dataesr/scientific-paragraphs-categorization huggingface dataset:
The SPD was finally built from 73 radiocarbon dates (50 terrestrial and 23 marine). Probability distributions from radiocarbon date calibration were calculated and summed using the rcarbon package  (Crema and Bevan, 2020). SPDs were first calculated separately for marine and terrestrial samples and then summed. A 100-yr moving average of the total SPD was calculated. The confidence interval was defined by the 0. 025 and 0. 975 quantiles of values in the 100-yr windows. Three additional SPD curves were built to evaluate the effect of sample selection and of the use of mixed curve calibration of marine samples: (1) all the dates available were included (no sorting) and only the Marine13 calibration curve was used for marine samples (no mixing) (Figure  S1a ), (2) only the selected subset of dates was included (sorting) and only the Marine13 calibration curve was used for marine samples (no mixing) (Figure  S1b ), and (3) all dates were included (no sorting) but mixed Marine20 and IntCal20 curves were used for marine samples when possible (mixing) (Figure  S1c ).
Although ancient sites may have been more subject to erosion or human exploitation, this scarcity can only be interpreted as a period of very low human activity. Detailed changes within this period cannot be resolved with this dataset. Radiocarbon dates are here concentrated in the past 2000 years, with ~30 dates per millennium (~50 dates per millennium before selection), above the minimum suggested by these earlier statistical studies. In addition, the 100-year moving average removes a large part of the variability related to stochastic sampling. The 95% confidence interval associated to the moving average curve provides an indication of the statistical significance of the SPD variations (Figure  2 ). We thus conclude that the broad phases observed in the SPD in the last 2000 years are statistically robust.
Acknowledgements This work was supported by the  French National program LEFE (Les Enveloppes Fluides et l'Environnement) , and by the  French National Research Agency  within the framework of the  IROCWA  project ( ANR-19-CE32-0003-01  grant). We thank the  LMC14  staff ( Laboratoire de Mesure du Carbone-14 ), ARTEMIS national facility, ( LSCE  ( CNRS-CEA-UVSQ)-IRD-IRSN-MC ) for the results obtained with the accelerator mass spectrometry method. The study received support from  CONCYTEC (Peru)  with grant  n°034-2019-FONDECYT-BM.
Its main instrument is the TROPOspheric Monitoring Instrument (TROPOMI), which provides tropospheric NO 2 column densities at high spatial resolution with a large swath width and with a daily frequency  (Veefkind et al. , 2012). By applying the steady-state continuity equation  (Beirle et al. , 2019; Lama et al. , 2020) , it is possible to build a top-down model that directly quantifies NO x emissions from these NO 2 column densities, provided that some key parameters (wind, temperature, hydroxyl radical concentration and concentration ratio between NO x and NO 2 ) are correctly estimated. This model is used to quantify the anthropogenic NO x emissions in Egypt for a 2-year period, from November 2018 to November 2020. This paper is organised as follows: Sect. 2 provides a description of the datasets used in this study.
Consequently, the majority of NO 2 is not transported far downwind from its sources. Thus, near-surface NO 2 concentrations are generally high over industrial facilities and densely populated areas that need to be identified. Because Egypt's population is almost entirely located along the Nile River and its delta, the study of NO x emissions in this country cannot therefore be reduced to the study of a small number of point sources, as would be the case for several other parts of the Middle East region, and must be carried out in the form of a mapping of the country. Further discussion is provided in Sect. 3. 3. To identify urban areas in Egypt, we use the Socioeconomic Data and Applications Center (SEDAC) GRUMP (Global Rural-Urban Mapping Project) data archive, which comprises eight global datasets, including a population density grid provided at a resolution of 30 arcsec, with population estimates norhttps://doi.
org/10. 5194/acp-22-11505-2022 Atmos. Chem. Phys. , 22, 11505-11527, 2022 malised for the year 2000  (CIESIN, 2019). We combine this database with field data giving the location of industrial facilities from energy-intensive industries in the region. Data have been retrieved from the Global Energy Observatory for five oil-and gas-fired power plants, from the work of  Elvidge et al. (2016)  for flaring sites, and from the work of Steven J. Davis and Dan Tong (unpublished data) for cement plants; links are at the end of this article. The locations of other industrial facilities, such as aluminium and iron smelters, were obtained from various sources. These datasets are used to remove the non-anthropogenic part of the NO x emissions signal. We conduct this removal by subtracting the mean emissions over areas without human activity from the mean emissions over industrial and densely populated areas.
