dataesr/scientific-paragraphs-categorization huggingface dataset:
I used either the non parametric Theil-Sen estimator or a least-squares estimator to assess the linear trend, the first being often preferred when data strongly depart from normality  (Hirsch et al. , 1991). The final results were not affected by this choice, and I only showed the results produced with the non parametric method implemented in the Kendall R package  (Mcleod, 2005). Our previous study based on the same raw dataset  (Choler et al. , 2021)  showed that NDVImax trends were robust to a +/-5% perturbation of the red and infra-red reflectances, a value which corresponds to uncertainties associated with MODIS products  (Miura et al. , 2000)  and so I did not reiterate this numerical simulation for this paper. To test for a collective, or per cluster, significance of anomaly, I used a test based on the counting of signs  (Huth and Dubrovsky, 2021).
All models were fitted using standardized anomalies of the response and the predictors. A stepwise procedure was used in which I first tested whether a random structure was justified and subsequently, whether including a particular fixed effect or an interaction between fixed effects was justified. The comparison of competing models was based on the Akaïke Information Criteria (AIC) and I retained models with the lowest AIC score. Models were fitted by maximizing the restricted log-likelihood with the lme function of the nlme R package  (Pinheiro et al. , 2022). The Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm was chosen for nonlinear optimization. Model parameters, variance explained by the fixed effects, or marginal variance, and variance explained by both fixed and random effects, or conditional variance, were estimated using the MuMIn R package  (Barton, 2023).
, 2008) , no dataset is available to depict trends and anomalies in stocking rates and land management at the scale of the European Alps. So far, there has been limited evidence that grazing activity has a significant effect on the NDVImax of mountain grasslands in the southwestern Alps  (Carlson et al. , 2017) , probably because extensive grazing by sheep predominate. Further studies should examine whether similar patterns are observed in more intensively managed livestock systems. My findings pointed out an overall positive effect of snow cover on canopy greenness. Snow cover is a complex ecological factor having multiple direct and indirect effects on vegetation activity and phenology  (Gao et al. , 2013; Choler, 2015; Walker et al. , 1993). Control of the length of the growing season, reduced risk of early frosts because of snow's insulating properties, and supply of water and nutrients by the melting snowpack are among the most commonly cited processes.
Acknowledgements Thanks are due to  Brad Carlson ,  Arthur Bayle  and  Simon Gascoin  for fruitful discussions during the preparation of this manuscript. This work received funding from the  LIFE PASTORALP  project ( LIFE16 CCA/IT/000060 ) and from the  Agence Nationale de la Recherche (ANR)  in the framework of the project  TOP, Trajectories of agrO-Pastoral systems in mountains  ( ANR-20-CE32-0002 ). LECA  is part of the  Recherche Labex OSUG@2020  ( IA-10-LABX-0056 ). https://doi. org/10. 5194/bg-2023-74  Preprint. Discussion started: 2 May 2023 c Author(s) 2023. CC BY 4. 0  License.
Observations We use four monthly reconstructed SST data sets to assess historical ENSO variability during 1861-1980. These data sets are COBE-SST2 on a 1° grid  (Hirahara et al. , 2014) , ERSST. v4 (Huang et al. , 2015; W. Liu et al. , 2015)  and ERSSTv5  (Huang et al. , 2017)  on a two-degree grid and KAPLAN EXTENDED v2 on a five-degree grid  (Kaplan et al. , 1998). We compute the mean over these four SST data sets as well as one standard deviation. For each data set, we calculate the Niño 3. 4 ENSO index by averaging the detrended SST anomalies over the Niño 3. 4 area (5°N-5°S, 170°W-120°W). The mean of the Niño 3. 4 index depicts a significant ENSO period at 2-4 yr during 1861-1980 (Figure  1c ).
Data Availability Statement COBE-SST2 data are publicly available at https://ds. data. jma. go. jp/tcc/tcc/products/elnino/cobesst/cobe-sst. html. ERSST. v4 & v5 data are publicly available at https://www. ncei. noaa. gov/products/extended-reconstructed-sst. KAPLAN EXTENDED v2 data are publicly available at https://iridl. ldeo. columbia. edu/SOURCES/. KAPLAN/. EXTENDED/. v2/. ssta/datafiles. html?Set-Language=en. The source code of CCSM4 is available at https://www. cesm. ucar. edu/models/ccsm4. 0/. Model configuration parameters used to run the model experiment are available in W. Liu et al. (2020) , https://www. science. org/doi/10. 1126/sciadv. aaz4876. The data to generate Figures  1 2 3 4 are available at Zenodo DOI: https://doi. org/10. 5281/zenodo. 7783335.
