dataesr/scientific-paragraphs-categorization huggingface dataset:
Second, the potential contribution of each precursor to net hepatic glucose release was evaluated by studying the relationships between the net hepatic glucose release and the availability of each precursor considered individually. Third, the net hepatic glucose release was considered in relation to the sum of all precursors. This last step required that all precursor and glucose fluxes had been measured in any given study. This was not always the case. An all-precursor dataset was constituted with studies that reported the net hepatic release of glucose and the NPA of at least one precursor (n experimental groups = 45; n treatments = 104). When missing, NPA of other precursors was estimated according to  Loncke et al. (2009c) , and their net hepatic uptakes were estimated according to the relationships developed in this work.
Estimations represented 41%, 29% and 40% of NPA data and 43%, 28% and 46% of net hepatic flux data for propionate, L-lactate and α-amino-N, respectively. Estimations were evaluated by comparing predicted and observed values when available. No biases were detected (results not shown). Data coding. Experimental groups were coded to specifically explore intra-study variations in NPA or net hepatic uptake of nutrients (X predictors). The physiological status was also coded for each study because of its potential influence on nutrient net hepatic flux  (Loncke et al. , 2015). Within experiments, data were considered only if the reported X variable varied by more than 2% within-study, as in  Loncke et al. (2015). This conservative threshold eliminated studies with absolutely no variation in X variables but retained studies with small variations.
Studies were considered as fixed factors in the models because experimental conditions and methods are specific to each study and could influence the study effect. This strategy has no impact on the statistical effect of the covariate  (Sauvant et al. , 2008). The range of inference for this meta-analysis is, therefore, limited to the domain of the specific experiments in the dataset  (Sauvant et al. , 2008). When the study effect was not considered, it was specified in the results. All statistical analyses were carried out using Minitab (Version 17). The normality of residuals was checked and outliers were identified on the basis of residues, HI leverage, Cook's distance and DFITS, as in  Loncke et al. (2009c and 2015). Determination of factors influencing the response equations. To explore the causes of heterogeneity between Loncke, Nozière, Vernet, Lapierre, Bahloul, Al-Jammas, Sauvant and Ortigues-Marty Dietary composition and intake calculated by additivity according to  INRA Feed Tables (INRA, 2007) ; PDI = protein digestible in the intestine, defined as the minimum between protein digestible in the intestine when energy is limiting and protein digestible in the intestine when nitrogen is limiting.
Results Description of the meta-design Data were normally distributed and variances were homogenous in the different datasets. Differences in intake, dietary composition and fluxes of nutrients were greater between physiological status (non-productive adults, growing, gestating or lactating animals; Table  1 ) than between species (bovine or ovine; P < 0. 001). As a result, the physiological status was included in the models, and interpretation accounted for the confounding effects of diet intake and composition, and animal species. Differences in intake and dietary composition and fluxes of nutrients according to species are however detailed in Supplementary Material S4 and Supplementary Material Tables  S3a  and b. The potential contribution of precursors to net hepatic release of glucose averaged 51.
At zero NPA of total amino acids, the uptake of total amino acids by the liver was higher with productive animals (growing and lactating) v. non-productive animals (P < 0. 001, Table  4 , model 9). At similar NPA of propionate (model 7, LSMeans effect, P = 0. 001) the net hepatic release of glucose increased when the hepatic uptake of amino acids decreased, and when the hepatic release of β-hydroxybutyrate increased. No improvement of fit was gained when these factors were added in the model. Influence of the hepatic uptake of the sum of precursors. When the all-precursor and the propionic infusion datasets were combined to evaluate the glucose response over a wide range of precursor supply, glucose release was curvilinearly related to precursor availability (Figure  4 ). In this combined dataset, the NPA of summed precursors ranged from 1.
Modelling hepatic release of glucose Loncke, Nozière, Vernet, Lapierre, Bahloul, Al-Jammas, Sauvant and Ortigues-Marty  e Percentage of estimated precursors : 30. 6%, 29. 6% and 31%, respectively, for models 13, 14 and 15. while net hepatic uptake ranged from 1. 098 to 9. 944 mmol C/ kg BW per h (Figure  4c  and d ). At similar precursor availability (LSMeans effect on both models), glucose release was increased when the net hepatic release of β-hydroxybutyrate increased (P < 0. 05). Another meta-analysis was conducted on the all-precursor dataset only, excluding the propionate infusion studies, to explore the more linear portion of the relationship. The NPA of summed precursors ranged from 0. 994 to 9. 69 mmol C/kg BW per h and was closer to the physiological range. A linear model (quadratic model not significant, results not shown) was applied and the net hepatic release of glucose increased by 0.
