dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank the patients and their families and caregivers for participating in this study, along with all investigators and site personnel. We thank  Ying Zhang, PhD  for her statistical expertise. Medical writing assistance was provided by  Dorothy McCoy , PharmD, BCPS, BCIDP and editorial assistance by  Carol Zecca , BS, both of  Merck & Co. , Inc. , Rahway, NJ,  USA.
The third state most commonly develops in accretionary sequences when the horns of pre-existing crescentic bars weld to the beach  (Wright & Short 1984). The configuration of Stanwell Park Beach can quickly change, with offshore bars migrating shoreward and welding to the beachface within days to months. From our dataset we estimate the beach closure depth being 13. 6 m. The NSW coast experiences a swell regime characterized by a highly variable wind-wave climate superim-posed on persistent long-period South-East swell 60% of the time. The wave climate can be defined as high energy with an average wave period of 7. 98 s and average wave height of 1. 59 m  (Short & Trenaman 1992). During winter, storms can occur with significant wave heights of up to 5. 5 m and 9. 2 m with return periods of approximately 1 year and 50 years, respectively.
Beach surveying The topography data were collected using a RTK-GPS in a three-dimensional system. GPS is a satellite-based positioning system developed by the U. S. Department of Defence to provide continuous, worldwide, all-weather navigation  (Morton et al. 1993)  but the accuracy of a standard GPS is not good enough for beach surveys. RTK-GPS, however, uses more sophisticated error correcting techniques that makes it suitable for surveying beach dynamics  (Harley et al. 2011a). In RTK-GPS, a computer box calculates its position relative to a fixed, known GPS receiver. Both receivers measure their position using the higher frequency satellite signals  (Harley et al. 2005). As the base station's coordinates are known, it can then calculate the degree of errors in the GPS measurements. These error corrections are transmitted in real-time to the computer box, which can then calculate its absolute position with accuracy in the order of a few centimetres.
Offshore wave data Wave data used for wave forcing analyses were obtained from the Port Kembla directional waverider buoy located in 80 m water depth, 12 km offshore of Port Kembla and 26. 5 km south of Stanwell Park Beach (Figure  S2 ). Data is provided from the Manly Hydraulics Laboratory (MHL) and are collected under the NSW Coastal Data Network Program managed by the Offce of Environment and Heritage (OEH). The buoy takes hourly measurements of the significant wave height (H s ), zero crossing period (T z ), wave direction (θ )maximum wave height (H max ) as well as peak wave period (T p ). In a previous study  (McCarroll et al. 2016 ), a good correlation in wave heights was found between Sydney wave buoy and Port Kembla wave buoy suggesting both are representative of wave conditions offshore of the beaches between these two buoys.
Failure of the Port Kembla buoy occurred during the study period which is why wave data is incomplete (Figure  2 ). It fails 2 days during P3 (Table  2 ) and 5 days during P5. In order to analyze the swell condition during the study period, we look for storm events in our wave dataset. A storm event is an event where H s exceeds the 0. 95 quantile  (Masselink et al. 2014)   (Dail et al. 2000). The greater the value of F b , the stronger will be the impact of the waves on the beach. This indicator can be calculated by: F g H s T 16 2 b mean 2 r t = (2) Where F b is the incident energy flux, ρ = 1025 kg/m 3 is water density, g = 9. 81 m/s 2 is gravitational acceleration, H s is the significant wave height in meters, T mean is the average period in seconds.
Rainfall data We obtained rainfall data from the Australian Government Bureau of Meteorology (BOM). The station used for this study is Darkes Forest rainfall station, located 6. 3 km to the west of Stanwell Park Beach (Figure  S2 ). The elevation of the station is 370 m. Rainfall is linked to discharge of the creeks which can contribute to the opening of the two lagoons on the beach and therefore can play a role in the morphology of the study site. Opening of the lagoons during heavy rains can introduce sediment to the surf zone.
