dataesr/scientific-paragraphs-categorization huggingface dataset:
ODE-wake method The results for 𝑓𝑓 presented in the former sections show that the accuracy of velocity profile affects directly the average velocity and therefore the friction factor values. Since, experiments and DNS show that the logarithmic law is not suitable in the outer region for high y+, it is important to use a more appropriate description of velocities. Log-wake law seems more suitable, the velocity profile is given by  [12] [13] [14] [15] [16] [17] [18] : With п is the Coles' parameter. Figure  (10)  shows that adding Coles wake function to the log-law allows a better description of DNS data for high y +. We use for the Coles' parameter a value of п equal to 0. 14  [13]. For the average velocity (equation 6), we use the trapezoidal rule for y + <20 and the "quad" function for the log-wake law.
Herein, the aurophilic interactions preexist, and preorganize, gold cluster reactive intermediates. Accordingly, and more importantly, the selectivity observed also differs from nanoparticles and heterogeneous gold catalysts reported to date. Deposition Numbers 2128908 (for 3), 2128909 (for 4), 2128910 (for 5), 2128911 (for 4 g), as well as 2144186 (for 4 b), 2144187 (for 4 c), 2144188 (for 4 d), 2144189 (for 4 e), and 2144190 (for 4 f) contain the supplementary crystallographic data for this paper. These data are provided free of charge by the joint Cambridge Crystallographic Data Centre and Fachinformationszentrum Karlsruhe Access Structures service.
Acknowledgements This work was supported by the  CNRS , the  Université de Bourgogne , the  Conseil Régional de Bourgogne-Franche-Comté  ( CHIMENE  project, PhD grant for T. -A. N. ), the  PIA-excellence ISITE-BFC program  ( COMICS: Chemistry of Molecular Interactions Catalysis and Sensors, for JCH ),  ANR   JCJC 2018 FITFUN  ( ANR-18-CE07-0015 , for R. J. ) and the  Fonds Européen de Développement Régional (FEDER). Calculations were performed using HPC resources from  CCUB (Université de Bourgogne). This work was granted access to the HPC resources of CINES under the allocation A0090807259 made by  GENCI , for PFL.
C5 Methodology: atmospheric inversions C5. 1 Inversion system simulations Nine atmospheric inversions (details of each are given in Table A4) were used to infer the spatio-temporal distribution of the CO 2 flux exchanged between the atmosphere and the land or oceans. These inversions are based on Bayesian inversion principles with prior information on fluxes and their uncertainties. They use very similar sets of surface measurements of CO 2 time series (or subsets thereof) from various flask and in situ networks. One inversion system also used satellite xCO 2 retrievals from GOSAT and OCO-2. Each inversion system uses different methodologies and input data but is rooted in Bayesian inversion principles. These differences mainly concern the selection of the atmospheric CO 2 data, prior fluxes, spatial resolution, assumed correlation structures, and mathematical approaches of the models.
The resulting loss of additional sink capacity amounts to 0. 9 ± 0. 3 GtC yr -1 on average over  2009 -2018 and 42 ± 16 GtC accumulated between 1850 and 2018 (Obermeier et al. , 2021). OSCAR, emulating the behaviour of 11 DGVMs, finds values of the loss of additional sink capacity of 0. 7 ± 0. 6 GtC yr -1 and 31 ± 23 GtC for the same time period  (Gasser et al. , 2020). Since the DGVMbased E LUC estimates are only used to quantify the uncertainty around the bookkeeping models' E LUC , we do not add the loss of additional sink capacity to the bookkeeping estimate. provided the data used in this Global Carbon Budget 2022 and the Global Carbon Project members for their input throughout the development of this publication. We thank Nigel Hawtin for producing Figs. 2 and 14. We thank Thomas Hawes for technical support with the data management pipeline.