Net hepatic insulin fluxes The insulin dataset was limited to n = 19 treatments. Arterial concentrations ranged from 6. 3 to 71. 9 μUI/ml and increased (intra-study slope) by 3. 04 ± 0. 36 μUI/ml per g DMI per kg BW per day (P = 0. 004). The NPA of insulin also increased with intake (expressed in g/kg BW per day) by 1. 31 ± 0. 25 μUI/kg BW per h (P = 0. 001, n t = 17) per g DM intake, by 9. 81 ± 1. 61 μUI/kg BW per h per g of starch digested in the small intestine (P = 0. 01, n t = 8) and by 8. 02 ± 1. 43 μUI/kg BW per h per g NDF digested in the rumen (P = 0. 001, n t = 17). Moreover, the NPA of insulin tended to increase with the portalarterial concentration difference (in mM) of propionate (slope = 44. 48 ± 20. 16; P = 0. 11, n t = 8) and acetate (slope = 10. 82 ± 4. 43; P = 0. 09, n t = 8), but not glucose (P = 0.
Discussion The present work substantiates the use of the NPA, instead of total afferent fluxes, as the predictor for the net hepatic uptake of propionate, α-amino-N and L-lactate in a database like the one used in the current study where within-study variations were mainly caused by dietary changes and where lactating cow data were obtained at mid-lactation. The higher the contribution of NPA to the afferent flux, the lower the difference in fit between the two predictors. Obviously, the relevance of using the NPA as predictor depends on the nutrient and on the range of nutritional situations under study. As a result, for propionate, α-amino-N and to a lower extent for L-lactate their net hepatic uptakes follow mass action laws, with NPA as the main statistical driving force. The statistical driving force may differ from the biological one.
This is especially the case for the amino acid fluxes, which are more accurate than the α-amino-N ones, and for animals that are not representative of this dataset. Indeed, when comparing cows pre-and post-calving, it was clear that increased net portal absorption of amino acids post-calving accompanying increased DMI was not related to increased hepatic net removal, the latter declining in response to decreased arterial amino acids concentrations related to the initiation of lactation  (Doepel et al. , 2009). Also as a consequence of this statistical choice, endogenous L-lactate availability from mobilized tissues, as in early lactation, is not accounted for. The within-study approach which was applied here focuses on incremental responses. It is testing the hypotheses that marginal changes in the X variables are inducing marginal changes in the Y variables of interest.
Domain of application of the models Empirical prediction models apply to a strict range of validity. Because available data were balanced between cattle and sheep, models apply to both species. Species or physiological status significantly affected models of the present work, but as in  Loncke et al. (2015)  it was not always possible to evaluate whether it was a strict species or physiological status Modelling hepatic release of glucose effect, or if there was some confounding with nutritional conditions. These effects are discussed below for each nutrient. In all cases, using physiological status as a covariate was more discriminant than using species as in  Loncke et al. (2015)  for ketogenic nutrients. This covariate also encompasses differences in the level of production. As for other models developed from the same Flora database  (Loncke et al.
Despite these effects, we decided not to correct nutrient fluxes in order not to introduce further uncertainties to the data besides those inherent to the measurements  (Rodriguez-Lopez et al. , 2016). Hepatic uptake of individual precursors Propionate. Results confirm the high hepatic uptake of propionate  (Armentano, 1992). In the propionate dataset, no data exceeded the extraction capacity of the liver, propionate supply remained below levels obtained with infusions (e. g. Berthelot et al. , 2002)  and portal propionate concentrations remained inferior to 1 mM. The intercept, not different from zero, was consistent with zero uptake in the absence of ruminal fermentations. The lowest hepatic uptake of propionate noted in growing animals was compatible with the known inhibitory effect of butyrate on propionate extraction  (Berthelot et al.
, 2002) , as in the dataset hepatic supply and uptake of butyrate were elevated  (Loncke et al. , 2015). L-Lactate. Hepatic uptake of L-lactate was related to two different types of predictors. First, amounts of L-lactate and propionate appearing in the portal vein, which reflect dietary nutrient supply and synthesis of L-lactate from glucose in gut tissues  (van der Walt et al. , 1983 ) that increases at high propionate levels  (Harmon et al. 1991). Considering NPA of propionate as an additional predictor significantly improved the model (model 4 v. 3) and removed all interfering factors. We interpreted this improvement as the result of high propionate availability in the current dataset, and subsequently an increased hepatic conversion of propionate into L-lactate as shown in vitro by  Demigné et al.
(1996)  and  Reynolds (2006) ) strictly applies to α-amino-N as a proxy of the summed amino acids and does not apply to individual amino acids whose uptake is more related to their total afferent flux  (Doepel et al. , 2009). Interestingly, the physiological and probably the underlying nutritional status of the animal (species, diet composition and intake, and level of production) influenced the baseline uptake (intercept) and not the marginal uptake rate (slope). Baseline net hepatic uptake (intercept) was higher in sheep than in cattle, probably reflecting lower production levels and lower tissue amino acid requirements  (Blouin et al. , 2002; Reynolds, 2006). In a meta-analysis with a different dataset from the same Flora database,  Martineau et al. (2011)  attributed the higher recovery of ingested nitrogen in the portal vein of sheep v.