Emission inventories We compare TROPOMI-derived NO x emissions to the Emissions Database for Global Atmospheric Research (EDGARv5. 0) for 2020 and the CAMS global anthropogenic emissions (CAMS-GLOB-ANT_v4. 2) inventory released in 2020. Both datasets provide 0. 1   (Hoesly et al. , 2018). From one inventory to another, the names and definitions of the sectors may vary. In EDGARv5. 0 and CAMS-GLOB-ANT_v4. 2, the emissions for a given country are derived from the type of technologies used, the dependence of emission factors on fuel type, combustion conditions, and activity data and low-resolution emission factors  (Janssens-Maenhout et al. , 2019).
4 m s -1 ) for level B. These lifetimes are compared to the corresponding lifetimes obtained from CAMS data in Fig. 5 , which is divided into seasons for a more convenient comparison. The use of level A leads to notable underestimations of the NO 2 lifetime in autumn compared to the lifetime calculated with the fitting method. This same level leads to lifetime overestimations in winter. This trend is not found with the use of level B, which leads to a better reproduction of the lifetimes calculated with the fitting method for the available seasons. Figure  5  shows a linear regression between the two calculated lifetimes. The results are scattered, with a correlation coefficient higher for level B (0. 408) than for level A (0. 220). When the intercept of the regression line is forced to zero, the resulting slope is closer to 1 for level B (0.
Acknowledgements. The authors would like to thank  Steven J. Davis  ( University of California, Irvine ) and  Dan Tong  ( Tsinghua University ) for their contribution to the construction of our emitter database. Financial support. This research has been supported by the  Horizon 2020 (EMME-CARE  (grant no. 856612 )). Review statement. This paper was edited by  Bryan N. Duncan  and reviewed by two anonymous referees.
Code availability. The software code for this research is not in a stand-alone user-friendly form but can be shared on request to the authors. Data availability. The TROPOMI NO 2 product is publicly available on the TROPOMI Open hub (http://www. tropomi. eu/ data-products/data-access, TROPOMI Data Hub, 2022). The offline mode (OFFL) data of version 1. 0. 0 to version 1. 1. 0 were used. CAMS data can be downloaded from the Copernicus Climate Data Store (https://ads. atmosphere. copernicus. eu/cdsapp#!/dataset/ cams-global-atmospheric-composition-forecasts, ECMWF, 2022a). The European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis be downloaded from the.
Principle The SC compares the eddies detected in different databases having the same formalism: each eddy is saved with a rotating sense, position, amplitude, radius, time, trajectory, and contours. As for the tracking procedure, when we want to associate similar eddies within a trajectory, here we search for one eddy at a time t in one atlas (the reference) and check if there is a corresponding eddy in another atlas (the study) at the same time, using the overlap of the effective contours of those eddies. The SC is defined in Eq. (  1 ) as the ratio between the intersection of the eddies' effective areas in the reference Earth Syst. Sci. Data, 14, 1087 Data, 14, -1107 Data, 14, , 2022  https://doi. org/10. 5194/essd-14-1087-2022 and the studied atlases and the union of their effective areas, expressed in percent: Similaritycoefficient (%) = 100 × Area Eddy ref ∩ Area Eddy study Area Eddy ref ∪ Area Eddy study.
Summary We present the new global Mesoscale Eddy Trajectory Atlas (META3. 1exp). Numerous evolutions have been made from the META2. 0 product, mainly to provide useful characteristics for users, in particular the eddy contours, which are mandatory for efficient colocation of eddies with other data. Moreover, some details of the detection and tracking algorithm were developed for weekly altimetric maps, with less accuracy than the daily products available at present, and needed to be adapted. The https://doi. org/10. 5194/essd-14-1087-2022 Earth Syst. Sci. Data, 14, 1087-1107, 2022 code has been freely released under a GPL V3 license (https://doi. org/10. 5281/zenodo. 6333989, Delepoulle et al. , 2022)  in the Python language, and the routines for data manipulation and visualization have been documented.