Beach rotation and sediment volume The values of the alongshore component of wave energy flux (Figure  8a ) are high and positives during P1, P4, P7 and P8, with a maximum of 85 kW/m during P4. These values indicate that the southern end of the beach moves offshore. However the field observations and topographic maps show only a beach rotation between P1, P2 and P8. As a consequence, this index, for the present study, is not completely related with the data and field observations. One reason could be that the database is too short (only 4 months) to encompass significant results. Others studies showing good results for this beach rotation proxy use databases spanning years and do not use RTK-GPS to study beach dynamics  (Komar & Inman 1970 , Van de Lageweg et al. 2013 , Biausque et al. 2016).
Large (positive or negative) values of mean alongshore component of radiation stress are related to episodes of beach rotation. For all periods the value of this parameter is close to zero except for P2 and P8 which had a value of -86 N/m and -48 N/m, respectively (Figure  8b ). These values indicate that during P2 and P8, a beach rotation event occurs while during all the others periods cross-shore sand transport is dominant. This is rather consistent with topographic maps observations during P2 and P8. For our study, the mean alongshore component of radiation stress is in better agreement with our topographic observations and hydrodynamic database than the alongshore component of wave energy flux, that do not behave as expected. The different observations described previously must be related with subaerial beach volume calculated from RTK-GPS surveys.
DISCUSSION This study examines topographic and hydrodynamic data for Stanwell Park Beach between February and May 2016. Our data only cover the subaerial section of the beach therefore we could not study the sub-tidal extents of the beach. As a consequence, the total sediment budget approach could not be analyzed and thus the total geomorphic response could not be fully described. In terms of hydrodynamics it is possible to note that the study period was calm without large storm events. These calm wave conditions are related to ENSO, which decrease the incidence of tropical cyclone activity off eastern Australia during what is normally the maximum activity period (February-March), causing the observed relative decrease in wave power impacting the Sydney region during this study  (Phinn & Hastings 1992).
Rip current channels and rip current embayments are well known morphologies that have already been studied in Australia  (Brander 1999 , McCarroll et al. 2016)  and in other places of the world  (Castelle et al. 2015 , Borra et al. 2022 , Wernette & Houser 2022). Based on the observations of this study, Figure  11  summarizes the behaviour of Stanwell Park Beach under the wave conditions of our study period. This representation does not support the general behaviour of Stanwell Park Beach but only reflects the behaviour of the subaerial beach during the four months of the study period. The representation of the behaviour of the beach shows only erosion phase but Stanwell Park Beach does not suffer from chronical erosion, the accretion phases were not captured by our short-time dataset. To calculate P y and S xy we use deep-water wave data.
We have checked that the impact of evaporation (which tends to increase the tracer surface concentration) is not significant: we made a rough estimate of the ratio of the change of surface concentration over the surface concentration, averaged over the area of interest, and found that it varies around 4‰ and does not exceed 15‰. This is much smaller than the impact of the perturbations on the wind presented in the next sections and is therefore neglected in the rest of the paper. The long reference simulation (GOT_REF) has been assessed with several datasets  (Piton et al. , 2021; Nguyen-Duy et al. , 2021; Nguyen--Duy, 2022 ) and shows adequate skills in reproducing the ocean state, both at the surface and sub-surface. Nothing indicates a major deficiency in the model or configuration. Therefore, in this paper, we do not duplicate the assessment published earlier but analyze the sensitivity of the simulations to wind forcing uncertainties.
They found that in their area of study, the value 30% is usually an underestimation, especially for the high modes. In this study, we use ASCAT wind products under the following assumption: the difference between ECMWF and ASCAT winds can be used as a proxy of the uncertainty on ECMWF fields. This allows us first to estimate ε, as described below, and then to verify the generated wind perturbations ensemble as presented in Section 2. 3. The ASCAT product distributed by EUMETSAT (EUMETSAT, OSI SAF, 2021) contains level 2 wind data, retrieved from the Advanced Scatterometer (ASCAT) on MetOp-B satellite, at 12. 5 km sampling resolution (with an effective resolution of 25 km). It is available daily and can be downloaded from the JPL website: https://podaac. jpl. nasa. gov/dataset/ASCATB-L2-Coastal.
