dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations This study was performed on the sequencing data from a single region, England. This is justified by the fact that the sequencing dataset associated to England on the GISAID open-source genome repository is by far the largest and most uniform compared to other countries/regions. Biases relative to specific data-taking practices un England may induce biases in the analysis. To validate the results, however, we have also analysed the data for Wales and Scotland, as presented in the supplementary material. We chose the other two nations of the Great Britain island because they have a very similar epidemiological history compared to England, thus we would expect comparable outcomes. As such, by comparing the results we would test the reliability of the ML procedure alone. In fact, the results for Wales and Scotland, while less significant with respect to statistics, show the same patterns we obtained for England.
Data collection The measured data used in this study to calibrate and validate the model, as well as to determine the upstream limit conditions, include river discharge (Q), salinity (Sal), suspended particulate matter (SPM), and water quality variables, i. e. , phosphate (PO 4 ), ammonium (NH 4 ), nitrate (NO 3 ), dissolved silica (DSi), dissolved oxygen (DO), total (particulate and dissolved) organic carbon (TOC), and chlorophyll a (Chl a), as indicators of phytoplankton biomass. Daily river discharge data were obtained from the national Banque Hydro database (http://www. hydro. eaufrance. fr/, last access: 21 August 2020). Water quality variables were collected from the following national databases: (1) the French Water Agencies through the NAIADES portal (http: //naiades. eaufrance. fr/, last access: 17 March 2020), for the physicochemical parameters of surface waters, (2) the REPHY (REPHY, 2021) database (https://www.
seanoe. org/ data/00361/47248/, last access: 20 April 2020), specifically for the monitoring of phytoplankton for the estuarine and marine parts, and (3) the SOMLIT database (https://www. somlit. fr/, last access: 23 June 2020), for coastal and estuarine water quality parameters. The temporal resolution of the water quality data acquired is generally monthly or bimonthly. Chl a concentrations were usually available only from March to September (main phytoplankton growth period). The gauging stations and water quality stations are located in Fig. 1b. The data for the marine boundaries were extracted from outputs of simulations performed by a coastal marine model ECO-MARS3D  (Cugier et al. , 2005b ; Lazure and Dumas,.
Model description and setup C-GEM is based on the premise that geometry and hydrodynamics exert first-order control on the estuarine transport and biogeochemical processes  (Volta et al. , 2014). It uses idealized geometry (defined by the estuarine width at the mouth, convergence length, and channel depth profile; see  Volta et al. , 2014, for details)  and hydrodynamics (such as river discharge and tidal amplitude) that can be gained from remote sensing images using Geographic Information Software (GIS) and readily available data sets (Table  1 ). The biogeochemical reaction network includes SPM settling and erosion, the air-water gas exchange for oxygen (O 2 ) and carbon dioxide (CO 2 ), nitrification, denitrification, primary production, phytoplankton mortality, and the aerobic degradation of organic matter (see the Supplement for detailed descriptions and mathematical formulations, according to  Volta et al.
Forcings and boundary conditions C-GEM is constrained by a set of riverine and marine boundary conditions. The riverine boundary conditions include river discharge (Q), SPM, and the state variables of water quality concentrations, which were linearly interpolated to obtain the daily values between the adjacent available measurements (Table  3 ). The marine boundary conditions include water elevation, water temperature, salinity, and the same water quality variables extracted from ECO-MARS3D at an hourly temporal resolution that allows the capturing of the tidal cycle. SPMs for the marine boundary conditions were collected from REPHY (REPHY, 2021) and linearly interpolated to obtain the data at the required timescale (Table 3). The annual means of boundary conditions are summarized in Table  4.
In this study, the Dordogne river, a tributary of the Gironde estuary, which contributes ∼ 40 % discharge to the estuary,  was considered as an upstream condition. The closest available sampling station (no. 5026000 from NAIADES) on the Dordogne river is around 32 km upstream from the confluence and in a stretch influenced by the tide. The inputs from the Dordogne tributary include daily Q and the same water quality variables (listed in Table  3 ) for riverine boundary conditions. Intermittent observed water quality data were also interpolated to daily values.
