dataesr/scientific-paragraphs-categorization huggingface dataset:
More specifically, two actions were undertaken: (1) implementation of a new set of peak integration settings to improve quantification at low sample mass loading (Table  7 ) as discussed in Swartzendruber et al. 35  , and (2) increase of the residence time in the detection cuvette by reducing the argon carrier gas flow rate to half of the manufacturer default settings while remaining within the range of recommended values (40 ml/mn and 100 ml/mn in "measure" and "flush flow" instrumental modes vs. 80 and 200 ml/mn in default settings). Quality control procedures are applied at each step of the data processing chain, from the raw measurement to the provision of the qualified dataset. Standardised quality assurance measures and calibration tools are applied on-site to provide documented and traceable data and data products.
Raw datasets as well as routine or exceptional maintenance files are compiled and processed by a custom-built software developed at the Institute of Environmental Geosciences (Grenoble, France) specifically designed for the QA/QC of the GEM/RGM/ PBM2. 5 datasets. In this automated process, the raw dataset is flagged (valid, warning, invalid) according to 43 possible criteria corresponding to all operation phases of the instrument (e. g. , calculation of Hg concentration, calibration, sensitivity of the instrument). The inclusion of all field notes implying further invalidations (e. g. , during maintenance operations) allows the production of a fully QA/QC' d dataset. Our data processing procedure is relatively close to the one developed under the umbrella of the GMOS project (G-DQM  65  ) but accounts for first-hand inputs from the site manager (e.
g. , field notes). A detailed description of this QA/QC procedure is available on the French national GMOS-FR AERIS data portal, reported in various publications  7, 10, 66  and briefly summarised in Fig. 4. It should be noted that, since 2012, we have had to replace the Tekran ® 2537 instrument 6 times due to instrument failure or unresolved technical issues (Fig. 3a ). These replacements were made following strict operating procedures (see above), with a strong focus on calibration tests, to prevent the introduction of systematic bias. Despite these occasional instrumental issues, the fraction of valid hourly measurements per month generally exceeded the 66% minimum WMO GAW requirement for continuous measurements  67  (72 out of 84 months; see Fig. 3a ). From 2012-2015, this minimum requirement can be reduced to 50% given the operating principle of the Tekran ® speciation unit (25% of the operating time of the 2537 analyser dedicated to RGM/ PBM2.
Usage Notes The standardised *. csv file format permits easy import into all analysis software commonly used in the atmospheric science community. The datasets can be used without further processing. In addition to datasets and associated metadata, the GMOS-FR AERIS website also includes a list of peer-reviewed publications that can help better understand the current state of science associated with these datasets. User should be aware that the RGM/PBM2. 5 datasets collected with a Tekran ® speciation unit may be biased, as discussed above. It is essential to consider these biases when interpreting and utilizing the data. The data presented in this manuscript have undergone peer review in 2023 and are represented by the specific versions (#v1. 0) given in Table  2. As monitoring activities are still ongoing, new datasets will be regularly uploaded to the GMOS-FR AERIS data portal.
These new versions might include additional information (e. g. , additional monitoring years) or refined data. Please note that any updates or new versions of the datasets are not part of the peer-reviewed data associated with this manuscript. n indicates the number of data points/samples. Note that RM measurements are still ongoing using PES-CEMs but samples collected after Feb 2022 have not been analysed yet (see Fig. 3d ). The violin plots show the kernel probability density and include a marker for the median (in red) and a box indicating the interquartile range (IQR). As in standard boxplots, the upper (lower) whisker extends from the box to the largest (smallest) value no further than 1. 5 × IQR. Values below the detection limit were discarded. We welcome enquiries regarding ancillary datasets also collected at AMS by partners (e.
Acknowledgements We deeply thank the overwintering staff for their tremendous work ( Boris Bouillard  and  Erwan Coz  in 2012,  Matthieu Le Dréau  and  Alice Croguennoc  in 2013,  Vincent Lucaire  and  Jeoffrey Chastaing  in 2014,  Joyce Marais  and  Nicolas Joly  in 2015,  Isabelle Jouvie  in 2016, Marine de Florinier in 2017,  Yann Bertrand  in 2018,  Elisabeth Logeais  in 2019,  Laura Noël  in 2020,  Sophia Laporte  in 2021,  Solène Duperray  in 2022, and  Virgile.
