dataesr/scientific-paragraphs-categorization huggingface dataset:
Typical acquisition times were 12 accumulations of 5 s integrations (for a total of 60 s per final spectrum), depending on the sample. The grating was fixed to center at 1050 cm -1 throughout spectrum acquisition. Random spikes due to cosmic rays were removed as part of the process of accumulating and combining multiple spectra. The acquired data (spectra and maps) were processed using the LabSpec 6 software. All spectra were pre-processed by truncating to 1800-250 cm -1 , de-noising and by baseline correction by a 7°polynomial function, before being normalized to the broad background fluorescence intensity between 1350 and 1000 cm -1. Regions with significant Raman features above the fluorescent background were identified by screening each pixel; extracted spectra were compared to reference spectra on the RRuff database  (Lafuente et al.
Backward trajectories of dust particles The LAGRANTO Lagrangian analysis tool  (Sprenger and Wernli, 2015)  was used to calculate air parcel trajectories, using horizontal and vertical wind data from forecast models. The tool used meteorological data from ERA-Interim  (Dee et al. , 2011) , produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). Trajectories of 4 µm dust particles were calculated by adding the sedimentation of dust particles to the vertical movement of air in the calculation of air parcel and dust trajectories. Four µm was chosen for dust deposition modeling because it is representative of both commonly-deposited dust particles on Hawaii, and of the smaller dust particles observed in this study  (Jackson et al. , 1971 ). The terminal sedimentation velocity was calculated using Stokes' equation.
Micro-Raman spectroscopy Micro-Raman spectroscopy identified apatite in the arid sites 1 and 2 (see Fig. 3  bottom left, and Table  1 ), corroborating the XRD and XANES analyses. The observed Raman peak at 965 cm -1 overlapped with that of apatite reference spectra from the RRUFF database (Supplementary Fig. S7 ). In these sites, micro-Raman spectra also indicated forsterite and anatase, but no quartz. At the most humid site, in the surface horizon (site 6A), on the other hand, micro-Raman spectroscopy revealed an abundance of quartz crystals, corroborating the XRD results and indicative of the expected dust deposition gradient. A Raman band of apatite was also detected at site 6A, though the apatite signal was much weaker than in arid sites (Fig. 3 , bottom right). The detection of apatite at site 6A complemented the micro-XANES analyses, which had found Ca-P, but were not conclusive regarding the type of Ca-P mineral.
Acknowledgments The  CLIMP  project (Forms and dynamics of soil phosphorus along a climosequence on basalt-derived soils) was funded by the  Swiss National Science Foundation  (Project number  200021_162422 ). CV thanks the  German Research Foundation  ( VO 1794/4-1 ) for financial support. Synchrotron analyses were conducted on the LUCIA beamline at  Synchrotron Soleil  (proposal 20160117), KMC-1 beamline at  Helmholtz-Zentrum Berlin  ( BESSY II ), ID21 beamline at the  European Synchrotron Radiation Facility , beamline 14-3 at  Stanford Synchrotron Radiation Source , and the VLS-PGM beamline at the  Canadian Light Source. We thank these institutions for allocation of beamtime. The  Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory , is supported by the  U. S.
1. 1, 1. 2) is run multiple times with the different quantifications of the key model parameters (previously determined in the sensitivity analysis). We conducted a Monte Carlo analysis using 10,000 stochastic simulations for each tracer ( 222 Rn and 224 Ra) where different parameter combinations are produced. In these simulations, (1) the approach used to quantify each key parameter in the mass balance is randomly selected between the different methods evaluated, and (2) model parameters are generated via normally distributed random values based on the mean and standard deviation (SD) of each parameter. The probability density function that best fits the data distribution obtained from the Monte Carlo simulations was determined using least-squares fitting (SciPy library for the Python programming language).
e. , friction) of vegetated areas vs. open water complicate extrapolating the land-based wind measurements to open water conditions  (Vachon and Prairie 2013). To evaluate the conceptual uncertainties associated with the determination of gas transfer velocities (k) for La Palme Lagoon, we compare how different parameterizations of gas transfer velocities influences the results. First, different ways of calculating wind speeds are used as input parameters for wind-k relationships, using the empirical relationships of  MacIntyre et al. (1995)  as a base equation. Different empirical and experimental approaches are then compared. Different wind speeds to estimate k. Hourly data on wind direction and speed at the nearby (< 5 km) meteorological station "Leucate" was extracted from the database of the French meteorological service (Météo France).