The changes between the META2. 0 and META3. 1exp concern the input sea level field, its filtering, the detection algorithm, and the tracking scheme. We developed a methodology to compare intermediate datasets where only one change is made at a time, and to compute an SC between matching eddies, from the ratio between their overlap area and their union. Using these diagnostics, we have quantified eddies which are similar in the intermediate datasets (SC over 40 %), those which differ (SC between 5 % and 40 %), those with multiples matches, and the new eddies present in only one dataset. The analysis was made on restricted datasets including only eddies in the open ocean involved in trajectories lasting at least 10 d, although all geographical regions and shorter eddy trajectories are included in the full dataset.
1exp dataset. To take into account the sharp gradients introduced in the ADT by the MDT while maintaining the mesoscale patterns in the filtered maps, the half-power cutoff wavelength was reduced from 1000 to 700 km. This change slightly increased the number of detected eddies, and the similar eddies (87 % of the dataset) had their radius and amplitude increased, but not significantly. The tracking scheme was also changed to improve the trajectories' reliability. Instead of searching for the eddy candidates to associate with a trajectory over a restricted area, the overlap method needs an overlap between the eddy and its next candidate, and the largest overlap is associated with the main trajectory in the case of multiple candidates. The number of consecutive virtual eddies introduced to respond to the "missing eddy problem" was increased from three to four.
0, despite the consistency of effective radius and amplitudes for the similar eddies. The additional eddies in META3. 1exp were not detected or available at the global scale until now; they appear to be consistent with longer trajectories, but they still need scientific validation that can be provided by their colocation with independent data. Author contributions. EM and AD developed and maintained the PET code. AD provided new routines and methods, and generated the META3. 1exp datasets and the intermediate atlases. AD and CP developed and analyzed the similarity coefficient, interpreted the results, and wrote the online documentation. CP wrote the manuscript, with inputs from AD. RM aided in interpreting the results and writing the manuscript. GD and YF supervised the project. Competing interests.
It is only some kilometres downstream of the confluence between the RR and the KR that the main sequence of post-T1 terraces is observed (Figures  3A  and 4B ). There, a total of 300 post-T1 remnants were mapped by  Bonnet et al. (2019)  over a distance of $50 km. Many correspond to perched abandoned meanders (Figure  4B ). In the following, we will expand the preliminary study of  Bonnet et al. (2019)  by acquiring new single-grain luminescence data for T1, post-T1 and T2 terraces in order to better constrain the Holocene RR incision in space and time and to investigate the additional constraints that the distribution of the luminescence signal may provide on this dynamic. For this purpose, we also acquired luminescence data on modern river deposits.
| Sampling In this study, 28 new samples distributed along the RR (Figure  3B ) were analysed for feldspar SG-pIRIR luminescence. This supplements an existing dataset of nine samples analysed using the same protocol by  Bonnet et al. (2019). This total of 37 samples includes (Table  1  and Supplementary Figure  S1):  i. two samples from fine sandstone bedrock (Wanganui basin deposits); ii. one sample from T2 terrace; iii. four samples from T1 terrace spread over 88 km, three on the RR and one on the KR (Figure  3B ); iv. 23 samples from post-T1 terraces distributed over a 70 km stretch downstream of the confluence between the RR and the KR (Figure  3B ); v. seven modern samples taken within the floodplain, five on the RR and two on the KR, spread over 142 km. One sample was taken from above the major knickpoint (Figure  3B ).
, 2018). We used here the sigma-b value of 0. 27 AE 0. 10 determined by  Bonnet et al. (2019). In addition, we applied the Central Age Model (CAM;  Galbraith et al. , 1999)  to the full D e distribution of each sample to allow comparison with the BS-MAM results. We define the inherited dose as the difference between CAM and BS-MAM palaeodose estimates (residual dose of  Chamberlain & Wallinga, 2019)  and use it as an indicator for the degree of bleaching. All palaeodose estimates were calculated using the R Luminescence package  (Kreutzer et al. , 2012). Note that data for the nine samples of  Bonnet et al. (2019)  were recalculated using this updated setup (see online Supplementary Material), resulting in slightly different values than originally reported (AE20% on average), where only the 30% brightest grains were considered.
| Processes that control luminescence signal distribution Many processes and factors control the luminescence signal of grains collected in a given geomorphic system  (Gray et al. , 2019). In fluvial settings, the signals measured include a mix of grains from different sources and transported through different steps that could have increased (during temporary burial) or decreased (when exposed to light during transport or surface exposure) their initial D e before their ultimate burial. The SG protocols  (Murray & Roberts, 1997 for OSL quartz; Reimann et al. , 2012  for pIRIR feldspars) offer data at the smallest resolution-of a unit of transport sediment-necessary to F I G U R E 4 Schematic cross-section (not to scale), photograph and sketch of two different areas along the Rangitikei River (RR).
