dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2010; Graham and McDonnell, 2010; Gabrielli et al. , 2012). To improve We modelled the estimation uncertainty of the bedrock topography (Eq. 1) by using input data from the GRANADA database and geographical information from local study area, Øvre Eiker area, Norway (Fig. 2 ). The purpose was to minimize the uncertainty (u) by using digital Quaternary maps (DQM), digital elevation models (DEM) and point observations of the sediment thickness D(u i ) recorded in the GRANADA database (NGU, 2017a). The methods we applied relied on multi-Gaussian statistics which is a simplification of the reality. We also assumed that the information in the DQM could be simplified to a spatial function R(u), where R(u) = 1 in areas where the bedrock was exposed (D 0 m), and R(u) = 0 in areas covered by deposits (D > 0 m).
Clustering and bias of empirical data Like most geo-and environmental databases, the GRANADA database contains clustered observations. This is due to the fact that spatial data is usually sampled for a specific purpose, which implies a large number of observations in a few limited areas while most of the sampling domain has less frequent observations. In this case, boreholes and sedimentary wells were drilled in populated locations, which means that there is a high frequency of recordings in urban areas and a limited number in rural and remote areas. Corrections of clustering effects are therefore necessary to perform before statistical inference are undertaken. In this project declustering was done by the moving grid method  (Deutsch and Journel, 1998). Declustering of sediment thickness D(u), shows that recordings of small D(u) are more clustered than observations of large D(u).
Minor sediment thickness should therefore receive less weights than large sediment thicknesses, and vice versa. In addition to clustering, the sediment thickness, D, recorded in the GRANADA database is most likely biased. The purpose of the GRANADA database was to create useful data to improve management of groundwater resources. A central part of the database was registration and localization of boreholes and wells and not recording of D. As boreholes and sedimentary wells are usually drilled for economic reasons, locations with large sediment thickness are usually avoided due to the casing costs. Thus, boreholes may be abandoned for economic reasons if the sedimentary depth is too big. The opposite is true for sedimentary wells. They are usually drilled for domestic water supply, which imply a desire for high water extraction capacity.
The water flux into the well is governed by the hydraulic conductivity and the thickness of the water conductive layer. Preferred locations for such wells are therefore areas with large sediment thicknesses. Thus, there are different preferences with respect to D for boreholes and sedimentary wells. The recordings of D are therefore prone to bias, with an over-representation of small D for boreholes and vice versa for sedimentary wells. These preferences have also impact of the location of boreholes and sedimentary wells with respect to horizontal distance to the nearest bedrock outcrop L, and the preferred Quaternary deposit. These effects are illustrated in the dataset from Øvre Eiker (Fig. 2 ). Only one of the boreholes were drilled at a location where L > 1 km, and 11 of the 25 boreholes had L < 100 m (Fig.
Because all spatial mapping includes uncertainties with respect to exact location, visual inspection of exposed bedrock can be used for quality control. This problem is illustrated in Tab. 6, where one of the boreholes (id. 58865) had inconsistent information. According to the GRANADA database, the bedrock surface was 7 m below the ground, but according to the digital Quaternary map, the borehole was located on exposed bedrock. This observation indicates the uncertainty of the spatial information. Depending on the purpose of the work, which determines specifications of accuracy and precision, such kind of spatial uncertainties should be kept in mind.
Statistical simplifications Modelling of the bedrock topography was first done by conditioning on normal score transformed data, and then secondly by using the lognormal transform. The normal score transform ranks the observations from minimum to maximum and finds the corresponding entries in a standard normal pdf  (Deutsch and Journel, 1998). If the empirical dataset does not cover extreme. 21). This drawback explains why the lognormal modelling results had low precision. In this case study, the modelling results based on the normal score transform did not differ very much from the lognormal transformed results. One reason is that even though the univariate pdf by definition is perfect Gaussian, the same is not automatically true for the bivariate pdf. This problem is visualized in Fig. 14 , where the bivariate normal score transforms of D and L were compared to a synthetic bivariate Gaussian variable with the covariance structure given in Tab.
