dataesr/scientific-paragraphs-categorization huggingface dataset:
Putting these together, the semiparametric panel data model with random coefficients is given by log CO 2 P op it = q R j=1 β R j X R it,j + u R i,j Z R it,j + f log GDP P op it + ε it where f log GDP P op it = β l log GDP P op it + q G l k=1 u G lk z lk log GDP P op it (3) and Z R = blockdiag X R i (1≤i≤N ) for i = 1,. , N (81), t = 1,. , T (25). X R it,1 is the intercept and X R it,j , 2 ≤ j ≤ q R are the other covariates (in log, percent, dummies or time trend). X R is an (N T × q R ) matrix of covari- ates, Z R is an (N T × N q R ) block-diagonal matrix of the X R i submatrices. In the statistics literature, X and Z are called the fixed and random effects design matrices associated with β and u. The latter are called the fixed effects and random effects vectors. This terminology is different from what the panel data literature dubs as "fixed" and "random" effects.
The random intercept is defined by the sum (β R 1 + u R i,1 ), the random slope for variable X i,2 is the sum (β R 2 + u R i,2 ), etc. The disturbances ε it have a normal distribution ε it ∼ N (0, σ 2 ε ). Following  Wand and Ormerod (2008)  and  Lee and Wand (2016a) , we use transformed cubic O'Sullivan splines. 10 Penalized splines can be viewed as random effects as one penalizes the spline basis function coefficients by treating them as a random sample from a multivariate normal distribution to avoid overfitting of the data (see  Lee (2016) ). 11 This semiparametric panel data model with random coefficients is a complex specification and the methods for estimating such a specification are few. In the panel data literature, random coefficient models (RCM) and linear mixed-effects models frequently use either maximum likelihood estimators involving repeated applications of the penalized least squares method (see  Bates et al.
(2015) ) or Bayesian Gibbs sampling (see  Hsiao and Pesaran (2008) ,  Bresson and Hsiao (2011)  and  Hsiao (2015)  among others). Adding semiparametric elements reinforces the complexity. The varying coefficient model considered here is therefore part of the already long tradition of research on semiparametric estimation of partially linear varying coefficient panel data models which allow flexibility to characterize trending phenomenon in nonlinear panel data analysis. Some use semi-parametric profile likelihood methods  (Chen et al. (2012) ,  Li et al. (2017) ), kernel or averaged local linear estimation (Li 10 O'Sullivan penalized splines are similar to P-splines, but have the advantage of being a direct generalization of smoothing splines. 11 That is, u G 11 ,. , u G Lq G L | σ 2 u1 ,.
The mean field variational Bayes approximation In this section, we present the panel data semiparametric model with random coefficients and we outline the mean field variational Bayesian approach (hereafter MFVB) used to estimate this model. We are influenced by the work of  Zhao et al. (2006) ,  Lee (2016)  and  Lee and Wand (2016a)  and we follow their notation. The linear-mixed Gaussian specification is given by y ∼ N X R β R + Z R u R + f X G , σ 2 ε I N T ( 4 ) where y is a (N T × 1) vector (y 11 ,. , y 1T , y 21 ,. , y 2T ,. , y N 1 ,. , y N T ) for i = 1,. , N countries and t = 1,. , T time periods. I N T is an (N T ×N T ) identity matrix. X R is an (N T ×q R ) matrix of covariates, Z R is an (N T × N q R ) block-diagonal matrix of the X R i submatrices. The dimensions of the vectors β R and u R are respectively (q R × 1) and (N q R × 1).
In what follows, we give a quick overview of the MFVB method and its application to the semiparametric panel data model with random coefficients. Consider a generic Bayesian model with observed vector y and parameter vector θ that is continuous over the parameter space Θ. The Bayes theorem allows one to define the posterior distribution as: p (θ | y) = p (θ, y) p (y) = p (y | θ) p (θ) p (y) with p (y) = Θ p (θ, y) dθ (9) Let q be an arbitrary density function over Θ. Then, the logarithm of the marginal likelihood satisfies (see  Bishop (2006) ,  Ormerod and Wand (2010) ): log p (y) = log p (y) Θ q (θ) dθ = Θ q (θ) log p (y) dθ = Θ q (θ) log p (θ, y) /q (θ) p (θ | y) /q (θ) dθ = Θ q (θ) log p (θ, y) q (θ) dθ + Θ q (θ) log q (θ) p (θ | y) dθ = log p (y, q) + KL(q, p) (10) where KL(q, p) is the Kullback-Leibler divergence between q (θ) and p (θ | y).