(A) Upstream RR valley section, between its main knickpoint and its connection with the Kawhatau River, illustrating the vertical incision of the RR into terrace T1 and the underlying bedrock, and its canyon shape, without any major flights of post-T1 terraces. (B) Downstream RR valley section, downstream junction with the Kawhatau River, illustrating wandering of the RR with numerous unpaired post-T1 strath terraces, some testifying to perched palaeo-meanders. T1 and T2 terraces are also visible. In both photographs, light grey colours on the canyon flanks are bedrock exposures T A B L E 1 Summary of dataset (samples with an asterisk are from  Bonnet et al. , 2019)   7. 5, 15, 30, 60, 120, 240, 0 and 15 Gy)  decipher the luminescence signal, classify the grains and use it as a geomorphic tool.
| Incision dynamics The incision dynamics of the RR is reconstructed using the 28 new age estimates of terraces that complement the preliminary dataset of nine ages of  Bonnet et al. (2019). Our new dataset provides the first luminescence dating of a T2 terrace of the RR, of 32. 1 AE 3. 9 ka, which falls into the age interval estimated for this terrace along the RR and other rivers of the North Island of New Zealand (30-50 ka; see review in  Litchfield & Berryman, 2005).
This age estimate agrees with a similar transition from aggradation to incision documented in the North Island of New Zealand on the Waihuka River between 10 and 8 ka  (Berryman et al. , 2010). Both for Waihuka and Rangitikei, knickpoint propagation followed by a slower rate of downcutting has been proposed as a cause for this transition  (Berryman et al. , 2010; Bonnet et al. , 2019). Our new post-T1 dataset (Figure  7 ) corroborates this interpretation, although our dataset is too small to investigate along-stream diachronicity of incision initiation as observed in laboratory experiments  (Baynes et al. , 2020). Nevertheless, several lines of evidence support such evolution. Firstly, major knickpoints with heights of several tens of metres exist along many tributaries of the RR (Figure  11 ).
The discovery of coesite  (Smith, 1984)  and microdiamond  (Dobrzhinetskaya et al. , 1995)  led to the characterization of the WGR as one of the best-preserved ultrahigh-pressure (UHP) units on Earth. Many studies focused on the exhumation mechanisms of the eclogites and hence the post-peak-pressure part of their P-T evolution  (Andersen, 1998; Andersen et al. , 1991; Carswell, Brueckner, et al. , 2003; Dewey et al. , 1993; Fossen & Dallmeyer, 1998; Schärer & Labrousse, 2003; Labrousse et al. , 2004; Root et al. , 2004; Young et al. , 2007; Johnston, Hacker, & Ducea, 2007; Labrousse et al. , 2011). Consequently, there is a large dataset of P-T estimates (and associated P-T paths) for the peak and retrograde conditions (blue and grey ellipses respectively, Figure  2 ), whereas only very few data are available for the prograde, subduction-related, part of the history (green ellipses, Figure  2 ).
Strain tensors of the quartz inclusions were calculated from the 128. 1, 207. 3 and 464. 8 cm -1 Raman peak mode shifts using the stRAinMAN software  (Angel et al. , 2019 ). An uncertainty of 0. 3 cm -1 was assumed for the shifts. Isomekes (i. e. lines in P-T space) of This article is protected by copyright. All rights reserved. possible quartz entrapment conditions were obtained using the EntraPT platform  (Mazzucchelli et al. , 2021)  with either almandine, pyrope or grossular as host (equations of state for quartz from  Angel et al. , 2017;  for garnet endmembers from  Milani et al. , 2015 Milani et al. , , 2017) )  and then recalculated with the measured garnet composition. The final propagated error issued from Raman shift uncertainties and misfit between the three modes results in pressure uncertainty of ± 0.