The increasing awareness of global climate change puts more pressure on firms to reduce their environmental externalities. Managers long ignored this responsibility, which may erode business profits, going against their traditional goals. In this study, we examine the effect of top management's extrinsic incentives (i. e. , reward-driven motivation) on corporate environmental innovation strategy (i. e. , eco-innovation) using a large dataset of S&P1500 non-financial firms for 2000-2020. The results indicate that firms with greater levels of top-management compensation exhibit higher scores of eco-innovation engagement. The effect holds after we address the endogeneity problem through the quasi-natural experiment using the difference-indifferences analysis on the event of the Paris Agreement 2015.
| Data and sample We collect data on all the common stocks for the US listed in the S&P 1500 composite equity index for the period 2000-2020. The index covers the three leading equity indexes for the US, including the S&P 500, the S&P Midcap 400, and the S&P SmallCap 600, which represent up to 90% of the US equity market capitalization. 3 While the data on corporate eco-innovation are extracted from Thompson Reuters's Refinitiv Eikon (formerly ASSET 4), the accounting data on topmanagement compensation, corporate governance determinants, and other firm-year accounting data are collected from Thompson Reuters's DataStream. Our study excludes financial firms from our sample which feature the standard industrial classification (SIC) codes between 6,000 and 6,999. Following the previous literature  (Brogaard et al.
, 2017; Trinh et al. , 2021) , if the firm accounting data are missing for the current year (t 0 ), the study replaces them with the previous non-missing values (tÀ1). To mitigate the potential effects of outliers, the study winsorizes all the variables at the 1st and 99th percentiles, except corporate eco-innovation. Our ultimate sample includes 1,154 listed nonfinancial firms with a total of 11,814 firm-year observations for all the selected variables.
Acknowledgements We are grateful to the  Museo Aragonés de Paleontología  and the staff of the  Fundación Conjunto Paleontológico de Teruel-Dinópolis  for providing the specimens, as well as to the  Dirección General de Patrimonio Cultural of the Aragón Government (Spain)  for the permission to excavate in San Just (exp. 171/2007). We are indebted to  Rafael López del Valle  for the preparation of the amber pieces. We thank  Juli Pujade-Villar  for their advice and discussion. We want to acknowledge the editor  Eduardo Koutsoukos  and the reviewers Alexandr P. Rasnitsyn and  Corentin Jouault  for their comments and suggestions that have improved the manuscript text, and for their corrections on wing venation nomenclature and taxonomic issues. This study is a contribution to the project  CRE CGL2017-84419  funded by the  Spanish AEI/FEDER  and the  EU.
Mineral dissolution rates control the temporal evolution of many natural processes on the surface and upper crust of the Earth. This is the first of a series of papers describing the development and implementation of a comprehensive and internally consistent database of the dissolution rates of the major rock-forming minerals. In this paper, we describe the retrieval and regression of the dissolution rates of the primary rock-forming silicates, including quartz and other SiO 2 polymorphs, feldspars, pyroxenes, amphiboles, micas, volcanic glass, and olivines. Owing to the limited fluid chemistry data available for many of the published experimental studies, BET surface area normalized mineral and most glass dissolution rates (r) were regressed using where A i stands for pre-exponential factors, a H + corresponds to the hydrogen ion activity, n i refers to reaction orders, T designates absolute temperature, R denotes the gas constant, E ai represents activation energies, σ stands for Temkin's average stoichiometric number, and A denotes the chemical affinity of the dissolution reaction.
Where relevant data were lacking, rates were extrapolated assuming that mineral dissolution follows similar trends within the same mineral group. Regression was performed using least-squares algorithms. The average difference between calculated and measured rates ranged from 0. 1 to 0. 7 log units depending on the mineral. The obtained rate equations were implemented into the carbfix. dat database for direct use in the PHREEQC software but can be extended to other geochemical modeling software, such as TOUGHREACT.
Zhang et al. (2019)  incorporated some of these rate equations into a PHREEQC script to facilitate their use in geochemical modeling calculations, but made no effort to update or verify the internal consistency of these rates. This study builds on this past work by 1) updating the regression calculations to account for rates published over the past 20 years, 2) deriving an internally consistent database of the rate equations and parameters, and 3) formatting the database for direct use within PHREEQC  (Parkhurst and Appelo, 1999)  and TOUGHREACT  (Xu et al. , 2006 (Xu et al. , , 2011)). It is anticipated that this newly created database can serve as a benchmark enabling the direct comparison of the results of geochemical modeling calculations in dynamic and time-dependent fluid-mineral systems.
