dataesr/scientific-paragraphs-categorization huggingface dataset:
(2020). The subset was created based on the availability of CH 4 flux data (i. e. , not all datasets contained CH 4 data). At each sampling site, two distinct regions were sampled: i) exposed sedimenthereafter named as "dry inland water", which corresponds to an area with air-exposed sediment that experiences periodic and/or recent historical inundation  (Marcé et al. , 2019) ; and ii) adjacent uphill soil, which corresponds to the adjacent terrestrial region that is not inundated. At each region, measurements were performed in triplicates that were, if possible, conducted at least 1 m apart from each other to capture spatial within-region variability.
Data analysis and statistical procedures Each sampling site was assigned to a climate zone according to "The World Maps of Köppen-Geiger Climate Classification"  (Kottek et al. , 2006). For each sampling site, we then calculated the contribution of dry inland water CH 4 emission to the total (i. e. , CO 2 + CH 4 ) GHG emission in CO 2 -equivalents (CO 2 -eq; see below how CH 4 was converted to CO 2 -eq) for each type of aquatic system (lakes, ponds, reservoirs, and streams). The CO 2 flux data was retrieved from  Keller et al. (2020) , and each CO 2 flux measurement was associated with the CH 4 flux measurement collected at the same sampling site. For all analyses, triplicate measurements were averaged, and one average value per parameter at each sampling site was used. To test the relationships between environmental variables and dry inland water CH 4 fluxes, a generalized linear mixed model (GLMM) was performed using the "glmer" function in the "lme4" package  (Bates et al.
5 mass concentration of suspended particulate matter") ensuring high-quality data. Hourly BC concentrations were measured by aethalometers (AE33) at 5 stations  (SIRTA, Paris13, A1, BPEst, Bld Haussmann). Two sites were equipped with Aerosol Chemical Speciation Monitors (ACSM) (SIRTA and Gennevilliers) giving access to the hourly non-refractive part of the PM 1 fraction, with organic matter (OM), nitrate (NO 3 -), and sulfate (SO 4 2-) used in the study. Additional daily filter samples of aerosols have been collected at SIRTA, Creil and LISA UPEC to obtain the concentrations of the major water-soluble ions (nitrate and sulfate) as well as to elemental and organic carbon (EC and OC, respectively). Most of the chemical speciation data are also part of the so-called CARA ("Caractérisation chimique des particules") program, operated at the national level  (Favez et al.
To put the large number of measurements into the context of the IDF measurement network, their respective representativeness is discussed. Concerning the PM mass, we chose to use the PM 10 data from Airparif which are most numerous in the region and which are consistent with the fraction used for the OP study. Although the instruments used may vary, they all meet the applicable European standards EN 12341 and EN 16450, respectively. Concerning the PM chemical speciation, we gathered all available observations to best account for spatial variations and the respective signature according to the source area characteristics (traffic, urban, suburban and rural). This has rather challenging implications for site-tosite and/or instrument-to-instrument comparisons given the stations may differ in terms of the fractions collected, i.
This suggests that the December 2016 event is mainly driven by local sources of pollution. A detailed characterization of atmospheric dynamics and their spatial variability is expected to provide further insights into the accumulation of air pollutants under such extreme conditions in the future. The analysis of the PM chemical composition shows that OM highly dominates the total PM concentrations during this pollution episode. OM concentrations are higher for urban and traffic sites than at rural locations and the suburban SIRTA site. Combined with the fact that OM was mainly of primary origins, this is pointing again toward local sources, with predominant contributions from biomass burning (revealed as BBOA from PMF analysis from ACSM data at SIRTA). The relative amount of BC originating from wood burning compared to BC from fossil fuel combustion is about 20% during the event.
Acknowledgments Measurements at SIRTA have been supported by  CNRS ,  CEA ,  INERIS ;  Ile de France region and ACTRIS-France  (notably through the CLAP national observation service). They were also part of the  EUFP7  and  H2020   ACTRIS  projects (grant agreements Nos. 262254  and  654109 ). Measurements and analyses carried out at the LISA UPEC station were supported by  L. Beillard ,  S. Chevaillier ,  A. Féron ,  A. -C. Guillet M. Maillé ,  F. Maisonneuve ,  C. Mirande-Bret ,  S. Triquet. This work was performed using HPC resources from  GENCI-IDRIS  (Grant  2020-A0090107232 ) The authors would also like to thank both  Erwan Personne  ( INRA ) for the coordination of the NUAGE research project and the  ADEME (French Agency for Environmental and Energy Management)  for its financial support through the  CORTEA program.
