dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This study received financial support from the  MISTRALS/ChArMEx  programme funded by  CNRS/INSU ,  CEA , and  Météo-France , from  ADEME , the  French Environmental Ministry , the CaPPA projects, and the  Communauté Territoriale de Corse  ( CORSiCA  project). The  CaPPA   project (Chemical and Physical Properties of the Atmosphere)  is funded by the  French National Research Agency (ANR)  through the  PIA (Programme d'Investissement d'Avenir)  under contract  ANR-11-LABX-0005-01  and by the  Regional Council Nord-Pas de Calais  and the  European Funds for Regional Economic Development (FEDER). This research was also funded by the  European Union Seventh Framework Programme  under grant agreement number  293897 , the  DEFI-VOC  project,  CARBO-SOR/Primequal and SAF-MED (ANR  grant number  ANR-12-BS06-0013-02 ).
The authors are thankful for the O3 and CO datasets made available, respectively, by  François Gheusi  from  Laboratoire d'Aérologie  and by  ICOS-France monitoring network. The authors also want to thank  Thierry Leonardis ,  Emmanuel Tison ,  Vincent Gaudion ,  Laurence Depelchin  and  Isabelle Fronval  for their contributions to obtaining of VOC datasets, from the organization and the setting of instruments at Ersa to the analysis of VOC samples at the laboratory, but also  Thierry Bourianne  and  Qualitair Corse  team for their involvement on site concerning VOC sample routines as well as the associated logistic procedure. The authors finally thank the  ChArMEx  project manager  Eric Hamonou  for his logistical help. https://doi. org/10. 5194/acp-2020-607Preprint. Discussion started: 8 July 2020 c Author(s)  2020.
Access to EOP and summer 2013 SOP-1b VOC datasets used for this publication is open to registered users following the data and publication policy of the ChArMEx program (http://mistrals. sedoo. fr/ChArMEx/Data-Policy/ChArMEx_DataPolicy. pdf, last access: 03/04/2020). VOC datasets from Ersa summer field campaigns of 2012 and 2014 are available upon request. Please contact Stéphane Sauvage (stephane. sauvage@imt-lille-douai. fr) for further information. Source regions (wind sectors) Transit time (h) Median [min-max] Occurrence (%) C1 Marine Marine (SW) Short trajectories Long trajectories 48 [18-48].
5 For these latter, VOC measurements did not cover winter 2014 period, that's why accumulated concentrations were only indicated https://doi. org/10. 5194/acp-2020-607 Preprint. Discussion started: 8 July 2020 c Author(s) 2020. CC BY 4. 0 License. for winter 2013 period. For AUC, HRL, BIR and SMR stations, represented by grey markers, VOC measurements were not realized both during winters 2013 and 2014, or were conducted with a PTR-MS and hence we considered accumulated concentrations only of aromatic compounds are not sufficiently representative for interannual VOC concentration variations. Ersa location and results are highlighted. Map provided by Google Earth Pro software (v. 7. 3. 3 image Landsat/Copernicus -IBCAO; data SIO, NOOA, U. S, Navy, NGA, GEBCO; © Google Earth).
(2018) , where precipitation predictions are obtained from the IPSL_CM5 GCM (5th phase of the Coupled Model Intercomparison Project;  Dufresne et al. , 2013; Global Climatic Model)  for Lebanon with a resolution of 0. 25° based on the AgMERRA dataset  (Ruane et al. , 2015). In  Doummar et al. (2018)  the scenario RCP 6. 0 was adopted; meaning a stabilization of the radiative forcing value without overshoot pathway at 6 W/m 2 after 2100. Projected raw data were corrected account for the elevation of the catchment area (1500-2200 m), using a constant lapse rate for T (-0. 24 °C) and P (+5. 5%) per 100 m elevation. For all simulations, we consider monthly averaged recharge variation. The results of simulations are analysed using the spatial maps of water content (θ) and three scalar metrics which are i) the dimensionless average saturation of the aquifer (  ), ii) the maximum water table elevation ( 0 ) and iii) the discharge rate at the spring outlet (  ).