As a result, to generate a consistent database covering as extensively as possible all major rock-forming minerals, one is obliged to fit the existing rate data based on Si release to an empirical function of pH. This approach is limited, as it does not allow explicit quantification of the effects of individual aqueous species, including dissolved CO 2 or organic ligands on rates. Numerous efforts have been made in the literature to include one or more parallel reactions to describe the influence of other aqueous species such as organic ligands and/or CO 2 on mineral dissolution rates. Such additional terms were not considered in this work, as it might lead to inconsistencies among the rates of different minerals (e. g. , calculation of a specific effect on rates of an aqueous organic ligand on some minerals for which data was available but not for others for which data may be unavailable).
(  7 ) is indistinguishable from Eq. (2), and/or 2) did not provide sufficient reactive fluid chemistry data to calculate the chemical affinity term. Consequently, only rates performed at 'far-from-equilibrium conditions' (r + ) are considered in this study, which includes the vast majority of the published rates. For the purposes of geochemical modeling, for example, using the PHREEQC computer code, the chemical affinity of the dissolution reaction can be calculated using the carbfix. dat thermodynamic database  (Voigt et al. , 2018). The form of Eq. (  7 ) suggests that once it is parameterized to describe the dissolution rates for any mineral, it could also provide estimates of the precipitation rates of this mineral by taking into account the change in sign in the term for the chemical affinity in the equation.
Data selection Experimental data were regressed using Eq. (  7 ) or (8). The parameters A, E a , and n in these equations were obtained via a two-step process, and the final chemical affinity term in Eq. (  7 ) was set to be consistent with the far-from-equilibrium conditions considered in most published dissolution rate studies. To generate rate parameters, first, experimental datasets containing rates determined at far-fromequilibrium conditions were tabulated as a function of temperature and pH. All collected experimental datasets for each mineral group discussed here are listed in Table  S1  in Appendix A. These experimental data were evaluated critically to assess the quality of the starting materials, their compositions, and their experimental conditions. Only rates obtained at a steady-state, performed at far-from-equilibrium conditions, and normalized to the initial surface area measured by the BET technique were retained for regression.
For other minerals, e. g. , the feldspars or micas, three parallel rate mechanisms were considered, where the published rates were divided into three sets according to pH. For each subset, the activation energies, pre-exponential factors, and reaction orders were fit from selected dataset using Eqs. (  7 ) and (  8 ) and method of the leastsquares minimization. These parameters are listed in Table  2. In cases where insufficient experimental data are available to regress these parameters for a given mineral, regression parameters are estimated based on the results of regression fits of similarly structured and composited minerals. When such an approach is used, it is noted explicitly below when describing the regression of a given dataset. The quality of regression fits is evaluated in the present study by the differences between measured rates and those calculated from the regression equations.
, 2008; Diedrich et al. , 2014)  or pH-independent rates at neutral to basic conditions  (Palandri and Kharaka, 2004). Such interpretative differences are largely due to the limited datasets available at these conditions and inconsistencies among these data. Moreover, suggested activation energies span the range of 18. 9 up to 94. 4 kJ mol -1  (Brantley and Chen, 1995; Chen and Brantley, 1998; Palandri and Kharaka, 2004; Brantley et al. , 2008; Diedrich et al. , 2014). As a result, the various rate equations available in the literature generate rates for pH greater than pH 6 and elevated temperature that can differ by up to two orders of magnitude from one another. To address this issue, we recommend adopting the activation energies of tremolite dissolution to approximate the dissolution rates for glaucophane and hornblende to higher temperature due to their similar structures and compositions.
Experimentally measured anorthite dissolution rates are summarized in Table  4  and Fig. 3. This dataset consists of 11 studies with 149 reported rates of plagioclases having an anorthite component greater than 89 mol%. The majority of such studies were performed at ambient temperature conditions and at pH ranging up to 11. 3  (Bailey, 1974; Fleer, 1982; Holdren and Speyer, 1987; Brady and Walther, 1989; Casey et al. , 1991; Amrhein and Suarez, 1988, 1992; Oelkers and Schott, 1995; Hodson, 2003; Gudbrandsson et al. , 2014). Berg and Banwart (2000)  reported anorthite dissolution rates as a function of CO 2 concentration from 0. 01 to 0. 1 atm, and  Oelkers and Schott (1995)  reported rates at pH below 4 and temperatures ranging from 45 to 95 • C. Rates reported by  Amrhein and Suarez (1992)  and those from  Gudbrandsson et al.