Hence, OLS estimation with inference based on clustering at the individual level can not serve as a benchmark for the semiparametric estimation of partially linear varying coefficient panel data models. These results also confirm the recent research of D'Adamo (  2018 ) which shows that inference based on the usual cluster-robust standard errors by  White (1984)  is invalid in general when the number of controls is a non-vanishing fraction of the sample size or for semiparametric partially linear model with fixed coefficients. So far, we have discussed average marginal effects. One of the advantages of the random coefficients specification is that it is possible to discriminate between these marginal effects for different countries (β R j + u R i,j ) for i = 1,. , N (81). If the average intercept estimate is β R 1 = -6.
Please insert Table 2 here Our results show that economic growth tends to increase greenhouse gas emissions. Thus the relationship between CO 2 emissions per capita and GDP per capita may vary across different stages of development. On average, the "CO 2 emissions -GDP elasticity" is around 0. 85 at the 5% level. This estimated value is close to those found in the literature using parametric panel data specifications with or without cointegrating relations. Moreover, we find a nonlinear relation and no evidence in support of an inverted-U curve between log CO 2 per capita and log-GDP per capita. Thus, the Kuznets hypothesis is not confirmed. We find no turning points with this semiparametric EKC. When Norway and Singapore are in the sample of countries considered, its shape is associated with what  Dasgupta et al.
UHPLC-HRMS Analysis We qualitatively assessed the metabolic composition of both leachates using High-resolution Mass Spectrometry (MS) and molecular networking. Briefly, chromatographic separation was performed on a Waters Acquity UPLC system interfaced to a Q-Exactive Focus mass spectrometer (Thermo Fisher Scientific, Bremen, Germany), using a heated electrospray ionization (HESI-II) source. Thermo Scientific Xcalibur 3. 1 software was used for instrument control. The LC conditions were as follows: column, Waters BEH C18 50 × 2. 1 mm, 1. 7 µm; mobile phase, (A) water with 0. 1% formic acid; (B) acetonitrile with 0. 1% formic acid; flow rate, 600 µL. min - 1  ; injection volume, 1 µL; gradient, linear gradient of 2-100% B over 6 min and isocratic at 100% B for 0. 6 min. An Acquity IClass UPLC photodiode array detector was used to acquire PDA spectra, which were collected from 210 to 450 nm.
LC-MS Data Processing ThermoRAW MS data were converted to the open MS format (. mzXML) using the MSConvert software, part of the ProteoWizard package  (Chambers et al. , 2012). The converted files were then treated using the MzMine 2. 36 software suite. The parameters were adjusted as follow: the centroid mass detector was used for mass detection with a noise level set to 1. 0E6 for MS level set to 1, and to 0 for MS level set to 2, respectively. The ADAP chromatogram builder was used and set to a minimum group size of scans of 5, minimum group intensity threshold of 1. 0E5, minimum highest intensity of 1. 0E5 and m/z tolerance of 8. 0 ppm. For chromatogram deconvolution, the algorithm used was the wavelets (ADAP). The intensity window S/N was used as S/N estimator with a signal to noise ratio set at 25, a minimum feature height at 10,000, a coefficient area threshold at 100, a peak duration ranges from 0.
02 to 0. 9 min and the RT wavelet range from 0. 02 to 0. 05 min. Isotopes were detected using the isotopes peaks grouper with a m/z tolerance of 5. 0 ppm, a RT tolerance of 0. 02 min (absolute), the maximum charge set at 2 and the representative isotope used was the most intense. An adduct (Na + , K + , NH 4 + , ACN + , CH 3 OH + , Isopropanol + ) search was performed with the RT tolerance set at 0. 1 min and the maximum relative peak height at 500%. A complex search was also performed using [M + H] + for ESI positive mode, with the RT tolerance set at 0. 1 min and the maximum relative peak height at 500%. A custom database search was finally performed using the Dictionary of Natural Products 2018 (v. 26. 2) database 1 , restricted to moss metabolites. Peak alignment was performed using the join aligner method (m/z tolerance at 8 ppm), absolute RT tolerance 0.