, Magand, O. & Bertrand, Y. passive discrete 1 Monthly to quarterly (2023). Discrete measurements of atmospheric elemental mercury at Amsterdam Island (L1). [Dataset]. Aeris. https:// 62 doi. org/10. 25326/489#v1. 0 Angot, H. , Dommergue, A. , Magand, O. & Bertrand, Y. RGM PBM2. 5 active continuous 1 4 hours (2023). Continuous measurements of atmospheric mercury at Amsterdam Island (L1). [Dataset]. Aeris. https://doi. 60 org/10. 25326/345#v1. 0 Angot, H. , Dommergue, A. , Magand, O. & Bertrand, Y. RM active discrete 1 Weekly to monthly (2023). Discrete measurements of atmospheric reactive mercury at Amsterdam Island (L1). [Dataset]. Aeris. https:// 63 doi. org/10. 25326/488#v1. 0 THg wet deposition flux passive discrete 2 Annual Angot, H. , Dommergue, A. , Magand, O. & Bertrand, Y. (2023).
Total mercury wet deposition fluxes at Amsterdam Island 64 (L2). [Dataset]. Aeris. https://doi. org/10. 25326/487#v1. 0 Mercury species Qualification level Variable Definition Date_time Date and time of measurement in local time (UTC + 5; DD/MM/YYYY HH:MM:SS) GEM_valid Ambient air concentration of Gaseous Elemental Mercury (GEM) in ng/m 3 at standard temperature and pressure (5-15 min time resolution) GEM, RGM, PBM2. 5 L1 RGM_valid Ambient air concentration of Reactive Gaseous Mercury (RGM) in pg/m 3 at standard temperature and pressure (4 hours time resolution) PBM_valid Ambient air concentration of Particulate Bound Mercury with an upper-size cutoff diameter of 2. 5 μm (PBM2. 5) in pg/m 3 at standard temperature and pressure (4 hours time resolution) GEM L2 Date_time GEM_valid Date and time of measurement in local time (UTC + 5, DD/MM/YYYY HH:MM:SS) Hourly-averaged ambient air concentration of Gaseous Elemental Mercury (GEM) in ng/m 3 at standard temperature and pressure 37, [52] [53] [54]   have shown the inertness of such membranes to GEM when deployed in an active sampling setting under environmental background conditions (1 to 2 ng/m 3 ) guaranteeing no overestimation of RM and underestimation of GEM.
Section 2 describes the dataset of ambient noise correlations and the initial velocity model used in the wave-equation tomography. The overall methodology is presented in Sections 3-4. Section 3 introduces the iterative process of the WET workflow, while Section 4 is dedicated to 3-D modeling of surface waves, with emphasis on the acoustic-elastic case. In the light of the specific case of the Ligurian-Provence basin, we document the importance of the fluid-solid coupling for marine crustal imaging based on ambient noise data. Section 5 presents tomography results and related discussions. In Section 6, we assess the robustness of the resulting 3-D velocity model.
Ambient Noise Data We use a subset of the data used by  Nouibat et al. (2022a Nouibat et al. ( , 2022b) )  consisting of vertical component cross-correlations computed between 600 broadband stations from all available temporary and permanent networks in the Alpine region (Fig. 1 , 2 • E-16. 2 • E; 41. 3 • N-49. 1 • N) during the period 2015-2019. The station array includes the entire AlpArray Seismic Network (AASN) and the Cifalps-2 experiment. The offshore component of the dataset consists of high-quality correlations between the AASN OBSs obtained from an iterative procedure involving onshore stations of the AASN and permanent networks. Beforehand, the OBSs daily noise records were cleaned from transients (e. g. , glitches) and seafloor-induced noises (compliance, tilt). For more details about this specific processing, readers can refer to  Nouibat et al.
(2022b). We keep only reliable cross-correlations by applying the following criteria: (1) signal-tonoise ratio (SNR) greater than 5, (2) difference in group velocity measured on the positive and negative parts less than 0. 2 km/s, and (3) inter-station distance larger than one wavelength for the maximum period considered in each period band. The SNR is defined as the ratio of the maximum amplitude of the surface wave to the standard deviation of the signal starting after the surface wave window. The final dataset includes ∼ 22 × 10 3 to 55 × 10 3 high-quality cross-correlations depending on the period band (Table. 1 ). For the wave-equation tomography, all stations in Figure  1  serve as receivers, out of which 185 stations are used as virtual sources (white circles).