By this paper we wanted to draw attention towards modelling of sediment thickness and the associated bedrock topography (Eq. 1). We explored the opportunities of combining information from the national well database (GRANADA); digital geological maps; and digital elevation models to reduce the estimation uncertainty. Multi-Gaussian methods were custom made to handle the complex dataset for this study. This included declustering of the global data; transformation of empirical data; and calculation of variograms and cross-variograms. In this case, the small-scale variance (the nugget) was large compared to the total variance (the sill), which explains why the mean absolute error and the estimation variance were large. The global semivariogram for the sediment thickness had a correlation length of about 2 km, and the cross-semivariogram between the sediment thickness and the horizontal distance to the bedrock outcrop revealed a correlation length of more than 5.
5 km (Tab. 1). These correlation lengths indicate that the estimation of the bedrock topography or the sediment thickness can capitalize on observations in the GRANADA database if the distances to the nearest GRANADA recording is less than 2 to 5. 5 km depending on the estimation procedure. The Poisson's equation was used to estimate the local trend in the sediment thickness. This was done by inverse modelling of a constant parameter in the Poisson's equation. By this approach we fitted a parabolic function to the bedrock topography, which minimized the differences between the point observations and the numerical solutions of the Poisson's equation. We solved the Poisson's equation by using the Dirichlet boundary conditions. In that case, we obtained robust results if we calculated sediment thickness instead of the bedrock topography directly.
The mean absolute error, accuracy and precision of the estimation methods depend first and foremost on the quality of the input data. Author contributions. The main part of the work was done by NOK with significant contributions from ÉL. A C : Accuracy, Eq. 24. P C : Precision, Eq. 25. 41 Ocean and coastal sediments, coherent, often great thickness. 43 Ocean, sea and beach sediments, patchy or thin cover over bedrock. 50 Fluvial (River and stream sediments). 70 Weathered deposits, not divided by thickness. 130 Exposed bedrock. Table  5. Jackknife cross-validation of sedimentary wells (the id. is the well number identification in the GRANADA database (NGU, 2017a). The measured length of well D well , was compared to estimated sediment depth D obs. In general, the real sediment depth is equal to or larger than the length of the sedimentary well: D ≥ D well , but for this cross-validation we let D D well because all wells were located in Fluvial sediments and they were drilled for water supply.
indicate a physical relation: If L is large, there is no small D, and vice versa. This explains the large uncertainties in modelling results if the D/L relation is included in the estimation procedures. These uncertainties were confirmed by the jackknife cross-validation results. It should also be noted that the D/L relation in the global GRANADA database had a similar high variability (Fig. 12). The percentile values for the global declustered D/L relation had median value of D/L = 4. 7 [1. 2, 18. 0] m/km with 0. 25 and 0. 75 percentile values in parenthesis (Fig. 13). In comparison, data from the study area Øvre Eiker had median D/L of 30 m/km, which corresponds to the 0. 8 percentile value of the global dataset. This indicate that the local D/L estimates should be utilized instead of the global percentiles.
Histogram of D/L from the global GRANADA dataset shows that for 50% of the boreholes with horizontal distance to bedrock outcrop L ≤ 1 km had sediment thickness D ≤ 4. 7 m (Fig. 13). Non-declustered median value of the same data material gave D ≤ 6. 7 m, which shows that there is a clustering of minor D/L values in the GRANADA database. Declustered data shows that 25% of the wells with L ≤ 1 km had sediment thickness D ≥ 18. 0 m and 10% had D ≥ 60. 8 m. Corresponding https://doi. org/10. 5194/esurf-2019-57 Preprint. Discussion started: 24 October 2019 c Author(s) 2019. CC BY 4. 0 License. values for raw data (not declustered) was 20. 2 m and 54. 7 m. For Øvre Eiker, the median value was D/L = 30. 7 m/km. This.