Model calibration The data used to calibrate and validate the model were obtained from the multiple databases mentioned in Sect. 2. 2. The locations of these stations along the estuaries are presented in Fig. 1. Calibration was implemented based on 2015, which is an intermediate annual flow between those of wet and dry years. The performance of the hydrodynamics modules was first evaluated by comparing the simulated salinity with observed salinity along the estuary. Then the mass transport was calibrated using longitudinal SPM profiles as an indicator. Good agreement with in situ data for salinity and SPM was reached by setting a maximum value of 100 m s -2 to the dispersion coefficient generated by the equations described in  Volta et al. (2014). This dispersion value is in agreement with the previous study in the Scheldt estuary  (Arndt et al.
0 times for TOC) than the Gironde. Furthermore, the Loire, with 1. 6 times the discharge of the Seine estuary, imported only 1. 2 times the TN flux to its estuary but 1. 8 times the TP flux. The river discharge of the Adour was quite similar to the Seine (86 % of the Seine), but its TN, TP, TSi, Phy, and TOC imports amounted to only 25 %, 34 %, 47 %, 24 %, and 40 %, respectively, of those of the Seine (Table  8 ). Based on this study, around 259 kt N yr -1 of TN, 9. 6 kt P yr -1 of TP, 304 kt Si yr -1 of TSi, and 145 kt C yr -1 of TOC were exported to the French Atlantic Ocean through these seven estuaries. They accounted for about 80 % of the total water discharge from all the estuaries on the French Atlantic coast (based on long-term analysis of runoff data  from French national databases) and 83 % of the total watershed areas on the French Atlantic coast.
Riverine boundary conditions comprise the observed data, extracted at the station closest to the model's upper boundary, which might still be located several kilometers upstream or downstream of the model's upstream boundary. It is noteworthy that the Dordogne river, which was considered as a source of water and biogeochemical material for the Gironde estuary at the confluence, ignored the tidal cycle effects that rise. Observed data are usually measured at monthly or bimonthly intervals. Some variables were, however, sampled more frequently, 2 or 3 times per month, while Chl a concentrations were usually only available from March to September. Also, DSi and TOC are usually less available than other variables. Instantaneous SPM and water quality sampling data were linearly interpolated to obtain the daily values between the adjacent measurements.
C-GEM can be combined with river models to simulate future scenarios with different degrees of anthropogenic impacts on the upstream basins and, in turn, to assess how the estuaries would respond to potential or forthcoming disturbances. However, ongoing work for a better representation of some biogeochemical processes (especially the water-sediment interface) should be implemented, while a systematic study regarding the grid size could be carried out on large and small estuaries. Data availability. The measured data used in this study can be accessed through the links in Sect. 2. 2. The C-GEM source code is available upon reasonable request to the corresponding authors. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-19-931-2022-supplement.
Author contributions. XW constructed the data set, performed the simulations, analyzed the data, prepared the figures, and wrote the paper. JG and GGL conceptualized and supervised this study, provided the data, and completed, modified, and revised the paper. VT, PP, and RLG provided the data for the data set used for this study and revised the paper. GB and MA revised this paper. Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements. This study (Prest' Eaux  project) has been supported by the  Office Français de la Biodiversité (OFB) , and their support greatly acknowledged for the project follow-up. Xi Wei 's post-doctoral research was funded by this project. Josette Garnier , the research director at the  CNRS , was the principal investigator (PI) of  Prest'Eaux. Goulven G. Laruelle  is a research associate of the  FRS-FNRS  at the  Université Libre de Bruxelles  and contributed to the development of G-GEM. The authors thank  Jack Middelburg , for editing the paper. Our grateful acknowledgements also go to  Pierre Anschutz  and two anonymous reviewers, for their constructive comments and suggestions. Review statement. This paper was edited by  Jack Middelburg  and reviewed by  Pierre Anschutz  and two anonymous referees.
