dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2017; Sharma et al. , 2018). Case studies are underway to assess these PBK models using animal data from the joint Food and Agriculture Organisation/World Health Organisation Joint Meeting on Pesticide Residues monographs on PPPs (cf. http://www. who. int/foodsafety/areas_work/ chemical-risks/jmpr/en/) and other regulatory data. Remaining challenges in the further development of in silico models include the need to establish parameter databases for human PBK models and to improve the available QSAR models. Further, PBK models need to be linked with other tools, such as AOP and mechanistic models, and system biology via key intermediates and molecular initiating events, that enable systematic understanding of the toxicity of the substance  (Sharma et al. , 2017b). Finally, databases on cell response systems are required that include information on dose-response relationships, as well as functional genomic tools that allow mapping and modelling pathways.
These PBK models include estimates of absorption rates and extent, metabolism, distribution into tissues, the excretion of the unchanged drug, and drug transport to predict drug concentration and effect in humans. IVIVE techniques are used to provide the input parameters for the PBK models. Data obtained with human liver microsomes, fresh or cryopreserved human hepatocytes, or recombinantly expressed human enzymes are used to predict in vivo metabolic drug clearance  (Fisher et al. , 2016). Hepatic scaling factors are required for IVIVE to convert the measured rate of metabolism in the in vitro system to an in vivo rate per whole liver. To describe the disposition of the compounds in a target population, the relevant distributions of values for demographical, biological, physiological and genetic parameters are identified, and the covariation between these parameters is assessed.
In the veterinary pharmaceutical or vaccines sectors, substance-induced effects have to be assessed in target animals and often also in toxicological species (rat, dog) for assessment of human safety (as relevant to manufacturing, user and human food safety of veterinary pharmaceuticals). The value of including such data into the databases for PBK modelling merits further evaluation. Data from target animals could serve to reveal species-specific physiological or pathological differences. Even though it might be laborious to comprehensively cover the spectrum of known species-specific differences in PBK models, their inclusion may improve the predictivity of the modelling. In the food safety area, a number of models are available mostly for agrochemicals and food contaminants (including persistent organic pollutants, metals, perfluoralkyls, etc.
In this context, the following actions were recommended: 1. Promote the establishment of a Toxicokinetics Speciality Section within EUROTOX; 2. Explore possibilities to create a database of modern toxicokinetic tools to assist hazard and risk assessment, building on existing information (e. g. , EFSA, 2014a); collation of a contemporaneous list of open tools (open source, managed by the EPAA); 3. Dissemination and training event on RVis with toxicokinetics stakeholders in early 2019. In closing the EPAA Partners' Forum Finding synergies for 3Rstoxicokinetics and read-across, Renate Weissenhorn (EC DG GROW), the EPAA EC Co-Chair, expressed her appreciation that the Partners' Forum had fully met all of its aims: The fruitful discussions had served to identify synergies and to provide incentives for future cross-sector collaboration.
database https://eurl-ecvam. jrc. ec. europa. eu/databases/jrc-qsar-model-database Human in vivo toxicokinetic data ECVAM KinPar database https://eurl-ecvam. jrc. ec. europa. eu/validation-regulatory-acceptance/docs-toxicokinetics/ECVAM %20KinParDB%20-%20Short%20manual%20data RIVM interspecies database https://www. interspeciesinfo://toxbank. net/ ToxCast high-throughput toxicology https://www. epa. gov/chemical-research/toxicity-forecasting Toxicology in the 21st Century https://www. epa. gov.
For convenience, this workflow will be referred to as 'SIMPCODE' for the remainder of the paper. The assumptions of the example are given in Table  1. In Fig. 2  the white ovals denote the input variables , including: , , and ; the grey ovals represent the performance outputs, , which include: , , , , and. Fig. 2  represents the workflow of SIMPCODE in its default state, that is, the default inputs to the individual constituent models are preserved. Depending on the study, the designer may choose to designate some of the default outputs as inputs (independent variables). In this case the workflow needs to be dynamically "reversed" using numerical methods  [6].