We are grateful to  Sorbonne Université  and  Institut Pierre-Simon Laplace  for support and facilities, as well as  Pascal Jeseck  for the CO11M measurement support. This work has been funded by the  French CNRS LEFE-CHAT program  in the framework of the  EPPI  project. JLJ and GU thank  ANR10 LABX56  and  ANR-15-IDEX-02  for funding instruments in Air-O-Sol facility.
The new 129 I dataset captured deep tracer maxima associated with density, salinity and potential temperatures expected for ISOW. Using 129 I depth profiles we were able to provide an independent validation of ISOW pathways being redrawn by Lagrangian studies. Specifically, we observed ISOW spreading along the boundary current in the eastern flank of the Reykjanes Ridge, over the ridge through the Bight Fracture Zone and off the boundary current following interior routes into the Iceland Basin and the West European Basin. The comparison of the new 129 I dataset to earlier distributions shows that tracer concentrations increased notably in recent years, allowing for the first time the straightforward tracking of ISOW by using this tracer. Further, we found that 129 I increased at an unprecedented rate after 2014 intruding other water masses in the SPNA.
Observed ISOW Pathways in 2017-2018 Here we provide an independent means of validating transport pathways of ISOW. Our approach is based on the identification of deep, high 129 I concentrations (> 15 ×10 7 at/kg) captured at the layer with 27. 85 -27. 90 kg/m 3 potential density, S >34. 95 and q of 2-3°C. We focus on cases where there is a large 129 I concentration gradient between ISOW and surrounding water masses. These conditions are met in the Reykjanes Ridge, Iceland Basin and the West European Basin, but not in the Irminger Sea because there the 129 I concentrations of ISOW are surpassed by higher tracer amounts carried by the underlying DSOW. We found seven 129 I depth profiles from OVIDE and PE424 cruises that captured the mentioned deep tracer spike in 2017/ 2018 (yellow and blue data in Figs.
On the contrary, the overflow water begun to carry sufficient 129 I in the Iceland Basin (station GV32) in 2014  (Castrillejo et al. , 2018). And it was not until 2018 that a strong tracer signal unraveled the passage of ISOW in the Iceland Basin (OV 57) and other parts of the SPNA. To provide a more comprehensive view on the temporal evolution of 129 I in ISOW, we calculated the mean tracer concentrations using all available measurements in the overflow found east of Greenland and west of the West European Basin. We exclude the West European Basin from the calculations because tracer data are insufficient to infer any temporal trend in that region. West of the West European Basin, 129 I observations are also limited (Table  S2 ), yet Figure  4E  convincingly shows a clear increase in tracer values between 1993 and 2018.
To further explore if 129 I could unveil off-boundary pathways of LSW beyond the SPNA (turquoise dotted lines in Figure  5B ) we visited three sites in the western and eastern subtropical North Atlantic Ocean (Figs. 5C  and 5D ). In the subtropical region, consistently with the literature, we preferred to call NADW U and NEADW U the water-masses derived from LSW far from its formation region, and NADW L for DSOW. To the best of our knowledge, the tracer observations presented here for BATS (Figure  5C ) constitute the first full-depth profile of 129 I in the low latitude North Atlantic aside from the two full depth profiles measured off the North American Slope in 1993/1994  (Santschi et al. , 1996). In their 129 I dataset, a near bottom tracer peak could be observed associated to the transport of NADW L via the Deep Western Boundary Current (Figure  5C ).
More recently another study by  Smith et al. (2016)  focused on the off-boundary transport of the same lower branch in our study area east of Bermuda (magenta dotted lines in Figure  5B ). But they did not report 129 I values for shallower depths occupied by NADW U. In our BATS profile (Figure  5C ) we observed a very clear peak of 129 I centered at about 1500 m depth coinciding with potential densities (s q ~27. 75 kg/m 3 , see Table  S1 ) and a local salinity minimum (S~35) that characterize the LSW in the North Atlantic  (McCartney and Talley, 1982). This tracer peak at 1500 m depth therefore shows that 129 I data allow the straightforward identification of NADW U taking offboundary pathways near Bermuda (turquoise dotted lines in Figure  5B ). We also sampled two partial depth profiles south of the Azores Archipelago during ATHENA (Figure  5D ).