0 - 4. 0 - Note: (1) The geometry information (length, surface area, and width) was derived from remote sensing images through the Esri Ocean layer in Geographic Information System (GIS). Depths were obtained from Defontaine et al. (2019), Goubert et al. (2019), Laruelle et al. (2019), McLean et al. (2019), Normandin et al. (2019), and Toublanc et al. (2015). *  (2) River basin areas include the basin area of the rivers flowing directly into the estuary. 2008; Ménesguen et al. , 2019), providing water elevation, temperature, salinity, SPM, PO 4 , NH 4 , NO 3 , DSi, DO, TOC, and phytoplankton (Phy) at 4 km spatial and hourly temporal resolutions. The use of the above database is detailed in Sect. 2. 3. 3.
For this study we select the 687 open clusters younger than 100 Myr. Furthermore, we refine the selection by only considering the 353 open clusters with more than 5 members brighter than absolute magnitude M G = 0 (computed taking into account their distance and interstellar extinction). This additional cut has been applied to select intrinsically bright clusters, and compensate for observational biases, which otherwise would cause an over-sampling of clusters in the solar neighborhood. In the following, we will refer to this sample as the young and intrinsically bright open cluster dataset. The complete open cluster sample can be downloaded from CDS.
Results Figure  1  gives an overview of the UMS P18 dataset. The spatial distribution of the stars in the X-Y plane of the Galactic disk is presented in Figure  1A , where the Sun is located at (X,Y)=(0,0) kpc. The lack of stars near the Sun's position ( 0. 2 kpc) is due to our selection criteria, as mentioned in the previous section. The spatial distribution exhibits some dense clumps, which correspond to well-known OB associations, i. e. Cygnus, Carina, Cassiopeia and Vela, amongst others. Also to be noted are radial features with respect to the Sun's position that become increasingly evident at heliocentric distances greater than about 2. 5 kpc. These are artificial features not due to distance uncertainties, but rather are "shadow cones" produced from foreground extinction in concert with the magnitude limit of this sample.
These features in the spatial distribution of stars can be interpreted as coinciding with spiral arm segments in the vicinity of the Sun. Specifically, we discern three inclined red stripes in Figure  1B  and 1C, corresponding (from left to right) to the Perseus arm, the local arm nearest the Sun, and an inner band to the Sagittarius-Carina and Scutum arms, respectively. In Figure  2 , we dissect Figure  1B  into slices along the Galactocentric radius R, spaced by 10 deg in Galactic azimuth. The assumed distance to the Galactic center is R = 8. 122 kpc (Gravity  Collaboration et al. 2018). Each slice illustrates the measured overdensity features in the form of peaks/bumps as a function of R. We also compare the results obtained with the UMS P18 sample to the overdensities derived from the UMS Z21 dataset.
Discussion It is worth comparing our overdensity maps, based on the distribution of young UMS stars, together with the open clusters and classical Cepheids, to previous models of the location of the spiral arms and other datasets avaiable in literature. By comparing our UMS overdensity map to the OB associations in the I and II quadrant first compiled by  Morgan et al. (1953)  (see their Fig. 1 ), using updated distances from Benjamin (2021, priv. comm. ), we found that there is excellent agreement (figure here not shown). In Figure  3  we have overlayed the spiral arm model based on positions of HII regions  (Georgelin & Georgelin 1976; Taylor & Cordes 1993, hereafter TC93). The same model is also overlayed on the overdensity map of the Cepheids in Figure  4 , together with another model of the Perseus arm from  Levine et al.
where Σ(X, Y) is the local density at the position (X, Y) in the Galactic plane, and Σ(X, Y) is the mean density. Both Σ(X, Y) and Σ(X, Y) are calculated via a bivariate kernel density estimator, but using two different bandwidths, i. e. 0. 3 kpc and 2 kpc, respectively, for the local and mean density. Details on the calculation of Σ(X, Y) and Σ(X, Y) are given in Appendix B. The overdensity measured from the UMS P18 dataset is shown in Figure1B. The corresponding plot using the UMS Z21 sample can be found in Appendix(Figure B. 1). Both maps are very similar, though the UMS Z21 sample shows more clearly the inner Scutum arm, while the UMS P18 sample seems to have a better sampling of the Perseus arm.