LC-MS Data Analysis: Molecular Networks Generation In order to further identify compounds in MS dataset, we used the molecular networking (MN) approach that group metabolites by structural similarity  (Wang et al. , 2016). The MN approach is based on the organization and visualization of tandem MS data through a spectral similarity map, revealing the presence of similar MS fragmentations patterns. As structurally related compounds tend to share similar fragmentation spectra, nodes gathered together create clusters of structural analogs. The generated molecular networks were annotated using experimental spectral libraries (GNPS libraries 2  ) and an in silico fragmented database of natural products using a previously detailed dereplication strategy  (Allard et al. , 2016). In the network created, edges were filtered to have a cosine score above 0.
65 and more than 6 matched peaks. Further edges between two nodes were kept in the network if and only if each of the nodes appeared in each other's respective top 10 most similar nodes. The spectra in the network were then searched against GNPS' spectral libraries. The library spectra were filtered in the same manner as the input data. All matches kept between network spectra and library spectra were required to have a score above 0. 7 and at least 6 matched peaks. The output was visualized using Cytoscape 3. 6 software 3. The nodes of the generated networks were colored according to the number of MS/MS triggered in a given sample, thus offering a semi-quantitative information. The size of the nodes was mapped according to the total sum of precursor ions intensities. Molecular networks are available on the GNPS servers at the following addresses: https://gnps.
Cells were identified and enumerated across 50% of the total chamber area using transects. The abundance of each species was converted into biovolume (µm 3 ), based on geometrical shapes and dimensions measured under the microscope (length or diameter; width, and height) and then into biomass using conversion factors as given in  Gilbert et al. (1998). The biomass data were expressed as micrograms of C per gram of Sphagnum dry mass (µgC g -1 DM).
Food Web Structure Analyses Networks were then produced using the igraph R package  (Csárdi and Nepusz, 2006). We extracted the core properties of the webs to evaluate whether substructures responded to leachate addition according to  Ma et al. (2019). Each network was then analyzed in terms of connectivity (C), density of links within the network (edge D), core size and density of links within the core (Phir). Connectivity was calculated as the number of links divided by the square of the number of species in the network. Density of links within the network was calculated as the number of effective links divided by the number of total possible links. Core size and density of links within the core were calculated as in  Ma et al. (2019). Indices of network beta diversity were also calculated between networks  (Poisot et al.
AUTHOR CONTRIBUTIONS VJ, BR, and SH designed and implemented the microcosm study with the help of CS. SH, BR, and VJ sampled the Sphagnum cores in the field and collected the leachates. SH and VJ collected the samples at the end of the experiment with the assistance of BR. SH performed MicroResp, characterized general characteristics of leachates and quantified enzyme activities with the assistance of VJ, BR, LB, and AB. VJ analyzed the microbial samples under the microscope. P-MA, SZ, and TS performed LC-MS analyses, peak data processing and molecular networks on Sphagnum leachates with assistance of GC and J-LW. SH and VJ performed statistical analyses and interpreted the data with assistance of BR, P-MA, LB, and AB. SH and VJ wrote the manuscript to which all authors contributed with discussions and text.
ACKNOWLEDGMENTS We are indebted to  Länsstyrelsen i Jönköpings  län and the staff of the  Store Mosse National Park , particularly  Arne Andersson ,  Dan Käll ,  Carina Härlin , and  Johan Rova  for granting site access (permission  521-895-2011 ) and make use of the infrastructure of the park. We are thankful to the three reviewers for their valuable comments on our manuscript.
FUNDING BR was supported by a  NWO-VENI  grant awarded by The  Netherlands Organization for Scientific Research  ( NWO-ALW: 863. 10. 014 ), by the  British Ecological Society  (small grant  SR17/1427 ), and by the  Dutch Foundation  for the Conservation of Irish bogs. This research has been funded by the  Swiss National Science Foundation  through the project  SPHAGNOL  (Grant No. 315260 149807  to LB) and supported by  MIXOPEAT  project (Grant No. ANR-17-CE01-0007  to VJ) from the  French National Research Agency.