ACKNOWLEDGMENTS Captains, crew and scientific staff of the four cruises are thanked for their dedication in collecting the samples used in this study. Thanks go to  A. M. Wefing  for her assistance in sample collection and comments on the manuscript and to  A. Schlatter  and  K. Kündig  for help in the laboratory. Norbert Frank  is thanked for inviting us to participate in cruise ATHENA (M151). Acknowledgements go to  Loes Gerringa  and  Rob Middag  ( NIOZ ) who collected samples during the PE424 cruise. Rod Johnson  (BIOS) and  Anita Schlatter  (EAWAG) collected the 129 I profile during one of their monitoring cruises at BATS station. The analysis of waters masses would not have been possible without the hydrographic data that was kindly produced by different groups from many nationalities, thus, a big thank you to all.
FUNDING The research of MC was partly funded by the ETH Career Seed Grant (SEED-06 19-2) and the Swiss National Science Foundation Postdoc Mobility Program (P400P2_199289). NC's funding came from the European Research Council (ERC Consolidator GAP-101001451) and the Swiss National Science Foundation (AMBIZIONE PZ00P2_154805 and PRIMA PR00P2_193091). The participation in the OVIDE 2018 Cruise onboard R/V Thalassa (https://doi. org/10. 17600/18000510) was supported by PL and funds from the Laboratory of Ion Beam Physics. Support to process samples from PE424 cruise onboard R/V Pelagia and to participate on BATS monitoring onboard R/V Atlantic Explorer came from Swiss National Science Foundation Ambizione grant of NC. Funding for ATHENA Cruise (M151) onboard R/V Meteor came from Deutsche Forschungsgemeinschaft.
At regular time intervals throughout the tests, the transducer array was used to measure time of flight of P and Sh elastic waves at four different orientations with respect to the compression axis. Only direct arrivals are considered, and converted phases are not analysed. Details of the experimental technique and data processing can be found in  Brantut (2015). The samples were first deformed in load-unload cycles with maximum differential stress gradually increasing by around 100 MPa between successive cycles. In each cycle, a constant axial deformation rate of 10 -5 s -1 was imposed until the target differential stress was reached. The samples were then unloaded at the same deformation rate down to minimum differential stress of around 20 MPa. These deformation cycles were conducted at increasing differential stress up to a maximum that was lower than the peak stress (failure strength) of the rock.
Data preparation We use the data of 45 seismic stations shown in Fig. 1  from August 2015 to June 2016. The preparation and preprocessing of the data are done with the CovSeisNet software  (57). First, seismograms are aligned in time and stored in 24-hour-long segments. After down-sampling them at 25 samples/s, we apply demeaning, linear detrending, and band-pass filtering between 0. 1 and 10 Hz. Instrument responses are considered being stable in time and not corrected for.
Covariance matrix spectral width For this characteristic function, we preprocessed the time series according to  (58)  with the application of a spectral whitening followed by a temporal normalization where the whitened signal is divided by a 0. 25-s long running average of its absolute value. Following  (28) , daily traces are cut into a number of overlapping time windows called averaging windows. Then, each averaging window is cut into M overlapping subwindows of length t such that t = Mrt, where t is the length of the averaging time window and r is the overlapping ratio that is 0. 5 on our case. The array data vector is obtained from the N seismic stations u( f ) as u( f ) = [u 1 ( f ), u 2 ( f ), …, u N ( f )] T and we then compute the outer product u( f )u † ( f ) within each subwindow, where † stands for the Hermitian transpose.
The covariance matrix C( f ) is lastly estimated on any averaging window from M consecutive subwindows C( f ) = 〈u( f ) u † ( f )〉 t = 1 ─ M ∑ m=1 M u m ( f ) u m † ( f ) (1) with u m ( f ) the data Fourier spectra vector in the subwindow m. We perform calculation on consecutive averaging windows over the entire time period of our dataset using M = 48 and t = 50 s. We thus obtain estimation of frequency-dependent covariance matrices in 20-min-long windows that overlap with a 50% factor. The covariance matrix is Hermitian and positive semidefinite, so it can be decomposed on the basis of its complex eigenvectors v n associated with real positive eigenvalues  n C( f ) = ∑ n=1 N  n ( f ) v n ( f ) v n † ( f ) (2) Then, eigenvalues are arranged in decreasing order, and the covariance matrix spectral width is computed as a function of frequency as ( f ) = ∑ i=1 N (i -1 )  i ( f ) ─ ∑ i=1 N  i ( f ) (3) The time evolution of function ( f ) is shown in fig.