Phase-equilibrium modelling Phase-equilibrium modelling was carried out in the MnNCKFMASHTO system (MnO-Na2O-CaO-K2O-FeO-MgO-Al2O3-SiO2-H2O-TiO2-Fe2O3) using the Theriak/Domino software (de Capitani & Petrakakis, 2010) and the internally consistent thermodynamic dataset 6. 2  (Holland & Powell, 2011). The following minerals and solution models were used: biotite, white mica, chlorite  (White et al. 2014) , amphibole and clinopyroxene  (Green et al. 2016) , epidote  (Holland & Powell 2011) , plagioclase  (Holland & Powell 2003). Garnet a-x relations of  White et al. (2007)  were used instead of those of  White et al. (2014)  following the suggestions of  Pan et al. (2020). Albite, lawsonite, quartz/coesite, rutile and titanite were considered as pure end-members. The analysed bulk rock composition was corrected for the presence of apatite and carbonates, not considered in the modelling, and the measured X(Fe 3+ ) ratio (molar Fe 3+ /Fe total ) was used.
Pure H2O fluid was considered in excess. The presence of carbonates in the sample implies the presence of CO2, which is known to shift the position of equilibria in the P-T field (e. g. Evans, 1990; Schumacher et al. , 2008). However, several studies on carbonate-bearing HP rocks  (Schumacher et al. , 2008; Skelton et al. , 2019; Manzotti et al. , 2021)  suggested very low X(CO2) in such systems (of the order of X(CO2)<0. 03), and consequently a limited impact on the modelled equilibria. Thermobarometry for the symplectite assemblage was achieved with THERMOCALC 3. 50  (Powell & Holland, 1988) , using the average P calculation mode and the internally consistent thermodynamic dataset 6. 2  (Holland & Powell, 2011).
This mechanically leads to an increase of the deviatoric stress sustained by the rocks (Figure  9d  inset) and therefore to a pressure increase  (Petrini & Podladchikov, 2000; Mancktelow, 2008; Yamato & Brun, 2017). Reuber et al. (2016)  showed that strong lower crust or strong heterogeneities therein may be indeed subject to large (> +100 %) deviations from lithostatic pressure during collision. Based on a wide dataset of metamorphic records from various (U)HP terranes,  Yamato & Brun (2017)  suggested that the peak pressure recorded in (U)HP rocks and the subsequent pressure drop were linearly related and could be explained by a switch of the state of stress between compression (burial) and extension (exhumation). Their mechanical analysis considered pressuredependent frictional yielding, i.
Acknowledgements This research was supported by a  Rennes 1 University  grant to  P. Pitra  ( Défis Scientifiques program ) and by the GeOHeLiS platform. P. Yamato thanks the  Institut Universitaire de France  for financial support. We are very grateful to  Xavier Le Coz  for thin section preparation and  Jessica Langlade  for her help during the mineral microprobe analyses. Alain Moréac ,  Katy Leroy  and  Alexandre Peillod  are thanked for the analytical support for the Raman spectroscopy, the data acquisition, and useful suggestions on data treatment, respectively. This study is a part of the PhD thesis of  M. Simon , funded by the  French Ministry of Higher Education and Research. The authors also thank  C. Möller , J. Majka and two anonymous reviewers who helped to improve the quality of this contribution.
Acknowledgements The authors are grateful to  Consorci de la Costa Brava Girona (CCBGi) , the staff of the  Palamós WWTP  for their unconditional help, and the AGAUR-SGR2017-1485 research group. Abu Alex would like to thank the  The Agency for Management of University and Research Grants of the Generalitat de Catalunya  for the Ph. D. grant ( 2019 FI_B 01059 ) and the  CCiT of University of Barcelona  for the analytical support.
In David et al. (2021), we introduced a neural network (NN) approach for estimating the column-averaged dry-air mole fraction of CO 2 (XCO 2 ) and the surface pressure from the reflected solar spectra acquired by the OCO-2 instrument. The results indicated great potential for the technique as the comparison against both model estimates and independent TCCON measurements showed an accuracy and precision similar to or better than that of the operational ACOS (NASA's Atmospheric CO 2 Observations from Space retrievals -ACOS) algorithm. Yet, subsequent analysis showed that the neural network estimate often mimics the training dataset and is unable to retrieve small-scale features such as CO 2 plumes from industrial sites. Importantly, we found that, with the same inputs as those used to estimate XCO 2 and surface pressure, the NN technique is able to estimate latitude and date with unexpected skill, i.