, 2018)  due to its rapid economic development but also as a region of increased carbon sequestration thanks to a number of reforestation projects  (Chen et al. , 2019). In China, a series of ambitious projects on mitigation of carbon emission was kicked-off in the last 10 years, which include the first Chinese greenhouse gas monitoring satellite mission (TanSat), which is supported by the Ministry of Science and Technology of China, the Chinese Academy of Sciences, and the China Meteorological Administration. The TanSat mission was initiated in 2011 and successfully launched on 22 Dec 2016. TanSat started acquiring and archiving data operationally in March 2017  (Chen et al. , 2012; Ran & Li, 2019). TanSat carries two instruments on-board: the Atmospheric Carbon Dioxide Grating Spectrometer (ACGS) and the Cloud and Aerosol Polarimetry Imager (CAPI).
GL provides routine measurements over oceans which are obtained by tracking the principle plane (the principal plane is spanned by the vectors from the sun to the surface footprint and from the surface point to the observer). ND provides the routine measurements over land with the satellite flying in consistent rotation angle routine as in GL mode. We only use ND observations in this study. The swath width of TanSat measurements is ~18 km across the satellite track and contains 9 footprints each with a size of 2 km × 2 km in nadir  (Lin et al. , 2017; Zhang et al. , 2019). Nadir and glint mode alternate orbit-by-orbit, and the TanSat nadir model ground track is typically between two OCO-2 tracks, which provides potential future opportunities for combined usage of both data products for increased spatial coverage.
The first global XCO 2 dataset from TanSat observations for the first half year of operations has been produced with the Institute of Atmospheric Physics Carbon dioxide retrieval Algorithm for Satellite measurement (IAPCAS)  (Yang et al. , 2018) , using the CO 2 weak band only. The nadir XCO 2 dataset has been evaluated using eight TCCON sites to show an average precision of 2. 11 ppm for the TanSat retrievals  (Liu et al. , 2018). In this study, we apply the University of Leicester Full Physics (UoL-FP) algorithm to the TanSat XCO 2 retrieval using a joint CO 2 weak band and O 2 A band retrieval, after adopting a new approach to radiometrically correct TanSat spectra. This new radiometric correction is introduced in Section 2 together with the UoL-FP TanSat retrieval approach. The applied quality filtering and bias correction methods are discussed in Section 3.
A set of 20,000 high quality solar calibration soundings (RMSE < 0. 21%, which is the mean RMSE of all retrievals) including all 9 footprints have been selected for this calculation. Another highly non-linear parameter is the stretch of the wavelength grid. The update of TanSat L1B data from version 1 to version 2 significantly improved the wavelength calibration, but additional corrections are still necessary. The solar calibration fitting also simultaneously provides wavelength stretch coefficients which we then use in the O 2 A band surface pressure retrieval. The impact of the Fourier continuum correction on the surface pressure retrieval is significant (Figure  3 ). We found that (1) the retrieval without the correction has a large bias and scatter, and (  2 ) there are large differences between the cross-track footprints in the retrieval even after continuum correction (with a constant gain factor).
The O 2 A band surface pressure retrieval with our newly developed continuum correction shows reliable results; hence we can extend the CO 2 retrieval with confidence to use the O 2 A and CO 2 weak band together to improve the retrieval accuracy. We have applied a two-band retrieval to produce XCO 2 data from soundings that are recognized as clear-sky scenes by the cloud screening. The retrieval uses a state vector that includes a CO 2 profile, scale factors for temperature and water vapor, surface pressure, surface albedo and spectral slope. In addition, we fit an additive zero offset and its wavelength dependent slope in both the O 2 A and CO 2 weak bands. For the CO 2 weak band, the same correction model as for the O 2 A band is applied. The full state vector is given in Table  1. Significant improvements to the O 2 A band residual have been found when using the Fourier series correction.
Dataset To remove outliers and to correct for small biases, a quality control filter and bias correction is applied using a reference dataset that is reliable enough for indicating retrieval errors. The Total Carbon Column Observing Network (TCCON) provides accurate measurement of XCO2  (Wunch, Toon, et al. , 2011; Wunch et al. , 2015) , and has been used for GOSAT and OCO-2 filtering and bias correction  (Kim et al. , 2016; Oshchepkov et al. , 2013; Wunch, Wennberg, et al. , 2011; Wunch et al. , 2017; Wu et al. , 2018; Yoshida et al. , 2013). The OCO-2 team has also developed additional methods for filtering and bias correction for their version 8 product, including small area approximation, multi-model median and southern hemisphere approximation  (O'Dell et al. , 2018). In this study we only focus on the retrieval around TCCON sites, and hence only the data of 20 TCCON sites (Table  2 ) has been selected as a reference dataset for bias/filtering and validation in this study.