Acknowledgments This research is a contribution to the  ANR @RAction chair  ( ANR-14-ACHN-0007-01-T Stieglitz ) and  Labex OT-Med  ( ANR-11-LABEX-0061 , part of the  "Investissements d'Avenir" program  through the  A* MIDEX  project  ANR-11-IDEX-0001-02 ) funded by the  French National Research Agency (ANR). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie grant   748896. V. Rodellas  acknowledges financial support from the  Beatriu de Pinós postdoctoral programme of the Catalan Government  ( 2017-BP-00334 ). P. van Beek acknowledges financial support from the  ANR  ( MED-SGD  project,  ANR-15-CE01-0004 ). We thank  S. Thomas  ( Labex OT-Med ) for constructive comments and  M. Diego-Feliu  for his help on statistical analysis.
Increasingly, users have asked for a public overview of the existing content. A first version of a searching tool is now available online. Explorer lists the databases whose creators have filled in the extended metadata and have accepted the referencing. The user can search by language, taxonomic group, fossil or current, geography, habitat, and key words. New developments of Xper3 are in progress. Some have a first version online, others are in production and the last ones are future projects. We will present an overview of the different projects in progress and for the future.
Xper2-specific features, such as merging states, adding notes, adding definitions and/or illustrations in the description tab, having different ways of sorting and filtering the descriptors during an identification (by groups, identification power, alphabetic order, specialist's choice) have to be added to Xper3. A new tab in Xper3's interface is being implemented to give an access to various analysis tools, via API (Application Programming Interface), or R programming code: • MINSET: minimum list of descriptors sufficient to discriminate all items • MINDESCR: minimum set of descriptors to discriminate an item • DESCRXP: generating a description in natural language • MERGEMOD: proposing to merge states without loss of discriminating power • DISTINXP, DISTVAXP: computing similarities between items or descriptors One last project that we would like to implement is an interoperability between Xper3, platforms with biodiversity data (e.
Biogenic VOC emissions were estimated by the Model of Emissions of Gases and Aerosols from Nature (MEGAN version v2. 1) algorithm  63. Biomass burning emissions were from the Global Fire Emissions Database version 4 (GFED4)  64. A more detailed model description and configuration can be found in Lu et al. (2019)  13. We designed a BASE simulation and a total of 33 sensitivity simulations to examine the role of anthropogenic and natural/biogenic (including soil and lightning) NO x emissions in ozone source attribution. The standard simulation (BASE) applied the anthropogenic and natural/biogenic emissions as described above. We then assessed the ozone source attribution using the standard "bruteforced" zero-out approach. Sensitivity simulations were conducted by excluding anthropogenic emissions of all primary emitted species over China (NoAnthro), excluding soil NO x emissions (NoSoil), excluding lighting NO x emission (NoLight), and excluding the combinations of these emissions (NoSoilLight, NoAnthroSoil, NoAnthroLight, and NoAnthroSoilLight) (Supplementary Table  3 ).
Data availability Code availability GEOS-Chem model codes, including the Berkeley-Dalhousie Soil NO x Parameterization (BDSNP), are available at https://github. com/geoschem/geos-chem/releases/tag/v11-02rc. WRF-Chem model codes are available at https://github. com/wrf-model/WRF/ releases/tag/V3. 6. 1.
Acknowledgements: This research is funded by the  French Agence nationale de la recherce , project  ANR-17-CE01-0008. We acknowledge the support of the  INRAE Research Centre of Grenoble  for the laboratory and the instrumental equipment. We thank  Christian Eymond-Gris ,  Frédéric Ousset , and  Xavier Ravanat  for the technical support in the development of the flume. We thank  Maarten Bakker  for helping in the analysis of the seismic data.
Data availability: The datasets analysed during the current study are available and temporary shared on this link: https://www. dropbox. com/sh/n3oqyl06csmnipg/AABnNuhwblRZIAhSRFt6EbEba?dl=0 In case of acceptance we plan to give access to all data through the data repository platform Zenodo with an associated Digital Object Identifier (DOI). Video supplement: The videos used for the analysis are temporary available on this link: https://www. dropbox. com/sh/ctyxz64hn0di41s/AAAgvoEkXihVE44norGPC-E8a?dl=0 The DOI's generation through the AV Portal of TIB Hannover is in progress.