Moho topography beneath the Ligurian sea 3-D crustal imaging of oceanic domains based on ambient-noise surface waves is a challenging topic due to the difficulty of accounting for effects of the water layer and complex seabed relief in a hybrid medium, as illustrated in this study. One of the main objectives of this study is to produce an homogeneous 3-D map of the Ligurian Moho in line with the physics of surface-wave propagation. Unlike the Alpine region, the 3-D structure of Moho in the Ligurian sea is poorly constrained by 3-D velocity models. In this region, the few constraints on the Moho depth are mainly provided by CSS data (e. g. ,  Makris et al. , 1999; Rollet et al. , 2002; Contrucci et al. , 2001; Dannowski et al. , 2020). This gap is partly due to the poor coverage of seismological stations in this area before the deployment of AlpArray OBSs, making it difficult to achieve 3-D high-resolution imaging with tomography methods such as ANT or LET.
Model robustness In this section, we will document the robustness of our resulting velocity model. First, we analyse the distribution of phase traveltime misfits of surface waves from the ambientnoise database used in this study. Then, we validate our model with earthquake seismic waves in the western Alps, (  1 ) by comparing P-, Rayleigh-wave observed waveforms with synthetic waveforms computed in our model, and (2) by analysing the distribution of first- arrival traveltime residuals between picked P, S arrivals and synthetic arrivals predicted in our model.
Finally, traveltimes are estimated along these rays with a ray sampling of 0. 5 km in order to achieve higher-computational accuracy. Using the same procedure, we calculate traveltimes in a 3-D model obtained from a local earthquake tomography using the same dataset  (Virieux et al. , 2023). The P and S residuals from our model (blue histograms in Fig. 14b-c ) show normal distribution shapes that are rather narrow (standard deviation < 1 s/100km) and remarkably well centred around zero misfit (mean < 0. 15 s/100km). The standard deviation of the S residuals is slightly larger, but it should be noted that the total number of P picks is almost 1. 4 times larger. However, the mean value of the S distribution is smaller. Such small mean values indicate that our model does not suffer from biases towards wrong mean-value velocities.
These histograms obtained from a completely independent dataset are comparable to those in Figure  11  based on surface waves from ambient noise. To further assess the relevance of our model, we compare with residuals from the LET model (red histograms in Fig. 14b-c ). The distribution of residuals from our model is notably narrower than that of the LET model. The overall good agreement between observed first-arrival traveltimes and those predicted in our model indicates that it is an accurate enough initial model for 3-D local earthquake tomography. Since it has been obtained using ambient-noise surface waves, this model has the advantage to constrain the lower crust that is often poorly sampled by earthquakes.
Acknowledgments This study was partially funded by the  SEISCOPE  consortium (http://seiscope2. osug. fr), sponsored by  AKERBP , CGG,  EXXON-MOBIL ,  GEOLINKS ,  JGI ,  PETROBRAS ,  SHELL ,  SINOPEC , and  TOTALENERGIES , and by the  RGF program  ( Référentiel Géologique de la France , (http://rgf. brgm. fr/). It is part of the AlpArray-FR project funded by  Agence Nationale de la Recherche  (contract  ANR-15-CE31-0015 ) and by  Labex OSUG@2020  ( Investissements d'Avenir ,  ANR-10-LABX-56 ). The deployment of  OBSs  for 8 months in the  Liguro-Provençal  basin was funded by project  AlpArray-FR  for the French component, and by the  LOBSTER  project that is part of the  German Priority Programme   SPP2017 4D-MB  for the German component. This study was granted access to the HPC resources of the Dahu platform of the CIMENT infrastructure (https://gricad.
univ-grenoble-alpes. fr/), which is supported by the  Auvergne-Rhône-Alpes region  (grant  CPER07-13CIRA ), the  Labex OSUG @2020 (reference  ANR10 LABX56 ) and the  Equip@Meso  project (reference  ANR-10-EQPX-29-01 ) of the program ' Investissements d'Avenir ' supervised by the  Agence Nationale de la Recherche  and the HPC resources of IDRIS/TGCC under allocation  046091  by GENCI. We are grateful to the editor and two anonymous reviewers whose comments improved our manuscript. We warmly thank  Jean Virieux  for computing and providing the first-arrival traveltime data. We thank  Yang Lu  for his constructive feedback on our inversion methodology. We thank  Aya Cheaib ,  Dorian Bienveignant  and  Stephane Schwartz  for their help in setting up the geological map. We are grateful to the operators of European permanent seismic networks who make their data available through EIDA (http://www.