, 2013; Wunch, Wennberg, et al. , 2011) , but since TanSat has a much higher data density compared to GOSAT, the spatial criterion above does provide a sufficient number of soundings. All TCCON measurements within ±1 hour of the satellite overpass are averaged to provide a reference dataset for each overpass, and only overpasses with more than 20 TCCON measurements within the 2 hour period are used. In this study, we found 396,068 co-located TanSat retrievals in 174 overpasses across the 20 TCCON sites. Using the TCCON average as reference, we define the individual retrieval error as the XCO2 difference between each TanSat retrieval ( b C TanSat ) and the TCCON average ( C TCCON ) for a satellite overpass: δ xco2 ¼ b C TanSat -C TCCON. Hence, there will be hundreds of individual TanSat retrievals for each average TCCON value for an overpass, except when heavily contaminated by clouds.
In this study, we cut-off at 2 ppm with 5 filters, which is an ad hoc choice, and the transparency is 64. 3%. The 2 ppm RMSE is a compromise between coverage and accuracy. The RMSE is calculated from individual TCCON overpass couples, which means the footprint can be spatially away from the TCCON location, and hence there are would be geolocation differences that casue naturally CO 2 differences. In addition, we also have to consider the measurement error and bias that include in the RMSE. measurements (e. g. stray light or calibration) and the databases which are used (e. g. gas absorption and solar model). The former two will typically lead to variable biases that will depend on other parameters while the latter one more likely induces a global bias. Commonly, the parameter-dependent bias dominates and can be corrected  (Wunch, Wennberg, et al.
In future studies we will investigate the impact of the differences of the two algorithms and their advantages and disadvantages. In this study we introduced an improved TanSat retrieval over land based on a two-band retrieval. The inclusion of the strong CO 2 band still needs to be investigated in the future and can be expected to further improve the retrieval performance by providing more information on water vapor and temperature, as well as the wavelength dependency of aerosols and clouds in the long-wave end of the SWIR. Further studies on target mode observations would be helpful to improve the retrievals. Furthermore, the retrieval of glint mode observations over both ocean and land surfaces will need to be studied, allowing us to extend the coverage of the TanSat XCO 2 dataset. Optimization in genetic algorithms is based on concepts drawn from the process of evolution taking place in biology.
If by f(x) we denote a cost function to be optimized with respect to a vector x, a genetic algorithm first generates a dataset (population) {x i } of candidate solutions, and then randomly updates the dataset by applying two operations to its elements: 1. Mutation: one or more component of an element x are randomly perturbed 2. Cross-over: a new element of the population is generated by combining two existing elements All the elements in the population are then ranked with respect to a fitness function which measures the goodness of a certain element as a solution of the optimization problem, and a selection is performed by retaining a user-defined percentage of elements which obtain the best ranking. Typically, the fitness function is the cost function itself. As a result of this selection process, a new population is created, and the random operations of mutation and cross-overfollowed by a selectionare applied to the new population.
Therefore, only mutations are applied in reproduction, which has been found to be sufficient  (Mandrake et al. , 2013). Threshold values for each gene are initiated randomly with a quantity of 100,000 individuals ranging between the maximum and minimum values in the training dataset. For each reproduction, only 15 individuals pass the fitness selection and replace the population of the last reproduction. Therefore, the mutation is only coming from these 15 individuals that contain advantaged genes. We let the GA mutate the gene 100 times for each individual by selecting gene and according number randomly, and then 150,000 individuals have reproduced after each mutation. A convergence criterion has been chosen to stop the GA run when the RSME of the retrieval error reduces to less than 0. 2% over the previous ten iterations.
Cenozoic uplift in SW England: persistent uncertainties Whatever the exact causes of epeirogeny among those listed above, crustal deformation is considered to have generated 1-2. 5 km of surface denudation in southern Britain during the Cenozoic  (Clayton and Shamoon, 1999; Hillis et al. , 2008 , Holford et al. , 2009). These inferences are based largely on apatite fission-track thermochronology. While the reconstructions recognise by default the long-term persistence of the Cornubian Island throughout the Cretaceous and Cenozoic, SW England actually remains undocumented by low-temperature methods and thus forms a gap in the regional dataset. Spatial interpolation of the fission-track data from the surrounding on-and offshore sedimentary basins postulates 1-1. 5 km of rock exhumation over Devon  (Hillis et al.