Sampling and Data Collection A list of 803 farrow-to-finish indoor farms present in southwest France (Nouvelle-Aquitaine and Occitanie regions) was obtained from the national database BDPORC. Two hundred and seventyone premises declared over 40 sows; of these, 269 had either a telephone number or an email address or both. Five farms were selected randomly for the pilot study and then discarded. Of the 155 farms that could eventually be contacted within 4 phone call attempts and that complied with the inclusion criteria above, 84 farmers agreed to participate, resulting in a response rate of 54. 2%. The response rates in the two regions were not significantly different (chi-2, P-value = 0. 21). The final sample was reduced to 70 farms due to (i) missing information (number of piglets per litter, 1 farm), (ii) missing name of the used medicine (making it impossible to find the SPC, 9 farms), and (iii) inconsistent reported values for antimicrobial dose, e.
g. , more than five times the SPC value (5 farms). One farm had two of the listed inconsistencies. Data from the calendar year 2014 were collected in 2015 using a questionnaire administered in a face-to-face interview. The questions were mainly closed and were organized into 8 sections, of which three are of interest here: (i) general information on the farm, including farm size and farm management during the post-weaning stage; (ii) economic and technical results, including mean piglet weight at weaning and at the end of the weaning stage; and (iii) farm health monitoring information (number of visits per year by a veterinarian or a technician) and antimicrobial treatments administered. The details of antimicrobial treatments administered during 2014 were collected: name of the drug and percentage of active substance, route of administration, number of packages or items used, size/volume of the package or item, dose used, administration frequency, number of days the product was administered, age of the animals at the beginning of the treatment, number of animals targeted by the treatment, and type of usage.
The type of usage could be either prophylactic (applied to healthy animals for the prevention of particular diseases), metaphylactic (administration of antimicrobials to animals experiencing any level of bacterial disease before overt disease occurred, with the time of intervention depending on the detection of disease outbreaks in a few animals in the group) or therapeutic (treatment only of animals showing symptoms of a disease). The dataset was therefore based on the active substances used in each treatment, on each farm and could include several active substances for a given farm.
7 (95% CI: 1. 5-2. 0), a maximum of 5 and a median of 2 antimicrobial families per farmer. Details on the active substances that were used are given in Table  2. When looking at active substances that were used without distinction between usage type, 16 (11%) doses were over 150% of the SPC value. Regarding real treatment length, 49 records (33. 8%) were more than 50% lower than the SPC values and 34 records (23. 4%) were more than 150% higher. Sixty-four records corresponded to prophylactic usage, 34 to metaphylactic usage and 47 to therapeutic usage. For prophylactic usage, 13 records (20. 3%) were higher than 150% of the SPC dose. Differences in the treatment length appeared more extreme; it was higher The number of occurrences of each active substance in the dataset is indicated along with the number and proportion of farms that reported using it in 2014.
DISCUSSION For indicator calculations when using SPC intervals, the highest dose and shortest treatment length were selected  to maintain consistency with therapeutic recommendations but implies that present results include less compliance with SPC recommendations by farmers based upon veterinary prescription compared to field practices. Indeed, this choice influences the differences between UDD and ADD significantly and thus differences may not be regarded as a sign of "UDD was not correct. " The objective of the study was to analyze how data selection may influence indicator values and not to analyse compliance with SPC recommendations. The size of the study sample (70 farms, 145 records) was limited by the exclusion criteria. However, the sample included farms that showed heterogeneity in size, dose, length of antimicrobial treatment applied and weight of animals.
Moreover, all countries do not use the same SPC values, these values can be very different, and may question the definition of good therapeutic practices  (24). Thus, it could be recommended that real dose, treatment length and real weight be used whenever possible to analyse antimicrobial consumption. Using real values would allow a better description of actual exposure to antimicrobials, although today one would prefer using standard references when aiming at comparisons. Data from 2014 were collected in southwest France in 2015. Prophylactic usage of antimicrobials, which are still used at high levels in many countries to sustain animal health and welfare was recently banned in feed for farm animals in the EU  (25) , and these were the treatments for which most discrepancies between ADD and UDD were found in our study.