Applying these criteria for the two bands separately, we obtained a sample of 148 and 109 pairs for BP and RP, respectively. The cumulative distributions are plotted in Fig. 24  as red lines. The fraction of stars that do not pass the test decreases to 21% for BP and 15% for RP, but it is still significant. In order to estimate how much the errors are underestimated, we multiplied the covariance matrix by various factors and then repeated the experiment on the 437 pairs that are fainter than G = 16 mag. The resulting p-value distributions are shown in Fig. 25. The figure shows that the variances are underestimated by a factor that is between 1. 2 and 1. 5. However, a detailed study of the coefficient error underestimation is presented in De Angeli et al. (  2023 ) by dividing the data into two groups of transits for the same source and comparing the obtained values.
2018). Comparison with the GUMS model confirms the presence of a high-mass tail in the FLAME data that is not predicted by the model. This tail is present in all Galactic directions, even at high latitudes. It is associated with an excess of young stars. These young (<2 Gyr) and massive (>2 M ) stars are on the giant branch. We therefore recommend using the FLAME masses with flags_flame[_spec] first character 1 (giant flag) only within the 1-2 M range and with caution, and taking their large uncertainties into account. The overestimation of the GSP-Phot extinction for low-mass stars (M G 7, Fig. 30 ) also has an impact on the FLAME parameters. The impact on masses M 0. 7 is illustrated in Appendix D of Gaia Collaboration (2023a). It has an impact on the luminosity similar to what is shown in Fig.
2023)). It is worth noting that the results of the SOM were not used to construct these tables. In other words, this label was attached to the sources that were previously selected as candidates by other means. In addition to these shared class labels, we can also identify unique labels for each table. In the QSO table we have access to the astrometric_selection_flag (ASF), which allows us to select only sources with a high probability of being quasars based on their astrometry. We can also use the source_selection_flags to isolate the QSOs in the qso_catalogue_name table (bit 3 set to 1), hereafter called QuasarObject list, which effectively corresponds to the sources that are found in well-known QSO catalogues that had enough raw data to be processed successfully by the QSO pipeline. Finally, in the galaxy table, we can select the sources whose morphology was fit reliably.
Unmatched sources and natural satellites of planets A small subsample of the data consists of 1 320 objects that were considered unknown at the time of processing. We refer to them as unmatched sources. Tanga et al. (2023)  performed a search to identify how many unmatched sources can now be identified (February 2022), and they found an identification for 712 sources. We cannot exclude either that some of the stillunmatched sources will be identified or linked to known objects when the observations are sent to the Minor Planet Center 18. All the sources will still appear as unmatched in the sso_source and sso_observations tables in the Gaia archive. The sample also contains natural satellites of planets for the first time. For a complete description of the process of selection, we refer to  Tanga et al.
Orbit determination process We used an orbit determination process to assess the quality of the data. This process is similar to the process that was carried out to validate Gaia DR2 (Gaia Collaboration 2018;  Arenou et al. 2018). We selected Gaia observations only for every known numbered minor planet in Gaia DR3. We used a modified version of the OrbFit software 19 to fit the orbits to Gaia observations alone. It is important to note that this software is completely independent from everything that runs in the Gaia data processing, and we improved it to fully exploit the accuracy of Gaia observations. The results of the orbital fit can be summarised as an orbit (if the fit converges), post-fit residuals in the (α cos(δ), δ) space and in the (AL, AC) space, and rejection of incorrect quality or mistakenly linked observations.
minorplanetcenter. net/ 19 http://adams. dm. unipi. it/orbfit/ A32, page 21 of 25  the along-scan post-fit residuals was higher than 250 mas and for which the absolute value of the across-scan post-fit residuals was higher than 2500 mas. In this way, we cleaned the database from possible contaminants. At the same time, we wished to keep the largest possible number of observations so that the community could search for interesting features (e. g. , the presence of satellites). As a consequence, the sample can still contain some contaminants. For example, we may only have removed part of a transit, but we decided to adopt a unique approach that is valid for all the observations. Some observations can also be rejected during the orbit-determination process, but this does not affect the overall quality of the data.