Influence of stream piracy It is likely that the Dartmoor gorges were cut iteratively by successive waves of knickpoints migrating up-channel in response to the profile-steepening mechanisms described above. Wheeler (1979)  analysed the long profiles of 115 British rivers and calculated a stream concavity index based on the maximum deviation above and below a constant gradient of percentage-drop to percentage-distance. With reference to the relationship obtained between trunk-stream relief and stream-profile concavity (watersheds with greater available relief have higher indices of concavity), Wheeler observed a number of atypical profiles positioned away from the line of regression through the data set. These anomalous stream profiles were either overconcave or underconcave, i. e. , in either case in excess of the graded profile empirically defined for the data set.
g. ,  Williams, 1983). • OSL thermochronometry is a new approach for evaluating post-glacial bedrock surface erosion  (Lehmann et al. , 2019). Given that OSL and 10 Be data integrate over different timescales, inverting bedrock OSL signals and 10 Be concentrations into quantitative estimates of post-glacial exposure duration could help to detect how tor erosion rates have varied through space and time since the last glacier melt-out event on Dartmoor. • Schmidt hammer exposure dating has gained from recent advances in other regions  (Tomkins et al. , 2018). The mechanical response of 13 10 Be-dated tor summits was tested by Gunnell (unpubl. ) with an N-type Schmidt hammer. A minimum of ten compressive strength measurements was made at each site. Results from this preliminary attempt (Fig. 24 ) reveal a positive correlation between 10 Be minimum exposure ages and rock-strength values, i.
2010b) , were subsequently cut and embedded in Huntsman Araldite 2020 to ensure the physical preservation of the structures (Figure  2b-c ). The umbo regions were cut along the maximum growth axis (through the middle of the hinge region to the ventral shell margin). The two resulting halves of each umbo were used to make thin sections and isotopic sampling. Thin sections were used for microscope and cathodoluminescence observations to ensure the pristine state of the shells and to obtain a seasonalscale temporal calibration  (Langlet et al. 2006; Mouchi et al. 2018). Cathodoluminescence observations were performed on a Cathodyne OPEA cold cathode with operating settings of 15-20 kV and 200-400 µA•mm -2. Specimens exhibiting suspicious microtextural patterns were discarded. Intensity of luminescence (in arbitrary units) were obtain along a continuous transect using the NIH-ImageJ software (ver.
Growth rates of oyster shells The smoothed intensity of luminescence (using a moving average; Figures  4  and 5 ) allows the estimation of age for each specimen at the time of death. The resulting age models gave lifespans for each individual oyster between 3. 5 and 9 years. These ages were compared to the total size of the shell in order to obtain the mean growth rate for each specimen. The mean shell growth rate for all specimens (Figure  6 ) follow a linear distribution. The data were tested for outliers (considering an outlier is more than three scaled median absolute deviations (MAD) away from the median) which could be interpreted as a significantly different growth rate which could be reflecting different environmental settings. No outlier was detected.
Discussion The isotopic composition of the shells allows to obtain information on the living environment of oysters. Although absolute values cannot be properly compared between localities due to differences in seawater and freshwater δ 18 O, Figure  7a  presents datasets from various environments exhibiting characteristic dispersion of values. In close proximity and influence of a river output, the freshwater induces a depletion in δ 18 O w and thus shell δ 18 O as well as occasionally large amplitude of values (i. e. , estuarine from Walther & Rowley 2013; Figure  7a ). Alternatively, in locations without direct freshwater input such as open marine settings, δ 18 O fluctuations are governed by temperature variations (i. e. , marine from  Tynan et al. 2014; Walther & Rowley 2013 ; Figure  7a ).