= - Margin as an absolute value % = [( -)/ ] * 100 Margin as a percentage The implications of placing margins on the variables in a computational workflow are discussed next, by making use of SIMPCODE (Fig. 2 ), where , , and are independent input variables, whereas and are computational outputs. and are 'intermediate variables' and could be treated either as input or output variables. Margins could be applied to any of these variables for two main reasons:  Margins accounting for evolving design requirements: these could be allocated on design or performance parameters to enhance flexibility, i. e. , accounting for the possibility that requirements may change with time (both during and/or beyond the development phase of the product). In other words, providing margins may offer reserves that could allow for the future evolution of the product (system).
For example, referring again to the SIMPCODE example, a margin could be applied to the equivalent still-air range ( ), to account for uncertainty in the inputs to the Breguet-Range model. Margins accounting for model uncertainty are applied to the output variables of the computational models. The relationship between margins, uncertainty, and the probability of constraint satisfaction has been recognized in the literature (e. g. [14] ). This relationship is illustrated by two possible design scenarios. In the first scenario, the margin is specified a priori, based on experience, or on historical data (see, e. g. ,  [4] ,  [5] ). By using this margin, as well as the results from an uncertainty quantification study, the decision maker would be able to assess the probability of constraint satisfaction as a consequence of setting (allocating) this margin.
Each element in the is the ordered -tuple ( , , … , ), where belongs to , belongs to and so forth. In the present work, the ordered -tuple of the values of the margins is referred to as a 'margin combination'. The general form of the is given by Eq. (  9 ). It is important to note that if the number of margins, , is large, a sensitivity analysis study could be performed to select a few most influential ones. = × × … × = { ( , , … , , … , ) | | ∊ } (9) Referring to SIMPCODE as an example, consider the margins on Lift-to-Drag Ratio ( = ), Empty Weight ( = ), and Payload Weight ( = ). is applied to a representative nominal value for payload weight of 28 000 , whereas and are applied to the relevant model outputs. Intervals can be specified on these margins, for example, = = [0, 5]%, = = [0, 5]% , and = = [0, 10]%, respectively.
= { | (DP, ) -≤ 0; ∀ = 1,2, … , } (13) where is the set of feasible margin combinations, = ( , , … , ) is the vector of a particular magnitudes for each of the margins, DP is the selected design point at value ( , , … , ), and represent the ℎ constraint. Note that the condition in Eq. (  13 ), i. e. , (DP, ) -≤ 0, corresponds to the case when the constraint is 'lower the better'. For 'the higher the better' type constraints, the condition will be, (DP, ) + ≤ 0. It is important to note that, although will be common for all the points in the design space, the feasible may be different for each design solution (point). In other words, a feasible margin space is linked to a particular point in the design space. This is demonstrated in Fig. 4 , where the SIMPCODE design space is shown, along with two selected design points, DP1 and DP2, and the corresponding margin spaces.
Intra margin space (MS) investigation Given a single design point, selecting particular values for one or more of the margins may cause the maximum allowable values for the remaining margins to change. It is therefore desirable to be able to explore the effects of selecting specific combinations within the feasible margin space associated with a particular design point. For such an exploration, it is proposed to use a matrix of two-dimensional contour plots of the margin space ( ). An illustration of how this matrix can be used is shown in Fig. 6 , using SIMPCODE as a simple example. The matrix shows all three combinations of the contour plots (i. e. vs. , vs. , and vs. ). The matrix shown in Fig. 6  corresponds to the selected design point, DP1 in Fig. 4. For the selected margin combination point, MP1 ( = 1%, = 3%, = 0%), the iso-constraint lines for the aircraft range ( = 3500 ) are shown in each of the three contour plots.
Deterministic design band filtering In the case where the interest is more on ensuring that minimum values are assigned to the margins, rather than investigating the exact relationships between the margins (and design variables), it is proposed that a parallel coordinates plot (PCP) be utilized. It can be used to filter out solutions that are not capable of providing the minimum requested margin values, subject to performance constraints. The remaining solutions constitute a deterministic design band ( ) in the parallel coordinates plot, which can be represented by: = {( , , )| ( , ) -≤ 0, = 1,2, … , } (14) The deterministic DB for SIMPCODE is shown in Fig. 8. It is confined by the dashed blue lines. In addition to the design variables ( and ) and performance parameters ( and ), the plot in Fig.