The data are downloaded for the period from 01 May to 31 August 2015. The ASCAT User Manual indicates that the accuracy is better than 2 m/s in wind component standard deviation with a bias of less than 0. 5 m/s in wind speed. Since ASCAT data are provided on different tracks on different days, they are first linearly interpolated onto the ECMWF grid. Then, the daily fields where less than 50% of the data are valid over the study region are filtered out; over the 123 days of this period, only 72 days (~58. 5%) are used for further calculation. To estimate ε, our approach is much simpler than the one of  Pasmans and Kurapov, (2019)  and does not introduce modal dependency. Since ε is built as the ratio between the standard deviation of uncertainty on the wind velocity and the standard deviation of the wind, then here ε is computed as the standard deviation of the difference between ECMWF and ASCAT data (std(diff)) divided by the standard deviation of the ECMWF field (std(ECMWF)).
Locally, in the center of the gulf for U w and south of 19 • N for V w , the variance of the misfits is lower than 4 m 2 /s 2 , meaning that the righthand side term of Eq. (3) (see Appendix) is negative, and Eq. (  3 ) cannot hold. This suggests that ASCAT errors are overestimated there or that ECMWF errors have a non-zero mean over the period of study (contrary to our hypothesis). With ENS_COAST, the ECMWF errors are better represented than in ENS_REF close to the Vietnamese coast (not shown) but, as ENS_REF, the ensemble is underdispersive. Another way to assess the ensemble is to check its ability to represent the wind conditions from different observational datasets. Here, wind data (hereafter WIND_ISLAND) is provided by Viet Nam Meteorological and Hydrological Administration. These data contain the wind speed measured at 10m every 6 h; the instrumental error is not reported.
We use observations from 3 stations, located on islands (Fig. 1B ): Bach Long Vy (~120 km from the coast), Hon Ngu (~4 km from the coast), Hon Dau (~1 km from the coast). WIND_ISLAND is used as a 'truth' value to validate the wind ensemble. Since WIND_ISLAND represents the wind velocity at a station, while the ensemble wind represents that in a grid point, WIND_ISLAND velocity is compared to the ensemble wind velocity inside a box of 0. 25 degrees around the station. We also use ASCAT wind speed data at the closest point to the station within 0. 25 degrees. In Bach Long Vy, most of the time (71 over 85 days), ENS_REF is able to span the full range of the WIND_ISLAND (Fig. 5A ). This is likely due to the fact that the wind data from Bach Long Vy is assimilated into the ECMWF forecasting system.
5B ). The effect of the stronger perturbation near the coast is clearer, with the larger ensemble range shown in ENS_COAST. The number of days that the WIND_ISLAND stays within the ensemble range is 71 (for ENS_REF) and 77 (for ENS_COAST). Within the 1st-3rd quartile, the numbers of days are 18 and 20, for ENS_REF and ENS_COAST, respectively. The ASCAT data availability is limited here since the station location is adjacent to the coast. In all three data points, the wind speed from ASCAT overestimates WIND_ISLAND, however, it is still lower than the upper value of the ensemble range. In Hon Dau, ECMWF and WIND_ISLAND wind velocities show similar variations, however, ECMWF data overestimate the observed one (Fig. 5C ). Compared to other stations, the number of days when the WIND_ISLAND falls between the ensemble range is lower, reaching 63 (for ENS_REF) and 67 (for ENS_COAST), which correspond to 74% and 79% of the total days.
The limited performance of the ensemble in this station can be explained by the underdispersion near the coast, as evidenced in the variance budget analysis. Similarly, due to the location of the station, most of the time, ASCAT data is not available in Hon Dau. Overall, although both ensembles are underdispersive near the coast, they still show some skill at reproducing the wind uncertainty. Firstly, the ensemble can exhibit sharpness, meaning that the spread is small where the uncertainty is small and vice versa. Here, the range of the wind perturbations is smaller offshore and larger near the coast, which is consistent with the spatial error patterns estimated by ECMWF and ASCAT. Secondly, there is a bias between the two observational data sources (ASCAT and WIND_ISLAND), however, most of the time (at least 74%), the WIND_ISLAND amplitude stays within the ensemble range.
