, thus attenuating the eddy and generating an Ekman pumping velocity that is often larger on mesoscales and smaller scales than that from the curl of the largescale background wind stress field (e.
The smaller footprint size for WaCM will be achieved with a larger antenna size and the use of a , rather than the Ku-band radar (13. 4 GHz) used for QuikSCAT or the C-band radar (5. 3 GHz) used for ASCAT. This study considers only the surface ocean velocity estimates. Our baseline simulations of WaCM measurements of surface currents assume a swath width of 1200 km. Recent engineering studies suggest that it may be possible to broaden the swath width to 1800 km, which would greatly improve the sampling compared with the simulations of WaCM data in this study. The benefits of the wider swath width are investigated as part of this study. The viability of SWOT and WaCM estimates of surface velocity for investigation of small-scale variability clearly depends on the signal-to-noise ratios of the measurements (SSH for the case of SWOT and surface velocity for the case of WaCM).
For SWOT, the utility of the data also depends on the validity of the geostrophic approximation. The objectives of this study are to investigate these issues and assess the resolution capabilities for mapping of the surface velocity and vorticity fields for both SWOT and WaCM. The approach is based on simulated SWOT and WaCM sampling of, respectively, the SSH and surface velocity fields from 30 days of twice-daily snapshots from a high-resolution model of the California Current System (CCS). Our focus is on the limitations imposed by the baseline science requirements for the cross-swath averaged standard deviations of uncorrelated instrumental measurement errors. In reality, the instrumental errors for both SWOT and WaCM vary across the measurement swaths (see Fig. F1  in Appendix F for the case of SWOT), with smallest errors near the centers of the swaths and increasing errors towards both edges of the swaths.
The measurement errors also depend on the significant wave height (see again Fig. F1  for the case of SWOT). The significant wave height (SWH) dependence and cross-track variations of the instrumental errors are not considered in the simulations presented in this study. Rather, we consider the cross-swath average instrumental measurement errors for the conditions of 2-m significant wave height that are specified for the baseline design requirements of the measurement errors. We note that the error characteristics of SWOT data could have been simulated more accurately and completely for this investigation by using the simulator software available from the SWOT Project Office  (Gaultier et al. , 2017 ; see also  Qiu et al. , 2016; and Gaultier et al. , 2016). Simulator software is not available for WaCM.
, 2010). The procedure consists of off-line, one-way nesting from larger to finer scales without feedback from the child grid solution onto the parent grid. It is implicitly assumed that a numerical "zoom" around a specific phenomenon is valid when it has an essentially local dynamical behavior, albeit with important influences from its environment of basin and regional circulation. Bottom topography was defined from the SRTM30-plus bathymetry dataset based on the 1-minute  Smith and Sandwell (1997)  global dataset but using higher resolution data where available. A Gaussian smoothing kernel with a width of 4 times the topographic grid spacing was applied to avoid aliasing wherever the topographic data are available at higher resolution than the computational grid. The maximum depth for all grids was set to 6000 m, which is not a serious distortion for the U.