[15] H 2 O are considered. The cK model, based on updated parameters from Riviere and Soufiani[15], is used to describe the gas radiative properties. For H 2 O, 44 spectral bands are considered up to 9200 cm -1. CO 2 absorbs radiation in 17 of these bands. For each gaseous component, a 7-point Gauss quadrature is used per band. In the 17 overlapping bands, 49 quadrature points are used. The cK database is then made of 1022 pseudo-spectral points.
The authors gratefully acknowledge the financial support within the  EU Horizon 2020 Soot Processes and Radiation in Aeronautical Innovative Combustion (SOPRANO)  project (Grant Agreement No. 690724 ). Furthermore, the work was performed using HPC resources from  GENCI-CINES  (Grant  2019-A0062B10159 ) as well as from the "Mésocentre" computing center of  Centrale-Supélec  and  École Normale Supérieure Paris-Saclay  supported by  CNRS  and  Région Île-de-France  (http://mesocentre. centralesupelec. fr/).
Therefore, the present study evaluated the potential impact of burrow disturbance and chick-handling in FP and CDP. During the 2018/19 breeding season, a group of control burrows (FP n = 20, and for CDP n = 16) was monitored monthly for each species and the hatching, fledging and breeding success were compared to those of the study burrows. To investigate the variability in the environmental conditions experienced by the two study species, average SST data for specific periods before, during and after the breeding season were obtained for areas considered important to the study species (Fig  1 ). More specifically,the summer period plays a key role in the growth and reproduction of coastal krill in Bass Strait  [60] , the main prey of both FP and CDP  [45]. Therefore, SST during the summer preceding each breeding season in Bass Strait (38-40˚S, 144-148˚E; Dec-Feb) was identified as a potentially important variable for both species.
All SST data were downloaded from the Copernicus Marine Services platform [http://marine. copernicus. eu/global_analysis_forcast_phy_001_024] with a spatial resolution of 0. 25 x 0. 25 degrees. All statistical analyses were conducted in the R statistical environment 4. 0. 0 (R Core Team 2020). Generalized linear mixed-effects models (GLMMs) were used to investigate the effect of SST on breeding success (binomial family) and phenology (Gaussian family) using the "nlme" and "lme4" packages  [61]. Bird nest was fitted as random effect to account for pseudo-replication, as the majority of the nest were sampled during the all four years of the study. Models were ranked based on their Akaike's Information Criterion (AIC) and were checked to ensure normality and homoscedasticity of residuals  [62]  before further statistical analysis.
Note that, while the decrease of the analysis-error aspect tensor, from the background value for dimension larger than one and to a wider family of covariance models. Two consistent implementations of the PKF for the VLATcov has been presented for the heterogeneous Gaussian VLATcov model: the PKF O2 and its local homogeneous version, the PKF O1. This study constitutes a theoretical contribution to the realm of data assimilation, featuring analytical results for the analysis uncertainty and illustrating these results using numerical experiments. The validation of the PKF was conducted in a 2D test-bed. A univariate numerical framework with anisotropic forecast error correlation functions was introduced to show the ability of the PKF analysis step to reproduce the KF statistics. Then an experiment of several assimilations cycles has shown the ability of the PKF to reproduce the KF statistics which have been computed from an ensemble estimation.
Polarimetry in the ultraviolet Since polarization simulations of starlight reflected by an exoplanet extending to the ultraviolet (UV), where P is believed to be particularly large, have never been performed before, we employed the PyMieDAP code  [22]  to show the exquisite sensitivity of UV polarization measurements to the physical properties of planetary atmospheres. Figure  3  shows flux (top row) and P (bottom row) as a function of phase angle at three UV wavelengths (150, 200, and 300 nm) for a HD189733b-like exoplanet hosting a clear atmosphere (left) and an atmosphere dominated by NH 3 (middle) or MgSiO 3 (enstatite; right) aerosols. The three curves have very distinct behaviors, particularly as a function of wavelength, clearly indicating the potential of UV spectropolarimetry for gaining critical information on planetary atmospheres, complementary to that provided by flux-only measurements.