Cross-validation by leaving one borehole out. The results show estimated sediment depths D obs , for percentile ξ = 0. 5. The boreholes were recorded before 2010, and the identification number (id. ) corresponds to the id. in the GRANADA database (NGU, 2017a). D obs and L obs are the measured sediment depths, and the horizontal distances to nearest bedrock outcrop. The results are given for: ordinary kriging (DOK ), cokriging (DCK ), bedrock kriging (DBK ), Poisson modelling of the sediment depths (DP ), and by ordinary kriging of the residuals between observations and the Poisson modelling sediment depths (DP K. The superscripts indicate that the modelling was based on observations that was either normal score transformed ( * ) or lognormal transformed ( ). Identification of the deposits was taken from the digital Quaternary map (DQM, c.
N , where N = 19682 for the global dataset. GRANADA boreholes located within the Øvre Eiker study area https://doi. org/10. 5194/esurf-2019-57 Preprint. Discussion started: 24 October 2019 c Author(s) 2019. CC BY 4. 0 License. recorded prior to 2010 were used for a primary cross-validation by leaving one borehole out. All of these wells -marked with.
Sows often receive the same feed during gestation even though their nutrient requirements vary during gestation and among sows. The objective of this study was to report the variability in nutrient requirement among sows and during gestation, in order to develop a precision feeding approach. A dataset of 2511 gestations reporting sow characteristics at insemination and their farrowing performance was used as an input for a Python model, adapted from InraPorc®, predicting nutrient requirement during gestation. Total metabolizable energy (ME) requirement increased with increasing litter size, gestation weeks, and parity (30. 6, 33. 6 and 35. 5 MJ/d for parity 1, 2 and 3 and beyond, respectively, P < 0. 01). Standardized ileal digestible lysine (SID Lys) requirement per kg of diet increased from week 1 to 6 of gestation, remained stable from week 7 to 10, and increased again from week 11 until the end of gestation (P < 0.
General Approach The originality of the approach developed (Fig. 1 ) is the combination of current knowledge about the nutrient use of sows with the flow of data recorded on-farm, to provide a dynamic determination of optimal nutrient supplies for each sow. These data include (i) insemination events (date of insemination, parity, body weight (BW) and back fat thickness (BT) of sows), (ii) events that occur during gestation (measurements of the physical activity of sows, BW and BT), and (iii) farrowing events (date of farrowing, litter size and piglet birth weight). In practice, the farmer or sensors can record these types of data, which may provide a more accurate and dynamic prediction of nutrient requirement. A mechanistic module based mainly on the InraPorc® model  (Dourmad et al. , 2008)  with some improvements, was used on a daily basis to calculate nutrient requirement.
Database used as an input to the model for the calculation of individual sow BW after farrowing = 275 x (1-exp((-3. 824/1000) x (Age at Farrowing) 0. 9801 )) The objective of BT after farrowing was set at 18 mm for all sows in accordance with the practices of the farm from which the data were collected. Average (± SD) LS at farrowing was 14. 1 (± 3. 3) with an average BW of 1. 48 kg per piglet (± 0. 24), and a total litter weight of 20. 5 (± 4. 4) kg. The average BW of the sows at insemination increased from 163 to 251 kg between the first and eighth gestation, while BT at insemination tended to be higher for first and second parity sows and then remained relatively constant (Table  4 ).
Simulations The simulation model was written in Python 3 (Python Software Foundation, Beaverton, Oregon). The Python model was composed of three classes (feed, environment, sow), and one gestation function. The gestation function calculated the growth of the different body compartments, and the nutrient requirements (Fig. 2 ) for each day and each sow. with the fixed effects of LS, parity, week and their interaction, and the random sow effect. With the R version 3. 4. 2, the LME function, from the NLME package  (Pinheiro et al. , 2018) , was used to fit the linear mixed-effect models  (Laird and Ware, 1982). The correlations over weeks and for each sow were calculated using the temporal corAR1 function, which represents an autocorrelation structure of order 1  (Pinheiro and Bates, 2000). The results are presented in Tables  5  and 6  as means and standard errors for each parity and LS groups, including the P-values to indicate if these two factors and their interactions were significant (P < 0.