Orbit accuracy: Comparison with known catalogues The post-fit accuracy of the semi-major axis (σ a ) is a good estimator of the orbit quality. We compared the post-fit σ a obtained using Gaia observations alone with that available from the JPL Small Body Database 20  , which makes use of all the available observations; see Fig. 52. The black line in the figure is the bisector of the first quadrant: the orbits of the objects below the line have a better uncertainty using Gaia observation alone. It is clear that Gaia DR3 alone is still not enough to reach the final accuracy expected for Gaia (Gaia Collaboration 2018, Fig. 32 ), but the number of orbits for which the accuracy is now better using Gaia alone has largely increased from Gaia DR2.
Conclusions The third data release of Gaia, DR3, provides a very large amount of new data. This complex and diverse dataset has a number of caveats that the users should be aware of. In this paper we summarised the main issues we found during the transversal validation, and we provided links to the relevant papers or documentation and recommendations. In particular, we highlighted that flags provided with the data products should be used whenever available (e. g. , flags_gspspec, flags_flame, dibqf_gspspec, and flags_qsoc). We warned about the error underestimation of the XP coefficients, GSP-Phot parameters, GSP-Spec ANN, spectroscopic and eclipsing binary solutions with a poor goodness-of-fit, and we provided a correction formula for the radial velocity error estimates. The DSC white dwarf and binary star classifications should not be used.
A number of parameters were highlighted as to be used with caution (MSC parameters, mh_gspphot, distance_gspphot, mg_gspphot, FLAME mass and ages for giants, radial velocities for (G RVS -G) < -3, and astrometric binary acceleration values). The corrections proposed in  Recio-Blanco et al. (2023)  should be applied to the GSP-Spec parameters. Some systematics such as those presented for vbroad, the XP spectra dip, or extinction overestimation are to be taken into account according to the science case. Filters need to be applied to the QSO and galaxy candidates to have purer samples (Gaia Collaboration 2023b). Monte Carlo techniques should not be used with the Thiele-Innes astrometric binary orbital parameters. This paper focused on limitations in the data released in Gaia DR3. We encourage a study of the other papers that accompany this data release for an overview of the high quality of the Gaia products and a glance at the wonderful science outcomes that can be expected from this wealth of data.
Acknowledgements. This work presents results from the  European Space Agency (ESA) space mission Gaia. Gaia data are being processed by the  Gaia Data Processing and Analysis Consortium (DPAC). Funding for the DPAC is provided by national institutions, in particular the institutions participating in the  Gaia MultiLateral Agreement (MLA). The Gaia mission website is https://www. cosmos. esa. int/gaia. The Gaia archive website is https: //archives. esac. esa. int/gaia. This work has made an extensive use of Aladin and the  SIMBAD, VizieR databases operated at the Centre de Données Astronomiques (Strasbourg) in France  and of the software TOPCAT (Taylor 2005). This work has been supported by the  Agence Nationale de la Recherche  ( ANR  project  SEGAL ANR-19-CE31-0017 ). It has also received funding from the project  ANR-18-CE31-0006  and from the  European Research Council  ( ERC grant  agreement No.
Data were harmonized to uniform formats and units, screened for fetch, de-spiked, and gap-filled using a common flux post-processing standard prior to calculation of diel and monthly averages (Pastorello et al 2020; Supporting Material Text). Flux footprint and wind directional screening were applied by each investigator to retain only those half-hourly observations representative of the water body. Note that a negative CO 2 flux indicates uptake by the ecosystem from the atmosphere and a positive flux means the reverse. All data are published in the Environmental Data Initiative repository (Golub et al 2022).
Data analysis We analyzed the half-hourly CO 2 fluxes and three major groups of biophysical covariates. The first group included variables related to wind forcing acting on the water surface (i. e. friction velocity, wind speed, momentum flux). The second group encompassed variables related to temperature cycles and proxies of energy in the system (i. e. T air , T water , ∆T (T water -T air ), sensible (H) and latent heat (LE) fluxes). The last group included the variables associated with solar radiation-proxies for primary productivity (i. e. ∆pCO 2 (pCO 2water -pCO 2air ), PAR). To determine the standardized difference between two means with repeated unpaired measurements and imbalanced population sizes, we used the Cohen's d test where the mean difference between the mean daily CO 2 fluxes is divided by the pooled variance.