Our goal was to estimate how sensitive snow buntings' performance may be to increasing Arctic temperatures, given their heat dissipation capacity. We first used thermal physiological data to construct a thermoregulatory polygon and predict the threshold temperatures at which sustainable performance (e. g. , birds actively provisioning nestlings) would be expected to decline in buntings maintaining thermal balance (i. e. , heat produced = heat dissipated). We then compared the thermoregulatory polygon prediction to both operative and air temperatures measured in the field at two latitudinally distant breeding sites to evaluate how heat constraints on bunting performance (i) differed between a low and high Arctic region, and (ii) could translate into site-specific impacts on reproductive performance and success.
In the high-Arctic, we recorded operative temperatures every 5 minutes from 22 May to 7 September 2019 and models were deployed over six separate periods, each lasting approximately 7 days (due to iButton memory limitations). After 7 days, we downloaded the operative temperature data and redeployed the 3D models to a new location. In the low-Arctic, we originally deployed 22 models once and recorded operative temperatures continuously from 11 June to 19 July 2019 at 2-min intervals. Unfortunately, polar bears damaged many models, so only 13 of our original 22 models were usable. However, the distribution of snow bunting breeding pairs at the low-Arctic site is much smaller in area than in the high-Arctic and these 13 models thus still provided sufficient geographic coverage of East Bay's microclimates experienced by snow buntings.
At both study sites, we collected air temperature data to compare against operative temperatures. In the high-Arctic, the downloaded meteorological data was measured at the National Oceanic and Atmospheric Administration's (NOAA) broadband radiation station located adjacent to the Global Atmospheric Watch (GAW) Observatory (82°28'N, 62°30'W). These data are 1-min averages of air temperature obtained at a height of 3 m above the ground using an aspirated Vaisala HMP-235 (PT100 sensor). In the low-Arctic, we collected an air temperature value every 30-min using six Kestrel weather meters (model 5500, Boothwyn, PA, USA) placed 2-3 m above ground level at separate locations across the study site.
We determined maximum dry thermal conductance by exposing birds to a ramped temperature protocol of gradually increasing air temperatures  [32]. We only included birds that tolerated air temperatures above 31. 5°C, representing the mean air temperature minus the standard deviation at which buntings started panting  [32] , as we assumed that birds that had initiated panting had reached their maximum thermal conductance  [49]. This resulted in the removal of only 1 bird from the final data set. At higher air temperatures, evaporative heat loss becomes significant and therefore must be accounted for in the calculation of maximum thermal conductance  [48]. We thus calculated maximum dry thermal conductance for each bird as follows: 𝐶 𝑚𝑎𝑥 = (𝑀𝑅 -𝐸𝐻𝐿) (𝑇 𝑏 -𝑇 𝑎 ) Eq. 2 where EHL represents evaporative heat loss measured during respirometry trials  [32].
Bay) We conducted all analyses in R 4. 0. 4  [50]. Over the course of the field season in the high-Arctic, we recorded a total of 843,773 individual operative temperature values from 68 3D models and a total of 107,092 air temperature values. In the low-Arctic, we recorded 405,000 individual operative temperature values from 13 models and a total of 10,803 air temperature values. We used these raw temperature data to create a time series of operative and air temperatures for each site averaged at 1-h intervals using the timeAverage function in the openair package  [51]. The discontinuous sampling protocol in the high-Arctic (e. g. , downloading data and redeploying models) resulted in 643 1-h gaps in our operative temperature time series. To estimate the percentage of time on a given day that buntings would have been behaviorally constrained from heat (see below) it was necessary to fill these gaps in our data.
Acknowledgements We sincerely thank the  2019 East Bay and Alert field  teams for their assistance. We are grateful to  Dr. Christina Semeniuk  for the use of spectrophotometer measurement equipment and to  Kevyn Gammie-Janisse  and  Chris Harris  for their assistance with spectrophotometer measurements and analysis. We are also indebted to  Lincoln Savi  for providing essential help with the 3D models.