It is known that particulate organic carbon is generally enriched in 13 C (less negative δ 13 C) from riverine to marine environments  (Hughes & Sherr 1983; Incze et al. 1982; Riera & Richard 1996). The 95% confidence intervals of the distributions of the various estuarine datasets in Figure  7a  tend to follow this behaviour, with not only larger amplitude of δ 13 C, but also with more negative δ 13 C values associated with depleted oxygen composition (reflecting lower salinity in those environments) due to freshwater input, with different food source for the oysters compared to marine water. The shape of these 95% confidence intervals tends to be a vertically-oriented ellipse with restricted amplitudes of both isotopic ratios for open marine settings (without significant correlation between δ 13 C and δ 18 O; in magenta and dark blue on Figure  7a ), and is a larger-amplitude diagonally-oriented ellipse for estuarine environments (corresponding to a highly significant positive correlation between both stable isotope ratios; p < 0.
It is however possible that this can be due to missing data during low temperature and/or salinity periods. Indeed, interspecific differences on the temperature and salinity tolerance may be at play, with O. edulis unable to build shell increments in lower salinity settings. This way, no shell carbonate could be sampled for isotopic analysis to obtain the complete record of the environment. This assumption has been advanced by  Surge and Lohmann (2008)  to explain the range of δ 18 O of their specimens, which is lower than expected considering the large salinity variations observed on site. If O. edulis specimens would have experienced low salinity conditions and been able to produce shell increments, additional data would probably be present on Figure  7b  at the bottom left corner of Mermian distributions, where δ 18 O (for low salinity) and δ 13 C (for food from continental origin) would be lower than presently represented, and the distribution would have a shape of diagonally-oriented ellipse with significant positive correlation.
Funding:  Work of the group "Energetic Metabolism of Streptomyces" of the "Institute of Integrative Biology of the Cell " (I2BC, https://www. i2bc. paris-saclay. fr/) is funded by the  "Centre National de la Recherche Scientifique" CNRS  (http://www. cnrs. fr), the  University Paris-Saclay  (https://www. universite-parissaclay. fr/en) and the grant  ANR-17-ASTR-0018  (Innovantibio). Funding:  Work of the group "Energetic Metabolism of Streptomyces" of the "Institute of Integrative Biology of the Cell " (I2BC, https://www. i2bc. paris-saclay. fr/) is funded by the  "Centre National de la Recherche Scientifique" CNRS  (http://www. cnrs. fr), the  University Paris-Saclay  (https://www. universite-paris-saclay. fr/en) and the grant  ANR-17-ASTR-0018  (Innovantibio).
The development of adaptation indicators and metrics that can be aggregated and compared to support environmental management is a key challenge for climate experts, finance institutions, and decision-makers. To provide an operational ex-ante evaluation of alternative adaptation strategies, statistical evaluation was conducted on 1562 adaptation projects contained in the Nationally Determined Contributions (NDCs) submitted by almost all parties who signed the Paris Agreement in 2015. As a preliminary stage, we are suggesting a physical risk taxonomy derived from climate model databases and an adaptation project taxonomy using a text analysis. The second stage, consisting of an evaluation metric using a correspondence analysis between adaptation projects and risk classes, was inspired by the analogy with adaptation mechanisms in living organisms-assessing the correct correspondence between threats from the environment and adaptive solutions.
Step 1: NDC Plus NAP Database Building Our database of projects focused on climate change adaptation uses two main sources. The first includes 1562 adaptation projects contained in the NDCs submitted to the Paris Accord in 2015. The second includes 547 High-Priority Projects (HPPs) from the National Adaptation Plans (NAPs) provided since 2011 to the UNFCCC by the fifty least-developed countries, which need adaptation the most  [42] [43] [44]. When a project consigned in the NDC of one of the least-developed countries was not explicit enough, we used the NAP description of the same project. The database is thus composed of 1562 lines, each presenting an adaptation project and its country of adoption.
Step 3: Level of Vulnerability for Risk-Country Combinations We associated a level of vulnerability with each country-risk pair for each of the projects using the Climate Vulnerability Monitor developed by the DARA organization and the Climate Vulnerable Forum. DARA is a non-profit organization aiming at improving humanitarian aid performance by conducting evaluations, research, and policy studies. We chose to work with DARA's Vulnerability Monitor instead of the vulnerabilities sometimes described within NDC documents in order to preserve the independence from national or supranational interests, and because the vulnerabilities were better documented. Our calculations can be run again using other databases, or when DARA issues an updated version of countries' vulnerabilities. No vulnerability is assigned to "Unknown hazard" and "Precipitation" in this repertory, as information was not available.