Acknowledgments: We thank  W. Thelen  and two other anonymous reviewers whose comments helped to improve and to clarify the manuscript. Funding: This study was supported by the  European Research Council (ERC)  under the  European Union Horizon 2020 Research and Innovation Programme  (grant agreements  787399-SEISMAZE  and no. 742335-F-IMAGE ) and by the  Russian Ministry of Education and Science  (grant no. 14. W03. 31. 0033 ). C. Jo. and G. F. benefited from a funding from  Ecole Normale Supérieure (ENS) ,  Paris, France. Seismological data were partially obtained with large-scale research facilities " Seismic infrasound array for monitoring Arctic cryolitozone and continuous seismic monitoring of the Russian Federation , neighbouring territories and the world" (https://ckp-rf. ru/usu/507436/) and are available from the GEOFON datacenter at  GFZ-Potsdam  (KISS temporary stations: doi: 10.
SAGE III/ISS Data The SAGE III/ISS conducts solar and lunar occultation measurements globally while orbiting the Earth. Vertical profiles of aerosol extinction were obtained using the rays passing through the atmosphere during sunrise/moonrise and sunset/moonset events. These aerosol extinction coefficient profiles (available since June 2017, Version 5. 2; https: //asdc. larc. nasa. gov/project/SAGE%20III-ISS, accessed on 28 June 2022) are retrieved at multiple wavelengths from ultraviolet to the near-infrared (384, 449, 521, 602, 676, 756, 869, 1021, and 1544 nm) from 0. 5 km (or cloud top) to 40 km altitude with a vertical resolution of ∼1 km being reported in 0. 5 km increments between 60 • S and 60 • N. Since the aerosol extinction information is unavailable in the lunar occultation product of SAGE III/ISS, only the solar occultation product (Level 2 Solar Event Species Profiles, V052, 0.
5 km vertical interval) is used in this study. The volcanic plume was identified from the averaged SAGE III/ISS vertical profiles using a criterion based on the vertical variability of the computed Angstrom Exponent (AE) with the aerosol extinction measurements at 449 and 869 nm, while the cloud-contaminated extinction values were excluded when the ratio of extinction coefficients at 521 and 1021 nm was found to be less than 2  [30]. Apart from this, the tropopause altitude information from the MERRA-2 reanalysis (Modern-Era Retrospective analysis for Research and Applications, Version 2) included within the SAGE III/ISS data is used. The sAOD is calculated by integrating the extinction coefficient profile from above the tropopause altitude to 30 km at the specified wavelength. Since the aerosol extinction at 521 nm is biased below 20 km due to an error in th O 4 absorption cross-section  [44]  used in version 5.
Hydrochemical survey The BC was monitored at the outlet (Baget B1, Fig. 1 ) between and 2020. The outlet represents the integration of the whole catchment in terms of response to environmental factors. The monitoring frequency was as follows: a weekly frequency from 1979 to 2006 by the CNRS (French National Centre for Scientific Research) laboratory of Moulis  (Binet et al. , 2020) , a biannual frequency by the BRGM (Bureau de Recherches Géologiques et Minières) Occitanie (ADES database, 2021) from 2007 to 2014, and a bi-monthly frequency between October and September 2020 by the Laboratory of Functional Ecology and Environment  (Ulloa-Cedamanos et al. , 2020). Rainfall was measured from September 2019 to December 2020 at The coordinates of the extreme edges of BC are 42 • 58 ′ 21. 9 ′′ N, 0 • 58 ′ 15.
Processing and exploitation of imagery data Multi-temporal images and multi-source data have already been used to study changes in land cover  (Dusseux, 2014)  for assessing various environmental processes  (Grinand et al. , 2013) , such as the influence of ancient ecological processes on current biodiversity  (Herrault, 2015). The photo-interpretation of imagery data (Google Earth) and the IGN forest inventory of 1987 served to develop the nomenclature  (Miranda et al. , 2018)  and to identify the tree species, as well as the location of anthropic structures (buildings, roads, cultivated areas). These data constitute the training dataset, 50% of which was used in the classification model and the other half to validate the classification image  (Marais Sicre et al. , 2020). This step was crucial, as the quality of class equality, maximum intra-class homogeneity and inter-class heterogeneity determine the pixels classification and therefore the discrimination of the different classes.