The aerosol and chemistry feedbacks listed here contribute up to the order of -0. 2 W m -2 K -1. This is smaller in magnitude than the carbon cycle response to climate (of the order of 0. 5 W m -2 K -1 ;  Ciais et al. , 2013)  or the physical cli-mate feedbacks (of the order of 1-2 W m -2 K -1 ;  Sherwood et al. , 2020). Data availability. All data from the Earth system models used in this paper are available on the Earth System Grid Federation website and can be downloaded from https://esgf-index1. ceda. ac. uk/search/ cmip6-ceda/ (last access: 12 November 2020, ESGF-CEDA, 2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-1105-2021-supplement. Author contributions. Manuscript preparation was done by WC, GT, DO, RCG, CES, SF and additional contributions from all coauthors.
Acknowledgements. Gillian Thornhill, William Collins, Ramiro Checa-Garcia, Martine Michou, Fiona M. O'Connor, Dirk Olivié, Michael Schulz, Ada Gjermundsen, Catherine E. Scott and Martine Michou acknowledge funding received from the European Commission's H2020 Research Infrastructures programme. Catherine E. Scott acknowledges funding from the Natural Environment Research Council. Fiona M. O'Connor, Gerd Folberth and Jane Mulcahy were supported by the Met Office Hadley Centre Climate Programme. Ada Gjermundsen, Dirk Olivié and Michael Schulz were supported by the Research Council of Norway and by Notur/NorStore. Toshihiko Takemura was supported by the Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency, Japan; the Japan Society for the Promotion of Science (JSPS) KAKENHI; and the NEC SX supercomputer system of the National Institute for Environmental Studies, Japan.
This research has been supported by the European Commission, H2020 Research Infrastructures (CRESCENDO (grant no. 641816) and CONSTRAIN (grant no. 820829)), the Natural Environment Research Council (grant no. NE/S015396/1), the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (grant no. GA01101), the Research Council of Norway (grant nos. 229771, 285003 and 285013), the Notur/NorStore (grant nos. NN2345K and NS2345K), the Environment Research and Technology Development Fund (grant no. JPMEERF20202F01) of the Environmental Restoration and Conservation Agency, Japan; the JSPS https://doi. org/10. 5194/acp-21-1105-2021 Atmos. Chem. Phys. , 21, 1105-1126, 2021.
Financial support. support This research has been supported by the European Commission, H2020 Research Infrastructures (CRESCENDO (grant no. 641816) and CONSTRAIN (grant no. 820829)), the Natural Environment Research Council (grant no. NE/S015396/1), the Met Office Hadley Centre Climate Programme funded by BEIS and Defra (grant no. GA01101), the Research Council of Norway (grant nos. 229771, 285003 and 285013), the Notur/NorStore (grant nos. NN2345K and NS2345K), the Environment Research and Technology Development Fund (grant no. JPMEERF20202F01) of the Environmental Restoration and Conservation Agency, Japan; the JSPS https://doi. org/10. 5194/acp-21-1105-2021 Atmos. Chem. Phys. , 21, 1105-1126, 2021.
Abstract. The streamflow of the Yellow River (YR) is strongly affected by human activities like irrigation and dam operation. Many attribution studies have focused on the long-term trends of streamflows, yet the contributions of these anthropogenic factors to streamflow fluctuations have not been well quantified with fully mechanistic models. This study aims to (1) demonstrate whether the mechanistic global land surface model ORCHIDEE (ORganizing Carbon and Hydrology in Dynamic EcosystEms) is able to simulate the streamflows of this complex rivers with human activities using a generic parameterization for human activities and (2) preliminarily quantify the roles of irrigation and dam operation in monthly streamflow fluctuations of the YR from 1982 to 2014 with a newly developed irrigation module and an offline dam operation model.
68. It successfully explained the decline of total water storage in the YRB. In this study, we add a simple module describing the dam operations to further improve the model over the YRB. A simple dam operation model is developed and firstly coupled to ORCHIDEE to simulate the real streamflows in this study. Similar to other GHMs and LSMs, our dam operation model is based on generic operation principles due to a lack of related data. Recent dam models are developed from different perspectives, such as the agent-based model River Wave  (Humphries et al. , 2014) , the basin-specific model Colorado River Simulation System (Bureau of Reclamation, 2012), and the original dam module in the Variable Infiltration Capacity (VIC) model  (Lohmann et al. , 1998). However, the representation of dam operations in many global hydrological studies (e.