Trade-off between margins and probability of constraint satisfaction To initiate this type of trade-off study, information on the probability density functions (PDF) of the uncertain sources must be available. The sources of uncertainty considered in the SIMPCODE example include model uncertainty in the aerodynamic and empty mass models, respectively. To render the outputs ( , ) of these models stochastic, a randomization treatment needed to be applied. In this case, the randomization treatment provided in Ref. [19]  was employed and the PDFs were assumed to be triangular. It should be noted that, to avoid double accounting of the uncertainty, margins and should be removed. The study involves uncertainty propagation and the calculation of the probability of constraint satisfaction for each design point in the design/margin space.
Here, these two iso-contours are combined in a single plot, wherein the designer can interactively change the values of different margins and alter the thresholds for the probabilities of constraint satisfaction. Employing SIMPCODE again, the technique is illustrated in Fig. 9. The white areas in the 2D design space in the plot in Fig. 9a  represent the robust region, where the corresponding probability of constraint satisfaction on is higher than 85%. The iso-contour line represents the location of 85% probability of constraint satisfaction. The iso-contours of different possible margins combinations are shown in Fig. 9b. For this example, an arbitrary margin combination selection is shown in Table  2. The contour line formed by a margin combination, and the associated reduction in the feasible design space can visually be overlapped on the robust/non-robust areas.
Trade-offs between margins and performance In general, the allocation of margins adversely affects performance, as it involves placing 'reserves'. The purpose of trading-off margins with performance parameters is therefore to identify combinations of margins that adequately account for uncertainty, but still allow acceptable performance. A technique using scatter plots is presented for interactively exploring the change in the location of the Pareto front when the margin values are varied. In this technique, a two-dimensional scatter plot is used to investigate the effect of the selected margin combinations in the performance space. Using SIMPCODE again, Fig. 11  shows a two-dimensional scatter plot of the performance space where smaller values for and larger values for are sought. The Pareto front in Fig.
At aircraft level, the NASA Flight Optimization System (FLOPS)  [22]  was used for aircraft sizing and performance analysis. Models for the subsystems were assembled from several research papers  [23] -  [27]. The outputs of the system models were aggregated and linked to the aircraft level to compute the total engine shaft power offtake and systems weight. All models were assembled into a computational workflow using the dynamic (automatic) workflow creation module  [6]  in AirCADia. In total, 171 models and 317 variables comprise the workflow.
Specifically, these allow decision makers, such as designers and product architects, to conduct interactive trade-offs between margins and margins, margins and performance, and margins and uncertainty (probability of constraint satisfaction). This was made possible by rendering the relationship between design variables, margins, and performance variables explicit, as well as through the design exploration means described above. The approach and the associated enablers were implemented into a prototype software tool, AirCADia, which was used for a qualitative evaluation by practicing designers. The evaluation, conducted as part of the EU TOICA project, confirmed the practicality of the approach. In particular, the explicit specification of margins, relationship between design variables, margins, and performance variables, and bi-directional links between margins and design spaces, demonstrated that the requirements prescribed by the industrial stake-holders were largely met.
The same also applies for LM3V  (Shevliakova et al. , 2009). Other so-called area-based DGVMs  (Smith et al. , 2001 ) such as ISAM  (Jain et al. , 2013)  and LPX-Bern 1. 0  (Stocker et al. , 2014)  included secondary land tiles in the model but their capability to represent different rotation lengths in land use is limited. In the ORCHIDEE model, sub-grid forest cohorts have been recently included in the ORCHIDEE-CAN branch mainly for forest management purposes  (Naudts et al. , 2015) , but a combination of both sub-grid land demography and gross land transition is still missing. Here we present the new model developments in OR-CHIDEE that combine both sub-grid land cohorts and gross LUC. The objectives of this study are (1) to document a new LUC module, including sub-grid vegetation cohorts, forest harvest, and gross LUC in the ORCHIDEE model, that can be run with and without sub-grid age dynamics; (2) to document through an idealized pixel simulation the simulated carbon fluxes from shifting cultivation or land turnover between model set-ups with and without sub-grid age dynamics; and (3) to document the model behaviour and forest age dynamics associated with the historical changes in LUC activities.