Discussion on the use of the ensembles for data assimilation Ensemble statistics are used as proxies of model errors in ensemblebased data assimilation methods, such as the Ensemble Kalman Filter (e. g. Evensen, 2003; Sakov and Oke, 2008). In this section we propose a preliminary study of the possible use of ENS_COAST in the case HF radar observations were assimilated into the model to constrain the plume. We first evaluate the consistency between the ensemble and the misfit between the observations to be assimilated and the model, within the observations uncertainties. Then we analyze the influence function of a single observations onto several model variables. We use surface velocity data from the high-frequency radar (hereafter HFR) system based on two antennas located at 18. 62 • N and 17.
(2023)  find that the wind variability does not impact the near field of the Gironde River plume. At last we estimate the impact of the wind uncertainties on the freshwater transport by the coastal current. We found that the sensitivity of the transport is large, sometimes almost as large as the transport itself; this suggests that the transport estimate from a single simulation is not a robust quantity with respect to uncertainties in the wind forcing. We also show that the uncertainties on the transport are mainly due to uncertainties on the surface current, at least in the section studied. In the prospect of assimilating HFR observations, we assessed the ensemble consistency with respect to the HFR data and found that the range of values reached by the model in both ensembles (ENS_REF and ENS_COAST) and by the HFR data (given their uncertainties) are not disjoint (at least not always and not everywhere); however, it is likely that the model suffers from systematic errors that are not represented by the ensemble (by construction).
Acknowledgements The Ph. D. thesis of  Tung Nguyen-Duy  has been funded by the  "Programme d'Excellence"   of the French Embassy of Vietnam  and by  IRD ARTS program. This work is a contribution to the  LMI LOTUS  project (http://lotus. usth. edu. vn) and to the  SWOT COCTO -FO project funded by  CNES (French Spatial Agency). First we gratefully thank  P. Marsaleix  ( LEGOS ) for his help with the SYMPHONIE model and the modelling strategy. Many thanks as well to  M. Ghantous  ( CLS ) for discussions at the beginning of this work on the ensemble generation. We thank  Manh-Cuong Tran  and  Alexei Sentchev  ( LOG, France ) for providing the mapped HFR data. This study has been conducted using ECMWF products (www. ecmwf. int) and E. U. Copernicus Marine Service product (GLOB-AL_ANALYSIS_FORECAST_PHY_001_024, available at https://resources.
Methods This was a retrospective non-interventional study of patients with a record of NDD-CKD in the Echantillon Generaliste des Beneficiaires (EGB) database between January 01, 2012, and December 31, 2017. The primary objective was to estimate the annual incidence and prevalence of anemia of NDD-CKD. Secondary objectives included description of the demographics and clinical characteristics of patients with NDD-CKD-related anemia. An exploratory objective was to use machine learning to identify patients from the general population that might have NDD-CKD but without a recorded ICD-10 diagnosis of CKD.
Results During 2012-2017, 9865 adult patients in the EGB database had confirmed NDD-CKD; of these, 49. 1% (4848/9865) had anemia. From 2015 to 2017, estimates of incidence (108. 7-114. 7 per 1000 population) and prevalence (435. 7-449. 5 per 1000 population) of NDD-CKD-related anemia were stable. Less than half of patients with anemia of NDD-CKD were treated with oral iron, and approximately 15% were treated with erythropoiesis-stimulating agents. Based on adult French population projections in 2020 and an estimated prevalence rate in 2017 of 42. 2 per 1000 population for confirmed plus possible NDD-CKD (as a proportion of the general French population), the estimated number of patients with possible NDD-CKD in France was 2,256,274, approximately five-fold greater than the number identified by diagnostic codes and hospitalizations.
However, developing disease management strategies at the population level requires reliable estimates of prevalence. Globally, and in France in particular, there is a paucity of data exploring the prevalence of NDD-CKD and related anemia in the general population; consequently, the lack of reliable prevalence estimates complicates the development of corresponding management strategies at the population level. Prevalence of anemia has been shown to increase with CKD progression  [10] ; however, this has not been evaluated extensively in French NDD patients with anemia of CKD". To provide greater insight into NDD-CKD and associated anemia in France, this retrospective study investigated the epidemiology and treatment of anemia among NDD-CKD patients. This study used real-world data from Echantillon Generaliste des Beneficiaires (EGB), a health insurance database to act as a representative sample of the healthcare consumption of the French population.