Hepatic conversion of precursors into glucose An interesting outcome of this work is to show that the net hepatic release of glucose increases linearly with the availability of summed precursors (portal appearance or hepatic uptake) up to approximately 6 mmol C/kg BW per h, beyond which the conversion rate decreases. In the present dataset, the highest levels of precursor supply were met by intragastric infusion of individual nutrient (e. g. Majdoub et al. , 2003). Whether it is the high level of precursor supply or the imbalanced nutrient supply (as in  Kraft et al. , 2011  with N deficient diets) which is responsible for this plateau would remain to be confirmed. This reduced efficiency of hepatic conversion of precursors into glucose suggests a supply of precursors in excess of glucose demand.
Hydro power can provide a source of dispatchable low-carbon electricity and a storage solution in a climate-dependent energy mix with high shares of wind and solar production. Therefore, understanding the effect climate has on hydro power generation is critical to ensure a stable energy supply, particularly at a continental scale. Here, we introduce a framework using climate data to model hydro power generation at the country level based on a machine learning method, the random forest model, to produce a publicly accessible hydro power dataset from 1979 to present for twelve European countries. In addition to producing a consistent European hydro power generation dataset covering the past 40 years, the specific novelty of this approach is to focus on the lagged effect of climate variability on hydro power.
Thus, understanding the impacts of climate variability on hydro power can assist with ensuring a stable pan European electricity network in time. In addition, other specialists such as energy traders require and utilize large-scale power production estimations for their bidding. An accessible homogenous hydro power dataset for Europe over several decades, by extending the short periods (normally a few years) of publicly available power generation data, would therefore provide an invaluable input to pan-European network assessment and planning. This is one of the goals of the Copernicus Climate Change Service (C3S) for the Energy Sector (C3S Energy)  [5]. Here, we introduce a dataset of hydro power generation for 12 European countries using a machine learning model, the random forest. While the model we propose is highly simplified and uses only two climate variables (2 metre temperature and precipitation) as predictors, it is capable of reproducing with high accuracy observed power generation data at the country level and at the sub-country level (snow depth was also used for testing purposes as described later, but it is not included in the final model).
The country average level is also a relevant scale for many applications (European-scale optimisation models for instance), including adequacy outlooks run by Energies 2020, 13, 1786 3 of 17 TSOs and European-level investments studies. Naturally, however, the countries considered have different sizes, and hence our model provides an indirect measure of its ability to reproduce hydro power generation at different scales. In addition, however, an explicit sub-country test for Italy is also presented. This paper is structured as follows. Section 2 describes the data used in this work and how the machine learning model was setup. Section 3 shows the model results, including a comparison with measured power data and further validation tests with independent datasets. Finally, Section 4 presents a summary together with a discussion on how to use the model and dataset efficiently.
Data The data used in this work meet two requirements. Firstly, they should be publicly accessible as the C3S Energy service output will be made publicly available; and secondly, they should span a long period and cover as many European countries as possible in a homogenous and consistent manner. Considering these two criteria, we selected European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset ERA5 (ECMWF Re-Analysis fifth generation)  [7]  for the climate data and the European Network of Transmission System Operators for Electricity (ENTSO-E) Transparency Platform  [6]  for the energy data. The data from the ENTSO-E Transparency Platform includes many variables for different purposes in the energy sector starting from 2015. For this study, we used the Aggregated Generation Per Type product [Product number 16.
If one were interested in reconstructing the actual hydro power production, this can be achieved by dividing our estimates by the installed capacity (as given in Table  1 ) and multiplying by the actual installed capacity. Attention is also drawn to the fact that the installed capacities reported in the ENTSO-E dataset can be different from the actual installed capacities provided by national authorities and it might be inconsistent with the generation time series in the Transparency Platform. This inconsistency, for example, may lead to capacity factors (actual generation divided by installed capacity) larger than 1 (this can be the case for German reservoir-based and Norwegian run-of-river generation for instance). A description of the ENTSO-E Transparency Platform and its limitations can be found in  [6].
Since this inconsistency derives from the process of reporting data which we cannot intervene, we decided to use the data as provided by ENTSO-E without any pre-processing. In any case, the installed capacity of hydro power has changed only marginally in the selected countries, and so we can assume that the observed variability of generation mainly reflects the natural variability of the climatic inputs. ERA5  [7]  is a reanalysis climate dataset developed by the ECMWF within the framework of the C3S programme. ERA5 provides essential climate variables at hourly resolution back to 1979 globally at approximately 32 km spatial horizontal resolution. To make climate data temporally and spatially corresponding to generation data, ERA5 data were also aggregated to daily values and country averages for the twelve countries considered.