These datasets are indicated in Fig. 3. To assure internal consistency among the dissolution rates of the plagioclase mineral regression, the equation in this study is based on that of  Gudbrandsson et al. (2014) , who performed a systematic analysis of the dissolution rates of plagioclases of distinct compositions. Specifically, we used the equations to describe dissolution rates as a function of pH for albite, oligoclase, andesine, labradorite, bytownite, and anorthite. Gudbrandsson et al. (2014)  reported that far-fromequilibrium plagioclase dissolution rates depend strongly on the Ca content of the mineral at acidic conditions, whereas these rates were found to be independent of this composition at basic pH. As such, the dissolution rates for the plagioclases are here assumed to be mineral composition independent at basic pH but depend increasingly on pH as the aqueous fluids become more acidic at pH < 6.
In addition, the activation energies of all of the plagioclases were taken to be identical. Although this might seem arbitrary, regression of the plagioclase rate data for the individual compositions yielded scattered activation energies as a function of composition, and the activation energy for anorthite was significantly lower than that of albite. This latter difference would lead to albite dissolution rates that are substantially faster than those of anorthite at elevated temperatures, which is inconsistent with observations of natural systems. The choice of single activation energies for all plagioclases prevents such inconsistencies in the resulting database and is within the uncertainty in published rate data. A comparison of far-from-equilibrium plagioclase dissolution rates calculated using Eq.
(  7 ) and parameters provided in Table  2  with corresponding experimentally measured values can be made with the aid of Figs. 3  and 4. At pH < 5, anorthite dissolves significantly faster than the more albitic plagioclases but dissolves at close to identical rates at basic conditions. The dissolution rate equation for albite describes the experimentally measured rate data with an average difference of 0. 63 log units; 50% of the experimental data are within 0. 54 log units of the corresponding calculated rates. The dissolution rate equation for anorthite describes the experimentally measured rate data with an average difference of 0. 61 log units; 50% of the experimental data are within 0. 41 log units of the corresponding calculated rates. The significant scatter in these experimental datasets may be due to effects of aqueous solution composition  (Burch et al.
, 1993; Knauss and Copenhaver, 1995; Oelkers et al. , 1994) , due to the different methods used to measure rates, or be related to sample preparation. In addition, the dissolution rates of the plagioclases are affected by the aqueous activity of Al and, consequently, any aqueous ligand that might complex this metal in the aqueous phase. Oelkers et al. (1994) ,  Blum and Stillings (1995) , and  Gudbrandsson et al. (2014)  noted that the experimental dataset for anorthite dissolution is strongly affected by uncertainties in the measurement of the mineral surface area, for example, in the case of the data from  Fleer (1982) , and secondary mineral precipitation, for example in the case of the data from  Amrhein and Suarez (1988, 1992)  and Hodson N/R = not reported. (caption on next page)  (2003).
K-feldspar Experimentally measured dissolution rates of K-feldspar, including those of orthoclase and microcline, are summarized in Fig. 5  and Table  5. Based on the similarity of K-feldspar and albite structures and compositions, these rates might be expected to be largely similar to one another. Nevertheless, the K-feldspar dissolution rates available in the literature are scattered in part due to the variations in composition of the feldspar samples used in the various experiments. Moreover, there are far less rate data available for K-feldspar than albite. The experimental dataset considered for the regression calculations includes rates obtained at ambient pCO 2 and in the absence of organic ligands. These data span in temperature from 13 to 150 • C  (Siegel and Pfannkuch, 1984; Bevan and Savage, 1989; Gautier et al.
, 1994; Schweda, 1989; Swoboda-Colberg and Drever, 1993; Stillings and Brantley, 1995; Stillings et al. , 1996; van Hees et al. , 2002). Additional rates have been reported in the presence of 1 atm pCO 2  (Busenberg and Clemency, 1976)  and in the presence of aqueous organic ligands  (Tan, 1980; Manley and Evans, 1986; Welch and Ullman, 1993, 1996; Blake and Walter, 1999; Ullman and Welch, 2002). Experimentally measured rates in this dataset at 25 • C range from as high as 10 -10. 1 at pH 2. 0 to as low as 10 -13. 3 mol m -2 s -1 at pH 5. 6. Rates obtained in the presence of 1 atm pCO 2 and/ with added aqueous organic ligands are up to 1. 5 log units faster than those measured at ambient pCO 2 and in the absence of organic ligands  (Busenberg and Clemency, 1976; Tan, 1980; Bevan and Savage, 1989).