The method used was a supervised classification fed by the first half of the training dataset using the Orfeo Toolbox in QGIS software  (QGIS Development Team, 2021). This process treats each pixel one by one from the image provided using the Random Forest algorithm  (Breiman, 2001; Marais Sicre et al. , 2020) , in order to assign all pixels to a certain class. This non-parametric algorithm  (Breiman, 2001)  was used due to the better performance compared to other classification algorithms  (Gislason et al. , 2006) , its robustness in wide area classification  (Pelletier et al. , 2017)  and the easy parameterization  (Rodriguez-Galiano et al. , 2012). Thus, seven main land cover types were identified over the whole basin (Fig. 1 , Appendix C) using satellite images, with beech and broadleaf forests as the two dominant land covers.
Confusion matrix shows which classes were confused with each other, i. e. , which pixels were classified in the wrong classes. For the classifications generated on aerial photographs, the evaluation was carried out on each mosaic and then averaged over all the mosaics covering the catchment. For satellite images, the assessment was carried out on the classified and smoothed image (use of a majority filter (3x3) to discard isolated pixels). The evaluations were based on the second half of the training dataset.
Statistical analysis All calculations were computed on a hydrological year basis (October-September). For simplicity, only the second year of each period will be indicated, for example the period from October 1978 to September 1979 will be named as the year 1979. Statistical data processing was performed in R (R Core Team, 2021) using RStudio software  (RStudio Team, 2020). Redundancy analysis (RDA, Jari Oksanen et al. , 2020) is a two-step analysis in which individual and environmental data are analysed using multivariate linear regression, producing a matrix of fitted values. Then, PCA of the fitted values was used to produce canonical axes, which were linear combinations of the predictors  (Legendre and Legendre, 2012). RDA was used to analyse many individual characteristics and environmental predictors simultaneously  (Forester et al.
Conclusions This study investigated the impact of landscape changes over decades on the stream water chemistry in a small forested karst catchment from the Pyrenees Mountains, using a unique 40-year survey dataset. The method developed may it possible to harmonize heterogeneous data sources (aerial photographs from 1942 to 1989, satellite images from 2001 to 2019 and other exogenous data) to reconstruct and quantify the landscape changes undergone in the Baget catchment (BC) from 1942 to 2019. The landscape classification was split into three main classes (pioneer community, open forest and mature forest stage) to create an indicator that allowed the comparison/harmonization between aerial photographs and satellite image data, as well as to be consistent with the objectives of the study. From 1942 to 2019, the Baget catchment has undergone a progressive enclosing of the landscape through afforestation of abandoned lands and forest growth.
2+ ) and HCO 3 -concentrations indicated a slight increase between 1979 and 2018. In contrast, longterm trends in discharge and calculated pCO 2 in the streamwater, after degassing of CO 2 and/or calcite precipitation(Ulloa-Cedamanos et al. ,  2021b), did not show any significant trends (Ulloa-Cedamanos et al. , 2020). The hydrochemical dataset between 2007 and 2016 (N = 35) included only a small number of samples per year compared to the rest of the other years (mean of 53 samples per year). However, this period was considered in the long-term trend analysis because it does not modify the trends, but it takes into account a greater dispersion of the points between 2007 and 2016 (Fig. 5A and 5B). The trend of the mean annual concentrations of (Ca 2+ +Mg 2+ ) and HCO 3 -confirmed the overall increasing trend already observed on instantaneous data by Ulloa-Cedamanos et al.
Acknowledgments The BC belongs to the  French Karst Network (SNO Karst , Jourde et al. , 2018; www. sokarst. org)initiative of the  INSU/CNRS (France) , which aims to strengthen knowledge-sharing and to promote crossdisciplinary research on karst systems. It is one of the observatories of the  French Research Infrastructure called OZCAR (the French network of Critical Zone Observatories , (Gaillardet et al. , 2018)) and also a  LTSER (Long-Term Socio-Ecological Research)  research site of the  CNRS, France (National Centre for Scientific Research ) collaborative research platform called " Zone Atelier Pyrénées-Garonne (ZA PYGAR )", which is a  French Research Infrastructure called RZA (Réseau des Zones Ateliers , (Bretagnolle et al. , 2019). RZA grouped with OZCAR is the French contribution to the  European Research Infrastructure eLTER (Long-Term Ecosystem in Europe , (Mirtl et al.