Whereas the current paper focuses on documenting new model developments and subsequent changes in model behaviour, a companion paper presents a global reanalysis of historical LUC emissions  (Yue et al. , 2017). 2013 ) is included (r2615). This allows for distinction between natural grassland and pasture that have been mixed together in previous LUC simulations by ORCHIDEE. In ORCHIDEE, land cover types are represented as PFTs, with each PFT being associated with a set of parameters. A typical model simulation consists of two stages: a spin-up stage with stable or constant forcing data until the model reaches an approximately equilibrium state, to mimic an era with no appreciable human perturbation, and a transient stage in which the model is forced with temporally varying forcings (e. g. climate, atmospheric CO 2 , land cover).
ORCHIDEE-MICT also inherits this externalization feature from ORCHIDEE-trunk. Here we ported the codes of forest age class functionality from ORCHIDEE-CAN to develop the CFT functionality needed for LUC simulation with cohorts in ORCHIDEE-MICT. The code base to include sub-grid forest cohorts was migrated from ORCHIDEE-CAN, with substantial adaptions being made in ORCHIDEE-MICT. Except for this, all other LUC developments have been achieved within the current study. Contrary to ORCHIDEE-CAN (see above), ORCHIDEE-MICT uses woody biomass to delimit different forest cohorts, with older cohorts having a higher woody biomass. Forest grows old by moving from the current cohort to the next one when the woody biomass exceeds the cohort upper boundary. Except for the cohort boundaries, no further cohort-specific parameterizations have been performed, so essentially all cohorts are governed by the same set of biophysical and ecological parameter values.
Conversion of herbaceous vegetation to forests or other vegetation Start with the oldest cohort, then move to younger ones. recruitment does not modify forest cohort ground coverage. In addition, forest mortality and subsequent regeneration due to forest fires are handled in a similar manner. ORCHIDEE-MICT has integrated a prognostic fire module to simulate open grassland and forest fires arising from both natural and anthropogenic ignitions  (Yue et al. , 2014). Other forest disturbances, such as wind-throw, diseases, and insect outbreaks, are not explicitly considered in ORCHIDEE-MICT. Because of these reasons, after the spin-up, the only way to create secondary cohorts in the model is through LUC. When entering transient simulations with LUC, younger cohorts will begin to be created. From a modelling perspective, the oldest cohorts in ORCHIDEE-MICT are somewhat equivalent to the primary lands (especially, the oldest forest cohorts are equivalent to primary forests), and other younger cohorts are analogue to secondary lands.
Model developments to include gross land use change and forest harvest, with and without sub-grid cohorts This section describes the implementation of gross LUC and forest harvest with sub-grid CFTs. We focus on the implementation with sub-grid cohorts because the same LUC process without cohorts could be simply treated as a particular case in which all MTCs have only one single cohort. The module interface is designed to receive forcing information on land area fluxes among four basic land cover types of forest (f), natural grassland (g), pasture (p), and cropland (c), taking into account the current LUC modelling landscape in DGVMs (as briefly reviewed in the introduction) and the availability of LUC reconstructions (e. g. Hurtt et al. , 2011). The present developments are intended for the case in which changes in vegetation coverage are only driven by historical LUC activities and so there is no need to use the dynamic vegetation module of ORCHIDEE.