General Structure of the Model The modeling approach is based on a combination of current knowledge of nutrient use of gestating sows with the flow of data produced on-farm, in a similar way to the work performed by  Gauthier et al. (2019)  for lactating sows. The approach considers individual variability in nutrient requirement according to gestation stage, sow characteristics at mating (age, parity, body condition) and reproductive performance (number and weight of piglet at farrowing). This approach makes it possible to calculate farm specific nutrient recommendations based on their own flow of data. In the present study, measured individual data were used to obtain LS and piglets birth BW. However, when applying the model in real-time, LS and piglet birth BW will not be available since techniques such as the ultrasound counting of foetuses are not applicable in practice.
All these adjustments will require the farms to be equipped with devices and sensors (weighing scales, cameras, accelerometers, hydro-thermometers, etc. ) that continuously record this information to feed real-time databases. It will also require building, based on the present model, a full decision support system that may be embedded in automated feeding equipment. As indicated by  Gauthier et al. (2019)  the approach developed in the present study is a contribution to the development of a new type of models that would be "data ready" and "precision-feeding ready", and able to process both historical farm data (e. g. for ex post assessment of nutrient requirements) and real-time data (e. g. to control precision feeding). Comment citer ce document : Gaillard, C. , Gauthier, R. , Cloutier, L.
nutrient requirement A dataset of 2511 gestations from crossbred Landrace x Large White sows, obtained on an experimental farm from 2009 and 2013 was used. It contained the characteristics of sows as regard to their insemination and farrowing performance used as inputs to the model for predicting the individual variability and dynamics of the evolution of nutrient requirement during gestation. This database contained measures of sow body condition (BW and BT) at Comment citer ce document : Gaillard, C. , Gauthier, R. , Cloutier, L. , Dourmad, J. -Y. (2019). Exploration of individual variability to better predict the nutrient requirements of gestating sows. Journal of Animal Science, sous presse (sous presse), sous presse. , DOI : 10. 1093/jas/skz320.
, from which the kAA values of the other amino acids were estimated as : kAA = (Protm x AAm + Protc x AAc) / [(Lysmaint + (Protm x Lysm+ Protc x Lysc) / 0. 72) x Id(AA:Lys) -AAmaint where kAA is the marginal efficiency of amino acid "AA", Protm the maternal protein depositiont, Protc the conceptus protein deposition, Id(AA:Lys) the AA:Lys ratio in the ideal protein for gestation, and AAmaint the AA maintenance requirement, calculated as the sum of requirements for desquamation, turnover and endogenous losses. citer ce document : Gaillard, C. , Gauthier, R. , Cloutier, L. , Dourmad, J. -Y. (2019). Exploration of individual variability to better predict the nutrient requirements of gestating sows. Journal of Animal Science, sous presse (sous presse), sous presse. , DOI : 10. 1093/jas/skz320 Description of the database (means ± SD) used to evaluate the variability of requirement Comment citer ce document : Gaillard, C.
A nutrient requirement A dataset of 2511 gestations from crossbred Landrace x Large White sows, obtained on an experimental farm from 2009 and 2013 was used. It contained the characteristics of sows as regard to their insemination and farrowing performance used as inputs to the model for predicting the individual variability and dynamics of the evolution of nutrient requirement during gestation. This database contained measures of sow body condition (BW and BT) at Comment citer ce document : Gaillard, C. , Gauthier, R. , Cloutier, L. , Dourmad, J. -Y. (2019). Exploration of individual variability to better predict the nutrient requirements of gestating sows. Journal of Animal Science, sous presse (sous presse), sous presse. , DOI : 10. 1093/jas/skz320.
This paper proposes semiparametric estimation of the relationship between CO 2 emissions and economic activities for a panel of 81 countries observed over the period 1991-2015. The observed differentiated behaviors by country reveal strong heterogeneity as well as different trends across countries and years. This is the motivation behind using a mixed fixed-and random-coefficients panel data model to estimate this relationship. Following Lee and Wand (2016a), we apply a mean field variational Bayes approximation to estimate a log model with structural breaks between CO 2 emissions per capita and GDP per capita including control covariates such as energy intensity and use, energy consumption, population density, urbanization and trade. Results reveal a strong "CO 2 emissions -GDP elasticity", close to one, confirming the increasing but complex link between these two variables.