Acknowledgements. We thank  Robert Benjamin  and  Elena D'Onghia  for fruitful discussions. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This publication makes use of data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation.
EP acknowledges support by the  Centre national d'études spatiales (CNES). TCG, FF, and MRG acknowledge support by the  Spanish Ministry of Science, Innovation and University (MICIU/FEDER, UE)  through grants  RTI2018-095076-B-C21  and the  Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia 'María de Maeztu ') through grant  CEX2019-000918 -M. RB, YF, and AL acknowledge support by the  BELgian federal Science Policy Office (BELSPO)  through various  PROgramme de Développement d'Expériences scientifiques (PRODEX)  grants. RA & MF acknowledge the support from the  DLR (German space agency)  via grant  50 QG 1403. PR acknowledges support by the  Agence Nationale de la Recherche  ( ANR  project  SEGAL ANR-19-CE31-0017 ), funding from the project  ANR-18-CE31-0006  and from the  European Research Council  ( ERC grant  agreement No.
We describe the way a global model of the geomagnetic field has been built using vector field data acquired by the absolute scalar magnetometers (ASM) running in vector mode on board the Alpha and Bravo satellites of the European Space Agency (ESA) Swarm mission. This model has been used as a parent model to build a candidate Definitive Geomagnetic Reference Field (DGRF) 2015. 0 model to meet the call issued in the context of the recent update of the International Geomagnetic Reference Field (IGRF thirteenth generation). Because small but systematic issues were identified in a previous candidate IGRF 2015. 0 model built in the same spirit (also only relying on ASM vector field data) in the context of the previous IGRF update (IGRF twelfth generation), we now also use improved ASM vector field (ASM-V) data.
Early ASM-V data-based modeling success and observed limitations Each Swarm satellite carries a vector field magnetometer (VFM) and a three-head Star Tracker (STR) collocated on an optical bench, as well as an absolute scalar magnetometer (ASM). All instruments are located on a boom away from the body of the satellite to avoid magnetic perturbations. The optical bench (with VFM and STR) stands at mid-boom, whereas the ASM is located 2 m away, at the very tip of the boom, to also avoid interferences with the VFM. The Swarm nominal Level 1b (L1b) magnetic data are produced by using relative vector field output from this VFM, calibrated using scalar output from the ASM  (Tøffner-Clausen et al. 2016) , and provided in both the VFM reference frame and the North East Center (NEC) reference frame.
The NEC field components are inferred from synchronous readings from the STR (linking the NEC frame to its own Central Reference Frame, CRF), using calibrated Euler angles to link the CRF to the VFM frames  (Olsen et al. 2013). Colocation of the VFM and STR on the same optical bench ensures stability of this CRF to VFM frame rotation. This set-up is optimal for producing nominal L1b magnetic data in the way just described, since only scalar readings (not requiring attitude transfer) from the ASM instrument are needed for calibration of the VFM. ASM instruments, however, can also produce 1-Hz self-calibrated vector data (ASM-V data) in addition to the 1-Hz absolute scalar data nominally required by the mission  (Léger et al. 2015; Fratter et al. 2016). All ASMs have near-continuously been run in this experimental vector mode since launch in November 2013 (except for the ASM on Swarm Charlie, which ceased to produce data on November 5, 2014, following a heavy ion hitting a key component of the instrument's electronics).
Parent and twin parent model construction We now describe the way we built the parent model of our DGRF 2015. 0 candidate model, using the improved Swarm ASM-V data. Following  Vigneron et al. (2015)  and  Hulot et al. (2015) , this approach was also designed so that a twin parent model using exactly the same data distribution, parameterization (and damping parameters values), but relying on nominal L1b data (rather than ASM-V data) could be built.