This is different from the LUC implementation in JSBACH DGVM in  Reick et al. (2013)  in which a lot of effort has been devoted to reconciling the vegetation types in the forcing data (primary and secondary natural lands in the Land-Use Harmonization data set version 1 or LUH1 data) and the vegetation distributions simulated by the dynamic vegetation module of JSBACH. We focus on including sub-grid land cohorts in the model and implementing a set of hierarchical rules for which land cohorts are subjected to different LUC processes (Table  2 ). The allocation of natural lands into forest versus grasslands in the model, and the reconciliation of LUH1 land cover distribution and model PFT map, are instead handled by independent preparations of reconstructed historical land cover map time series.
e. export of animal production and methane emissions (see  Chang et al. , 2015, for details). F Inst and F Wood are both fluxes on an annual timescale that depend only on wood mass at the time of forest clearing and the respective wood product degradation rates (see Sect. 2. 1. 5). F HR is simulated at a time step of 30 min and depends on soil temperature and moisture. F Fire is simulated with a prognostic fire module SPITFIRE  (Yue et al. , 2015). The LUC emissions (E LUC ) are quantified as the difference in simulated NBP between two paired simulations, with LUC (or a specific LUC process) included in one simulation but not the other one: E LUC = NBP LUC -NBP control , (4) where NBP LUC and NPB control are NBP simulated with and without LUC. A negative E LUC denotes a carbon source to the atmosphere, i.
Simulation over southern Africa Subsequently, the model behaviour has been documented for a real-world case over the region of southern Africa (south from the Equator of the African continent). All three LUC types occurred historically in this region, making it ideal to demonstrate model behaviour regarding forest cohort dynamics as presented in Fig. 5. This regional simulation serves a single purpose -to further exemplify model features that cannot be sufficiently demonstrated over a grid cell. The regional simulation is performed at 2 • resolution for 1501-2005. We used the land use reconstruction from LUH1 covering 1501-2013  (Hurtt et al. , 2011 , http://luh. umd. edu/ data. shtml#LUH1_Data) re-gridded from the original 0. 5 • to a 2 • spatial resolution. From the LUH1 data set we derived the matrices of the three types of LUC: net land use change, land turnover, and wood harvest.
Land turnover information is extracted from LUH1 as the minimum land fluxes between two vegetation types. Wood harvest from primary and secondary forests in LUH1 is used, while wood harvest from non-forest is not. Climate forcing data are from CRUNCEP-v5. 3. 2 at a 2 • resolution. For the spin-up, climate data were cycled from 1901 to 1910, with atmospheric CO 2 concentration fixed at the 1750 level (277 ppm). In the transient simulation, atmospheric CO 2 concentration began to increase in 1750 and climate data were varied starting in 1901. The dynamic vegetation module was turned off in order to apply the prescribed historical LUC. Factorial simulations are conducted to highlight changes in areas of different forest cohorts when different LUC processes are included, as shown in Table  4. Each forest MTC has six CFTs to represent six cohorts.
g. Fig. 5 ), for example, when prescribed secondary forest wood harvest can actually harvest a primary forest in the model if all younger cohorts are exhausted. Second, because of this clearly defined border of the LUC module to use land areas as the input information, model output from ORCHIDEE-MICT can potentially disagree with the socio-economic information used to generate the LUC forcing data. For instance, crop yield simulated by ORCHIDEE may differ with that used to convert food demand and consumption to cropland area, so that simulated crop output or food production may disagree with historical food demand in the real world. The same applies to forestry wood production: simulated harvest wood volume might disagree with the wood volume actually used to generate the harvest area information -the harvested wood biomass information is provided in the LUH1 data set but not used as an input in the current stage of model development.
This largely raises the issue of to what extent the information that drives LUC decisions can be internally integrated into DGVMs, for example, to directly use crop production, rather than cropland area, or wood volume, rather than forest harvest area as the model input. One potential obstacle is that statistical information (e. g. on wood volume demand) is often available on a regional basis (FAO global forest resource assessment, http://www. fao. org/ forest-resources-assessment/en/; Eurostat, http://ec. europa. eu/eurostat/data/database), and complex decision-making rules are needed to disintegrate such information on spatial grids that DGVMs are operated on. But in general and over the long term, land use or land management decisions need to be integrated directly into DGVMs. ORCHIDEE-CAN has integrated forest management decisions based on simulated tree diameters and stand density, so that harvested wood biomass is actually a model output that can be validated against historical statistical data  (Naudts et al.