The statistical evidence for the EKC is not robust and the mechanisms that might drive such patterns are still contested (see for instance  Van Alstine and Neumayer (2010)  and  Stern (2004 Stern ( , 2017)) ) to mention a few). Most of the empirical studies in this area imposed relatively restrictive functional forms (such as a quadratic form for the EKC or for the link between CO 2 emissions and urbanization). However, recently some have used semiparametric approaches, which do not impose any a priori restrictions on the functional form. In this paper, we study the relationship between carbon dioxide emissions and GDP using a semiparametric panel data approach with random coefficients. It allows us to take into account the heterogeneity between countries and periods. Inspired by the work of  Lee and Wand (2016a) , we use a mean field variational Bayesian (MFVB) approach which has great advantages as compared to Markov Chain Monte Carlo (MCMC) technique such as Gibbs sampling.
Our simulation results support the use of the MFVB approach even in the presence of non stationary variables like log CO 2 per capita and log GDP per capita in our application. Our MFVB estimation on a nonlinear semiparametric panel data specification with random coefficients does not lead to non stationary residuals. In a related context, statistical inference for the random coefficient panel data model find that, in a random coefficient autoregression context, there is no unit root problem. Horváth and Trapani (2016)  show that the weighted least squares estimator of the autoregressive root is always asymptotically normal, irrespective of the average value of the autoregressive root, of whether the autoregressive coefficient is random or not, and of the presence and degree of cross dependence (see also  Ng (2008) ).
The vast majority of the existing literature assumed that there exists a linear relationship between urbanization and CO 2 emissions. Ehrhardt-Martinez et al. (2002)  argue that urbanization is a good proxy for modernization, and thus the relationship between urbanization and CO 2 emissions may vary across different stages of development. Our results in Table  1  show that the effect of urbanization on CO 2 emissions is positive and significant. Zhu et al. (2012)  investigate the relationship between CO 2 emissions and urbanization in a sample of 20 emerging countries over the period 1992-2008 using the semiparametric panel data model with fixed effects. They find a nonlinear relationship between log CO 2 emissions and logurbanization and little evidence in support of an inverted-U curve. Wang et al.
(2002)  call the "race to bottom". This rapid overview of the empirical validations of the EKC curve reinforce the idea of using a semiparametric form, allowing slope coefficients to vary between countries and avoiding the use of the cointegration approach with fixed coefficients for a quadratic specification. Nevertheless, methods for estimating a semiparametric panel data model with random coefficients are few. In section 4, we use a Bayesian approach that is capable of estimating such a complex specification.
The data The data come from several sources: the World Development Indicators, from the World Bank, The Emission Data base for Global Atmospheric Research (EDGARv4. 3. 2), from the European Commission, Joint Research Centre (JRC)/PBL Netherlands Environmental Assessment Agency, the Eurostat database, from the European Commission and the Standardized World Income Inequality Data base (SWIID) created by  Solt (2016)  for the Gini index. Merging the data coming from these sources lead to an unbalanced panel data set with missing values. The percentage of missing values range between 0 and 45% depending on the variable considered. We dropped countries, variables and years for which missing values were larger than 15%. These variables included education, health, institutional and political behavior, democracy, literacy, environmental non-government organizations.
(2016)  to mention a few). To guard against possible spurious regressions, we will also introduce structural breaks which correspond to the business cycle reversal phases. These statistics show that there are fundamental differences among countries. The motivation of a mixed fixed-and random-coefficients model is then conditioned on these individual specific effects. Such a specification allows us to draw inference on certain population characteristics through the imposition of a priori constraints on the coefficients (see  Hsiao and Pesaran (2008) ,  Bresson and Hsiao (2011) ,  Hsiao (2014 Hsiao ( , 2015) )  among others). These different elements lead us to choose a semiparametric panel data specification with random intercepts and slopes coefficients which is the subject of the next section.