, 1990; Fan et al. , 1998; Bousquet et al. , 2000; Gloor et al. , 2000; Gurney et al. , 2002; Peters et al. , 2007; Chevallier et al. , 2010; Peylin et al. , 2013; Rödenbeck et al, 2018a Rödenbeck et al, , 2018b;; Friedlingstein, et al. ,2022]. Similar studies have also been carried out for CH4  [Houweling et al. , 2017]  and N2O  [Schilt et al. , 2010; Thompson et al. , 2019]. When all available datasets are used in those applications the users usually assume that these datasets are compatible and consistent over time. The applications may be limited by various types of inconsistencies between the datasets, including differences in scales or scale realizations and in sampling systems or procedures etc. When persistent bias exists between laboratories, the applications such as flux estimates derived by modelling systems using combined datasets on various spatial domains and temporal scales can have large uncertainties  [Masarie et al.
Data Preparation All measurements used in this study have been screened by the originating laboratory to ensure that each sample and subsequent measurement have not been compromised during collection and analysis. Each laboratory determines their own criteria for the quality control of their data and assigns the flags "valid", "invalid" or "suspected". These data files were provided to us by individual laboratories and have specific time stamps, which can be found in Table  S2. These time stamps identify the state of the data used in this study, in terms of scale updates/ corrections etc. , which is important information because the same datasets may be found in other data-repositories as updated versions with scale changes and /or https://doi. org/10. 5194/amt-2023-99 Preprint. Discussion started: 8 June 2023 c Author(s) 2023.
The 95% CI is computed using methods described by  [Campbell et al. , 1988]. In this way, our initial statistics should not be unduly influenced by outliers. The final derived annual median deviations are compared to the target goals outlined by the WMO GAW greenhouse gas program to assess the level of agreements of individual datasets with the reference laboratory. In addition to the assessment of individual laboratory co-located comparisons, we attempt to estimate the overall level of grouped agreement from multiple measurement records for each species using two approaches. The first approach provides the 95 th percentiles of the individual differences of all laboratory's measurements relative to NOAA's or INSTAAR's corresponding observation. However, because variations in NOAA's or INSTAAR's observational records might impact the results, we also report a second proxy for the level of grouped agreement, i.
When persistent differences exceed the WMO/GAW recommended targets, we then consider results from same-flask and samecylinder experiments to confirm the differences if data is available. To support the results at Alert, the corresponding comparisons at MLO and at CGO are also evaluated. We recognize that for some species, the network comparison goals may not be currently achievable within current measurement and/or scale transfer uncertainties and that these https://doi. org/10. 5194/amt-2023-99 Preprint. Discussion started: 8 June 2023 c Author(s) 2023. CC BY 4. 0 License. goals are targeted for application areas which require the smallest possible bias among different datasets for the detection of small trends and gradients. However, there are, of course, other application areas where such tight comparison goals may not be required, such as in urban emission estimates, long-term trend analysis, as well as in some regional modelling studies where uncertainties in air transport, for example, overshadow measurement uncertainties.
(e) and Table  8 ) shown as the yellow lines (no individual data points are shown) with a comparable result of ± 0. 34 ppm (N=905). The observed measurement differences (as annual medians) found in this study can also provide a first estimate of time dependent uncertainties of observations from a single laboratory. To assess the impacts of those uncertainties on related applications (e. g. , longterm trend analysis), we estimate long-term trends of CO2 from the six individual datasets (CSIRO, MPI-BGC, UHEI-IUP, SIO, ECCC, NOAA) for various 11 and 12-year time periods  (2005-2016, 2005-2015, 2006-2016 ) via Nakazawa's curve-fitting routine  (Nakazawa et al. , 1997). Table  S10  shows very consistent results for these applications. Coplen, 1994; Hofes, 1996; Trolier et al. , 1996]. A delta value defined here is the relative deviation of two isotopic ratios between a sample and the standard, i.
e. , the primary VPDB-CO2 or VPDB scale (VPDB: Vienna Pee Dee Belemnite). As the numerical value of a relative deviation is usually very small (close to 10 -3 ), it is normally multiplied by 10 3 and expressed in permil (‰) as in the following relationship  [Coplen, 1994; Coplen et al. , 2002] :  13 Csamp/VPDB-CO2 = [(( 13 C/ 12 C) sample/( 13 C/ 12 C)VPDB-CO2)-1] x 10 3 ‰ There is no single approach to the realization of the VPDB scale amongst individual laboratories (Table  5 ); in other words, although the laboratories have created local scales relative to VPDB through a link to NBS19, small inaccuracies in establishing this link may introduce scale differences between the measurement records. This should be kept in mind while interpreting the differences between the data records. S4. The overall median difference results (Fig.