However, river flow data is not available in the ERA5 dataset (as well as other common climate datasets). Therefore, here, we attempt to capture the variability of inflow indirectly with snow depth, precipitation and temperature. Although it might be clear why precipitation and snow depths could impact the hydro power generation, their effect might not be instantaneous. Snow accumulates in mountainous areas in winter and takes time to melt and contribute to river flow in spring and summer; precipitation over a large area also takes time to become run-off or underground water and eventually flow into rivers, depending on the characteristics of individual water catchments. To this end, we have applied two different methodologies to model this non-instantaneous time relationship, using both optimal lag and multiple lags of temperature and precipitation variables.
Optimal Lag Approach With the optimal lag (see also  [13] ), generation at a given time is modelled using the precipitation and temperature data at the same time, plus a single lagged precipitation and temperature. The optimal lag is defined as the lag that yields the highest correlation between the modelled and actual generation. While the optimal lag approach provides a relatively simple methodology to improve the simple straight daily average predictors used in our random forest model, the local correlation maxima in general are not robust with respect to the length of the dataset (the optimal lag can be considerably different whether the reference dataset is a few years rather than several years). This is also due to the fact that there are normally several local maxima, which may not be distinctly different from each other.
Multiple Lag Approach Multiple lags are multiple and regular instances of the lagged-averaged predictors, computed with increments of 5 days (using less than 5 days yields negligible differences). These different lag times are then used as predictors to the random forest model. To reduce their number, and especially to understand which lags are more critical for simulating hydro power generation, we evaluate the model output through a process of selecting important predictors implemented in the two-step random forest model; further explanation is given in Section 2. 2. 3. Lag selection is automatic and adapts each time the training dataset is updated. In terms of construction, this method is more robust than the optimal lag approach, which is based on one single criterion. Formally, lagged precipitation is calculated as the sum of all precipitation within the lag range, while lagged temperature and snow depth are average values.
Results Our methodology is tested in three different ways. Firstly, a 5-fold cross-validation is applied to compare the optimal lag and multiple lag approaches in modelling hydro power generation. Secondly, the selected best model from the previous test is assessed against independent and longer datasets for three countries (France, Sweden, and Finland) to examine the robustness of the model and its ability to extrapolate in time. Thirdly, the model is also implemented at the sub-country level using the Italian bidding zones to test our methodology at a more local, regional scale.
05 in orange), and not different (p > 0. 05 in pale yellow). The white colour indicates no available data. Since the multiple lags approach is more robust than the optimal lag approach with respect to the length of the training dataset, and since multiple lags of temperature and precipitation are able to capture the impact of snow on hydro power, we retain the multiple lags of temperature and precipitation as our final model (model (4)-green bars in Figure  2 ). This is also considering that in the second phase of the C3S Energy service, the same model is applied to climate projections and seasonal forecast, for which snow depth is not always available (and in any case, snow depth quality in long-range predictions is worse than temperature and precipitation). Table  4  presents the correlation coefficient of the modelled versus observed generation values with the chosen model, resulting from the 5-fold cross-validation process (the cross-validation columns).
Furthermore, a leave-one-out test was also implemented in order to assess the model performance using an alternate assessment procedure. Each year in the period 2015-2019 was left aside for validating the model trained on the rest of the dataset. The average results of this leave-one-out test are presented in Table  4  and are compared to the 5-fold cross-validation method. Overall, the model performance is considerably lower when using the leave-one-out test, and again lower in reservoir-based than in run-of-river generation, with an average correlation of 0. 47 and NMAE decrease of 0. 53% for reservoir-based, and 0. 67 and 3. 77% for run-of-river generation, respectively. Nevertheless, some countries are more robust than the others. For example, Spain and Italy perform well under the leave-one-out test.
This highlights the fact that not only can the hydrological and climate regimes vary from year to year, but the interannual variability in hydro power generation strongly depends on non-climate factors related to power-dispatching decisions which depend on a variety of factors such as electricity price, competing water usage, and power plant maintenance regimes. Further discussion on the ability of our model to extrapolate hydro power beyond the training dataset using independent datasets is presented in Section 3. 2.
Country In order to produce a dataset of hydro power generation for the twelve countries of interest, country-specific models were trained again with the full observed ENTSO-E dataset for the period 2015-2019 using model 4 (multiple lags with temperature and precipitation). The model was then used with the full ERA5 time series data to extend the hydro power generation simulation to the historical period from 1979 to present-so called reconstructed data. While the focus of this paper is on the historical reconstruction over the past ca. 40 years, it is worth noting that the C3S Energy service is also developing similar datasets for two other cases: seasonal forecasts, up to six months ahead, and projections, up to 2100. The same model developed for the historical period is implemented in both of these cases.