The EGB is a well-established and extensive resource for epidemiology studies in France, particularly for those focused on the long-term research of non-rare diseases  [16] [17] [18] [19] [20]. However, in the absence of a confirmed CKD diagnosis, there is an absence of methodology or data in the EGB database to identify CKD patients from their biological results and outpatient medical diagnoses are not captured. Consequently, a machine learning-derived algorithm was also applied to estimate the number of patients in France who may have undiagnosed NDD-CKD and related anemia.
Ethics EGB is a medico-administrative database of insured persons, and all patient-level data used for this retrospective analysis were collected as part of routine diagnosis and treatment. A unique anonymous identification number was associated to each insured person; as such, all data were fully anonymized prior to access and inclusion in this study. The Health Data Hub (a French public structure), and an independent scientific committee approved the access and use of the EGB to conduct this study on April 7, 2020 (dossier n˚778539). Informed consent was not sought.
Study design and patients This was a non-interventional, retrospective database study in France using the EGB database, which constitutes a 1/97 th random sample from the Système National d'Information Interregimes de l'Assurance Maladie/Système National des Donnees de Sante(SNIIRAM/SNDS) database  [21] , selected through the National Insurance Recording System. Access to the EGB does not require approval from the Commission Nationale de l'Informatique et des Libertes as there is a simplified process in place that requires approval from an independent scientific committee and the Health Data Hub  [22]. The SNIIRAM/SNDS database includes all ambulatory care, hospital stay reimbursement data and death-related data nationwide in France  [23]. Furthermore, the SNIIRAM/SNDS database links, at the patient level, data for all outpatient-reimbursed health expenditures and hospitalizations in all public and private hospitals.
Currently, the SNIIRAM/SNDS database covers 98. 8% of the French population and provides information on patient demographics, long-term diseases, universal health coverage, clinical events, procedures, medicine prescriptions, laboratory test dates, hospitalizations, and cost coding  [21, 23]. The SNIIRAM/SNDS database does not include laboratory test results, coding for physician specialties, most inpatient drug records, or outpatient diagnoses. The study inclusion period was from January 01, 2012, to December 31, 2017. Eligible patients were adults aged �18 years with confirmed NDD-CKD, defined as �1 hospitalization with a CKD diagnostic code and/or those who were covered �1 day with a long-term disease registration ("affection de longue duree" [ALD]) for CKD diagnosis, during the inclusion period.
For the main statistical analysis, patients were followed retrospectively (lookback period) for the maximum time available between their ID and January 01, 2011 (Fig  1 ). Data from 2011 onwards were used to ensure a minimum lookback period of 1 year for each patient to assess whether their anemia was incident or prevalent. Further details to ensure that the EGB database would provide a sufficient sample size for our study are provided in the Supplementary Methods section in S1 File.
Data extraction and statistical analyses Data were summarized descriptively, and no significance testing was performed. Data analysis incorporated feedback from an expert scientific committee that included a pharmacist, a nephrologist, and a coding clinician. Demographic and clinical characteristics (sex, presence of a CKD long-term disease, CKD stage, diabetes, and cardiovascular disease status) were reported as categorical variables. Age was described as a continuous variable. For the primary analysis, an incident NDD patient with anemia of CKD was defined as a patient with a first marker of anemia during the follow-up period and without a prior marker of anemia since 2011. Once a patient was identified as anemic in a given year, the patient was considered anemic for the rest of the follow-up period.
Sensitivity analyses were conducted to calculate incidence and prevalence rates using a 3-year lookback period prior to the calendar year of analysis. To estimate incidence and prevalence among the general population in France, the results of the French 2020 census were used. Epidemiological endpoint analyses were repeated for the pooled "confirmed + possible" NDD-CKD group identified by the machine learning tool. Route of iron administration (intravenous/oral) and ESA type were described. The occurrence of and time to disease progression were estimated using Kaplan-Meier methods. Patients were censored after 5 years of follow-up. No missing data were identified, as the EGB database included all incidents of reimbursed healthcare. Data extracted in the EGB for the study were subject to quality control and consolidation prior to being made available.