Moreover, experimental data from Swoboda-Colberg and  Drever (1993)  obtained at field and laboratory conditions and normalized to the geometric surface area, and data from  Gautier et al. (1994)  obtained at various close-to-equilibrium chemical affinities were also not considered for the regression. Experimental datasets that are not included for the kinetic fit are marked in circles in Fig. 5  and indicated in Table  S1  in Appendix A. The K-feldspar experimental datasets spanning the range of temperature from 25 to 95 • C and pH from 2 to 12 were fit to Eq. (  7 ). A comparison of experimentally measured rates with those generated using the regression equation and the parameters in Table 2 can be made with the aid of Fig. 5. The activation energies retrieved in this study, 51. 7, 38. 0, and 62.
2 kJ mol -1 , at acidic, neutral, and basic conditions, respectively, are similar to those previously reported by  Helgeson et al. (1984) ,  Bevan and Savage (1989) ,  Schweda (1990) ,  Sverdrup (1990) , and  Blum and Stillings (1995). The activation energies of  Palandri and Kharaka (2004)  at basic conditions are somewhat higher as they considered the rates of  Gautier et al. (1994)  in their regression. As seen in Fig. 5 , there is significant scatter among the datasets leading to differences between calculated and measured rates in some cases. The dissolution rate equation for K-feldspar describes the experimentally measured rate data with an average difference of 0. 53 log units; 7% of the experimental data are more than one log unit different from the corresponding calculated rate. Many of these larger differences may be due to the composition of the studied feldspar, which ranged from Or 66 to Or 94.
Basaltic glass Measured basaltic glass dissolution rates have been collected from 9 different studies containing 146 distinct data points. Aside from field data in  Gordon and Brady (2002) , all rates were measured in the   and Eugster, 1987; Daux et al. , 1997; Oelkers and Gislason, 2001; Wolff-Boenisch et al. , 2004; Stockmann et al. , 2012) , although  Guy (1989)  and  Gislason and Oelkers (2003)  reported rates at temperatures ranging up to 200 • C. Oelkers and Gislason (2001)  and  Stockmann et al. (2012)  measured basaltic glass dissolution rates in the presence of organic acids and bacteria, and  Crovisier et al. (1987)  reported on basaltic glass dissolution in seawater. As the presence of seawater significantly accelerates the dissolution of basaltic glass (marked in circle on Fig.
Rhyolitic glass Fewer studies have focused on the dissolution rates of rhyolitic glass. The rhyolite glass dissolution dataset collected in this study comprises 93 data points. Wolff-Boenisch et al. (2004)  compared dissolution rates of natural glasses as a function of their silica content from 25 to 74 • C, at pH ~ 4 and 10. 6. Karkhanis et al. (1980) ,  White (1983), and Declercq et al. (2013)  reported rhyolite glass dissolution rates over the temperature range of 40 to 200 • C as a function of pH and in the presence of organic ligands. Measured rhyolitic glass dissolution rates are one to four orders of magnitude slower than those of basaltic glass at corresponding temperature and pH conditions and range from 10 to 10. 4 to 10 -11. 6 mol m -2 s -1 at neutral pH and ambient temperature conditions.
Micas Mica dissolution rates have been previously summarized and reviewed by  Ross (1967)  and  Nagy (1995). The experimental datasets of biotite and muscovite dissolution considered in the present study are summarized in Table  6  and Fig. 7. Constant temperature far-fromequilibrium mica dissolution rates tend to minimize as a function of pH at near to neutral conditions. The published dissolution rates of biotite appear to vary more strongly with pH than those of muscovite. Although the dissolution rates of both muscovite and biotite are comparable at 25 • C, muscovite dissolution rates appear to increase faster with increasing temperature. No significant effect has been observed of the magnesium to iron ratio of the biotite on its dissolution rate to date. Magnesium content is expressed as Mg number (Mg# = Mg/(Mg + Fe)) in Fig.