Kinetic Study In order to determine the time required for reaching the adsorption equilibrium, phenol and PHBA solutions were put in contact with the activated carbon S23 for different times, at a constant temperature of 25 C°. For that purpose, a number of samples were prepared by mixing 0. 5 g of AC with 2. 35 g l -1 of a solution of phenol and others of PHBA (3. 38 g l -1 ), shaken, filtered and then analysed by an HPLC apparatus. The amount of phenol adsorbed on the activated carbon samples at different contact times was calculated by the difference between the initial and the final phenol concentrations. The kinetic data obtained from batch studies have been analyzed by using pseudo-first-order and pseudo-second-order models. The first order equation of Lagergren is generally expressed as follows  (Srihari and Das, 2008) : (1) Where q e and q are the amounts of pollutant adsorbed (mg g -1 ) at equilibrium and at time t (min), respectively, and K 1 is the rate constant of pseudo-first-order sorption (min -1 ).
Modeling of the Adsorption Isotherms Many models have been used to describe the equilibrium relationship between solute and activated carbon in both single and multi-component systems. The mono-component isotherms were evaluated according to the two parameter models (Langmuir, Freundlich and Jovanovic). The adsorption models constants, the values of which express the surface properties and affinity of the adsorbent, can be used to compare the adsorptive capacity of adsorbents for different compounds. Langmuir equation is the most isotherm equation used for modelling of the monolayer adsorption data  (Altaher et al. , 2015) , is given by Eq. (  5 ). ( ) 5 Where q e is the amount of adsorbate taken up on adsorbent at equilibrium (mg g -1 ), C e the equilibrium concentration of the adsorbate in aqueous solution (mg l -1 ), q max and K L are the Langmuir constants, representing the maximum adsorption capacity for the solid phase loading and the energy constant related to the heat of adsorption, respectively.
SAGE III-ISS aerosol extinction profiles The Stratospheric Aerosol and Gas Experiment (SAGE III-ISS) was launched in February 2017 and mounted aboard the International Space Station. Data from the instrument are available from June 2017 onwards  (SAGE III, 2019). SAGE III uses solar and lunar occultation and limb scatter to infer profiles of trace gases like ozone and aerosol extinction coefficient at nine wavelengths between 384 and 1544 nm. Due to orbital considerations, SAGE III acquires 30 sets of profiles per day in two latitude bands which span from roughly 60 • N to 60 • S over the course of a month, with the best spatial coverage at the mid-latitudes (30-60 • ). The vertical extent of the aerosol extinction coefficient profiles is from roughly 40 km down to encountering either the solid Earth or exceeding the dynamical range of the detector, usually in association with an optically opaque water cloud.
These profiles have a vertical resolution of ∼ 1 km and are reported every 0. 5 km from 0. 5 to 40 km. The horizontal resolution is approximately 200 km along the line of sight between the instrument and the Sun and stretched along the direction of motion of the platform (ISS) by an additional 200 km. The meteorological data including the height of the tropopause are derived from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2). Herein, we make use of the cloud-unfiltered version 5. 1 solar occultation aerosol extinction coefficient profiles at three wavelengths  (384, 521, 676 nm). 1a  from 1 to 5 September. Tropopause heights given by the SAGE III data set are indicated (dotted green line: ±2. 7 km; dotted red line: ±2. 3 km). The number of profiles averaged is indicated.
, 2011; De Wachter et al. , 2012). Validation against MOZAIC airborne in situ data have demonstrated that SOFRID-CO data are able to capture the seasonal variability of CO at mid-latitudes (Frankfurt) as well as at tropical latitudes (Windhoek) in the lower (and upper) troposphere with correlation coefficients of 0. 85 (and 0. 70, respectively)  (De Wachter et al. , 2012). Barret et al. (2016)  have also shown that SOFRID-CO data were able to document the accumulation of CO in the UTLS AMA.
UVSPEC radiative forcing estimations The shortwave surface and TOA direct radiative forcing for this event is estimated using the UVSPEC radiative transfer model and the LibRadtran package  (Mayer and Kylling, 2005) , available at the following website: http://www. libradtran. org/doku. php, last access: 11 November 2019. The radiative transfer equation is solved with the SDISORT method, the pseudo-spherical approximation of the discrete ordinate method (DISORT)  (Dahlback and Stamnes, 1991). Surface and TOA direct and diffuse shortwave spectra are computed in the range 300 to 3000 nm (0. 1 nm spectral resolution). We use the input solar flux spectra of  Kurucz (2005). The atmospheric state in terms of the vertical profiles of temperature, pressure, humidity, and gas concentration is set as for the AFGL (Air Force Geophysics Laboratory) climatological standard tropical atmosphere  (Anderson et al.