Summary and Conclusions We presented a comparison of measurements of CO2, CH4, N2O, SF6, and the stable isotope From this work, we find that the co-located atmospheric CO2 and CH4 measurement records from Alert by CSIRO, MPI-BGC, SIO, UHEI-IUP, ECCC, and NOAA are compatible to the WMO network compatibility goals within ±0. 1 ppm CO2 and ±2 ppb CH4 at the 95% CI, respectively, over the 17-year period. In addition, we find that the co-located comparison programs at MLO and CGO show similar agreement levels to those at Alert within a range of ±0. 1 ppm for CO2 between CSIRO, SIO and NOAA records and within a range of ±2 ppb for CH4 between CSIRO and NOAA records. An important caveat to these CO2 and CH4 results is that we often observe periods where the biases between datasets exceed the WMO target levels and may persist as systematic bias for months or years, which could impact our observed compatibility.
51 to +0. 53 ppm for CO2, -0. 09 to +0. 07 ‰ for  13 C-CO2, -0. 50 to +0. 58 ‰ for  18 O-CO2, -4. 86 to +6. 16 ppb for CH4, -0. 75 to +1. 20 ppb for N2O, and -0. 14 to +0. 09 ppt for SF6, respectively. Using another alternative approach as discussed in Sect. 2. 6. , we provide the means of the 2-sigma of each weekly sampling episode, involving all participating laboratories over the entire available time period, which are ±0. 37 ppm for CO2, ± 0. 06 ‰ for  13 C-CO2, ±0. 31 ‰ for  18 O-CO2, ± 3. 62 ppb for CH4, ±0. 64 ppb for N2O and ±0. 09 ppt for SF6, respectively. Results from this analysis reveal overall cumulative differences due to errors introduced at one or more steps in the entire atmospheric measurement process, including sampling and analytical procedures. In summary, this study assesses the level of measurement agreement among individual programs by comparing co-located flask air measurements and provides more confidence on the uncertainty estimation while using those datasets either individually or collectively in various applications.
The long-term increases in CO2 concentrations are 23. 62 (2. 15 ppm/year) ± 0. 40 ppm (2-sigma) for 2005-2016, 21. 11 ± 0. 38 ppm (2-sigma) for 2005-2015, and 20. 87 ± 0. 22 ppm (2-sigma) for 2006-2016, respectively. The relative differences between the independent datasets are within a narrow range of 1. 5 -2. 4 %, indicating that reliable results can be achieved from those individual datasets for long-term trend analysis (>10 years). It is likely that much larger relative uncertainties would be involved in annual growth rate determination using the corresponding datasets. 3. 2  13 C of CO2Stable carbon isotopic ratio measurements in CO2 are reported commonly as delta values [McKinney et al. , 1950; Craig, 1957; Faure, 1986; O'Neil, 1986; Gonfiantini, et al. , 1993;      https://doi. org/10.
75 AE 0. 07 eV which is far below the core hole lifetime broadening. Thus, the main fraction of the broadening can be traced back to an increase of the source size caused by spatial beam jitter and/or drift during the first experiment. We also determined the instrumental resolution using the elastic line measured with a monochromatic beam, which is in good agreement with the data presented from the emission experiments (see Section 5). The integrated efficiency of the spectrometer for an energy bandwidth FWHM of the rocking curve is calculated by the angular acceptance along the non-dispersive direction l c multiplied with the integrated reflectivity R, the latter representing the area under the rocking curve of the crystal. The crystals with l c = 110 mm are placed at a distance of 250 mm from the sample which corresponds to a horizontal angular acceptance of 433 mrad.
Testing of pulse-resolved XES from a DAC In a second DAC experiment  (Cerantola & Sternemann, 2022)  on FeS in Ne at 32 GPa, we tested the efficiency of Fe K XES from a sample in a DAC using a single pulse of photons at full transmission. We used a combination of three Si(531) and one Si(111) analyser crystals to measure the Fe K and K fluorescence at the same time. The signal of one crystal each was accumulated over four pulses showing the highest intensity, to showcase the possible signal intensity of the K and K signal when using four crystals of the same type. For comparison, we accumulated the signal over 8000 trains also at full beamline transmission. Fig. 5  shows the difference in quality between the high-noise single pulse and the highquality data integrated over 8000 pulses at 10 Hz (red).