Data selection Only Swarm Alpha and Bravo data produced from the ASMs running in vector mode were used. ASM Level 0 data version 0201 were processed into ASM-V Level 1a data using v7 version software, and into ASM-V L1b data with UTC time stamps, using XPh1B software, stray field corrections from Swarm 0506/0506 nominal ASMxAUX files, and characterization and calibration data base (CCDB) files updated as a result of the modification introduced in the previous section. These ASM-V data are expressed in the ASM reference frame. For geomagnetic field modeling purposes, they can be used in the same way as nominal L1b data (when using data expressed in the VFM reference frame, also available). This simply requires recovering attitude information from the q_NEC_CFR quaternion in 0506/0506 MAGxLR files, which we also used to recover satellite positions (radius/lat/long).
Model optimization The model is optimized by minimizing the mismatch between data and model prediction, using iteratively reweighted least-squares with Huber weights and temporal damping as in  Olsen et al. (2014)  the notation of which we follow, but only using two damping parameters, 2 and 3 :  (Nov-2013)  and end (May-2019) of dataset with the same damping value as CHAOS-4 2 = 10 nT year -2 -2. • 2 : |d 2 B r /dt 2 | constrained at beginning • 3 : |d 3 B r /dt 3 | integrated at the core surface and over the time coverage constrained with the same damping value as CHAOS-4 3 = 0. 33 nT year -3 -2 for all Gauss coefficients except for g 0 1 where we use 3 = 10 nT yr -3 -2. As in  Hulot et al. (2015) , a geographical weight is introduced, proportional to sin (θ) (where θ is the geographic colatitude), to balance the geographical sampling of data.
DGRF 2015. 0 candidate model and associated error estimates The DGRF 2015. 0 model being intended to be the best possible representation of Earth's internal magnetic field at epoch 2015. 0 up to degree and order 13, and since the purpose was to produce a model only based on Swarm ASM-V data, our DGRF 2015 candidate model is simply derived from the ASM-V parent model estimated at epoch 2015. 0 and truncated at degree and order 13. For comparison purposes, a twin DGRF 2015 candidate model was derived from the VFM twin parent model in the same way. Error estimates on each of the Gauss coefficients were also produced by assuming that the main cause of errors is in the data themselves, and assessed by taking advantage of our ability to rely on VFM twin models. We first split the ASM-V data set in two: ordering and numbering data as a function of time, every even number datum is put in sub-dataset ASM-V1, every odd datum is put in sub-dataset ASM-V2.
Likewise, the twin VFM data set (Swarm L1b nominal data) is split in two VFM1 and VFM2 datasets (the time distribution of VFM1/2 data matching that of ASM-V1/2). A model is next computed from each sub-dataset, using the same parameters as the parent (and twin) model, leading to four models: ASM-V1 and VFM1, ASM-V2 and VFM2. For each Gauss coefficients, the values A 1 (from ASM-V1), A 2 (from ASM-V2), V 1 (from VFM1) and V 2 (from VFM2) are next used to compute the quantity which we use as an error estimate for the corresponding Gauss coefficient. These error estimates, provided in addition to the DGRF 2015. 0 candidate model, are consistent with the observed spectral difference between this model and its twin (shown in Fig. 3 ). Note, however, that this technique only provides estimates of variances and ignores possible cross-correlations between Gauss coefficients.
0 candidate model of  Vigneron et al. (2015)  and the twin IGRF 2015. 0 candidate model these authors had built in the same way, i. e. by substituting VFM data to the ASM-V data (in cyan). Each of these spectra can be viewed as a measure of how well candidate models built in the same way from either ASM-V or VFM data agree with each other. As can be seen, the agreement of our DGRF 2015. 0 candidate model with its twin is much better than that of the IGRF 2015. 0 candidate model with its own twin, particularly so for degrees ranging between 4 and 6. This now directly testifies for the improvement brought by the new ASM-V data. To further illustrate the quality of our DGRF 2015. 0 candidate model and the improvement brought by the ASM-V data, we now turn to maps of the radial component B r of the field at the Earth's surface, for relevant field model differences.