Acknowledgements We acknowledge  European XFEL in Schenefeld, Germany , for provision of X-ray free-electron laser beam time and beamlines 828  Johannes M. Kaa  et al. von Hamos spectrometer for DAC experiments at EuXFEL would like to thank the staff for their assistance. Data recorded for the experiment at the European XFEL are available at 10. 22003/XFEL. EU-DATA-002624-00 , 10. 22003/XFEL. EU-DATA-002941-00 ,  10. 22003/XFEL. EU-DATA-002804-00  and  10. 22003/XFEL. EU-DATA-003478-00. We would like to thank  DELTA  for beam time at BL9 for characterizing the analyser crystals. We thank  Roberto Verbeni  ( ESRF ) for providing an analyser crystal for first test studies. Open access funding enabled and organized by  Projekt DEAL.
1 wt% for each cristalline phase, working at standard conditions (Cu Kα radiation wavelength: 1. 543 Å, tension: 45 kV, current: 40 mA, 2θ angle range: 7 • -80 • , step size: 0. 067 • , step time: 29. 89 s). Mineralogical phases were identified from the JCPDS and COD databases. The particle size distribution was determined on clay mixtures using a Mastersizer 2000 laser granulometer.
Acknowledgments The authors gratefully acknowledge the financial, scientific, and technical supports from  TERREAL Company (CRED)  to this research. The support from l 'Agence Nationale de la Recherche (ANR)  under the  LabCom RESPECTc  project is also acknowledged. We also would like to thank the staff at  CEMES-CNRS , and  ICA and RAPSODEE Center  ( IMT Mines Albi ) for technical assistance in this research.
Horizontal Visibility A SENTRY™ Visibility Sensor 1 (SVS1) instrument was used to measure visibility. The instrument estimates the scattering of visible light by the atmosphere to calculate the extinction coefficient, μ, which represents the amount of attenuation of a beam due to scattering and absorption by aerosols. Horizontal visibility data is recorded every minute. Visibility data from SVS1 are validated and extensively used for detecting occurrences of fog and dust at the Abu Dhabi location  (Nelli et al. , 2022; Temimi et al. , 2020a; Weston et al. , 2022).
Detection of fog over experiment site Fog is a meteorological condition where horizontal visibility becomes less than a conventionally accepted threshold of 1000 m due to the presence of water droplets. In this study, fog events are detected based on the horizontal visibility measured at a height of 10 m. First, all time-steps where horizontal visibility is less than or equal to 1 km are labeled as potential fog events  (Fonseca et al. , 2023; Mohan et al. , 2020; Nelli et al. , 2022; Temimi et al. , 2020a). Afterwards, a visual inspection of 15-minute SEVIRI fog RGB images is conducted to confirm the formation of fog on the selected dates and times. Additionally, cloudy or rainy conditions are filtered out using the 30-min merged IR brightness temperature data from geostationary satellites  (Nelli et al.
Fog deposition flux calculations In this section, we describe how the total vertical fog water flux between the surface and the atmosphere and the fog deposition velocity are estimated using data from the fog monitor sampler and ultrasonic anemometer installed at BNPP. Before calculating any quantity related to fog deposition, we first pre-processed fog monitor data for all recorded fog events at the site. We recall that the fog monitor provides information on LWC, the number of water droplets per bin droplet size, and the Median Volume Diameter (MVD) every 1 second. The data were re-sampled to 1-minute frequency. The concentration number 𝑁 𝑖 (𝑡) per size bin for each time step t (# cm - 3  ) is calculated by 𝑁 𝑖 (𝑡) = 𝑛 𝑖 (𝑡) 𝑑𝑤 𝑖 * 𝑆𝑉(𝑡) (2) where 𝑛 𝑖 (𝑡) and 𝑆𝑉(𝑡) are the count of droplets in size bin i and the sampling volume in cm 3 at time t, respectively; 𝑑𝑤 𝑖 is the width of the size bin i in logarithmic scale calculated as 𝑑𝑤 𝑖 = 𝑙𝑜𝑔(𝑢 𝑖 /𝑙 𝑖 ), with 𝑢 𝑖 and 𝑙 𝑖 being the upper and lower bounds of the size bin i, respectively.
In both, FLEXPART is integrated over 48 hours from 00:00 UTC on 15 February, 2021, and the radioactive release is assumed to start 20 hours after initialization. The dispersion model output data is recorded every 15 minutes. Figures  12a-b  show the spatial distribution of the total deposition of 137 Cs to the ground (sum of dry and wet deposition) integrated from 08:15 to 08:30 LT on 16 February, 2021, respectively from the baseline simulation and that carried out under foggy conditions. As expected, we observe a noticeable downwind increase in ground deposition caused by the fog deposition process, which will subsequently lead to higher levels of soil. Each model grid cell has an area of 1 km 2. Figure  12c  shows the temporal variation of the contaminated areas for both the reference dispersion simulation and that with the effect of fog deposition included.
Conclusions The main objective of this paper was to characterize the deposition of fog droplets around the Barakah nuclear site and to perform a modeling simulation experiment to explore the impact of fog deposition on the ground deposition of radionuclides in the event of a radiological emergency at the site leading to the release of radioactive materials into the air. State-of-the-art in-situ instruments including a ground-based radiometer, fog monitor sampler, ultrasonic anemometer, and visibility meter have been deployed around the site to probe fog events during the winter seasons of 2021 and 2022, and twelve cases of fog were recorded. The microphysical properties for each fog event were inferred by analyzing data of horizontal visibility, liquid water content (LWC), number concentrations (Nc), and median volume diameter data (MVD).
Acknowledgment We would like to thank  Khalifa University 's high-performance computing and research computing facilities for their support of this research work. This research work was supported by the  Federal Authority for Nuclear Regulation (FANR)  through the research project  Modeling of Radionuclides Dispersion in the UAE Environment (MORAD). Nawah and SHAMS power companies are both acknowledged for facilitating the deployment of instruments and conducting the field campaign in the UAE desert.
Materials and Methods For this study, measurements of the Hg concentrations (n = 70) and isotopic compositions (n = 22), TOC contents (n = 60), and major and trace element concentrations for sedimentary rocks and interbedded volcanic ash beds (n = 55) were made. These data are complemented by U-Pb zircon geochronology based on single zircon grains from five volcanic ash beds in the two sections (Table  S1  in Supporting Information S1).
Organic Carbon Content All TOC content data for Laxian (n = 26) were acquired during the present study. For the Potuo section, 10 data points are from  Bagherpour et al. (2020)  and an additional 28 samples have been analyzed for this study. Organic matter (OM) content and preservation were assessed by Rock-Eval pyrolysis using a Rock Eval™ 6 with the analytical procedures described by  Behar et al. (2001). This included measurements of TOC content, pyrolysis temperature (T max ), hydrogen index (HI) and oxygen index (OI). The standard IFP160000 was used for calibration of the samples and instrumental precision was about 0. 1 wt. % for TOC, 10 mg HC/g for HI and 10 mg CO 2 /g for OI.
Uranium and Pb isotopic compositions were measured on IsotopX Phoenix TIMS at the University of Geneva. Pb was measured using a dynamic peak jumping routine on a Daly ion counting system, while uranium was measured as UO 2 in static mode using 10 12 Ω resistor Faraday amplifiers for samples POT66T, POT67T, POT68T, and using ATONA Faraday amplifiers (with a 30 s integration time) for samples LAX8T and LAX10T. The measured isotopic ratios were corrected for interferences of U-Pb dates of zircons were calculated using data reduction software Tripoli and ETRedux  (Bowring et al. , 2011; McLean et al. , 2011) , and all uncertainties are reported at the 95% confidence interval. The interpreted weighted mean age is reported in the format: "weighted mean" ±/X/Y/Z, where X corresponds to analytical uncertainty, Y to analytical + tracer uncertainty and Z to analytical, tracer and decay constant uncertainty  (Schoene et al.
, 2006). All zircon 206 Pb/ 238 U dates were corrected for 230 Th-238 U disequilibrium using a Th/U magma of 3. 5 ± 1. 0. We discuss dates at the precision level of analytical uncertainty (X), since most U-Pb data in the relevant literature were obtained using the same isotope tracer (ET2535) and mode of analysis (ID-TIMS), effectively eliminating inter-lab uncertainty. ). Excess scatter is indicated by the elevated MSWD value and is commonly observed for the synthetic ET100 solution. This is potentially derived from instrument instability and from the sample preparation process  (Schaltegger et al. , 2021).