dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014; Vezy et al. , 2018). Consequently, the aims of the present study are to: (i) develop metamodels for spatially-dependent variables based on MAESPA, which has already been calibrated and validated on coffee agroforestry systems; (ii) develop a new plot-scale 1D, 5-layer (shade tree, coffee, and 3 soil layers), average plant, ecophysiological process-based model for coffee crops grown under agroforestry or in full sun, while combining the advantages and strengths of three previously published models. Nutrients are considered non-limiting in this first version, which is realistic for many field conditions. (iii) parameterize and evaluate the model using a multi-objective approach applied to an extensive dataset from a long-term observatory including the energy and water balance, GPP, yield, NPP and carbon mass per organ.
Comprehensive database of measurements "Coffee-Flux" is a collaborative research observatory (https://www. umr-ecosols. fr/recherche/projets/53-coffeeflux) monitored continuously since 2009 and located on the Aquiares coffee farm. This research site has been intensively studied and described in detail, notably for hydrology and eddy-covariance by  Gómez-Delgado et al. (2011) ; LAI by  Taugourdeau et al. (2014) ; light budget by  Charbonnier et al. (2013) ; belowground biomass and NPP by  Defrenet et al. (2016) , ecosystem biomass, NPP and LUE by  Charbonnier et al. (2017) ; and energy balance, water balance and surface temperature by  Vezy et al. (2018). The measurements used in this study to evaluate the model included: half-hourly  (Net Ecosystem Exchange),  (sensible heat flux),  (latent heat flux), and  (net radiation) measured using an eddycovariance tower.
Description of MAESPA MAESPA is a 3D explicit process-based model  (Duursma and Medlyn, 2012; Medlyn, 2004; Wang and Jarvis, 1990 ) used to simulate forest energy, water, and carbon fluxes at the scale of the individual tree. The light interception module, canopy temperature, water and energy balance of the model have already been calibrated, used and validated on the same agroforestry system  (Charbonnier et al. , 2013; Vezy et al. , 2018). MAESPA is particularly well suited to simulate agroforestry system fluxes because it describes the forest at voxel scale, which is a homogeneous representation of a sub-part of the tree crown. It can manage several tree species with their own position on the plot, their overall structure (crown height, width, etc. ), and their physical and physiological parameters.
Thus, MAESPA computes a fine estimation of the light interception, energy, water, and carbon fluxes of each plant in the forest and of the soil, while taking the spatial heterogeneity of the canopy into account. However, MAESPA lacks a carbon allocation module or growth process, and requires computationally-intensive simulations, e. g. a week of computation on the shared Montpellier Bioinformatic Biodiversity (MBB) computing cluster platform to complete a distributed simulation of a 0. 2 ha AFS plot, including 4176 Coffea arabica sprouts and 14 Erythrina poeppigiana shade trees over one year at 30-minute time-scale. Consequently, MAESPA was used here mainly to compute metamodels to simulate spatialdependent variables.
MAESPA was used to simulate one year of the coffee agroforestry system from the Coffee-Flux site in the Aquiares farm in order to calibrate the metamodels. The parameterization and description of these MAESPA simulations are reported in  Vezy et al. (2018). The outputs considered are converted from half hourly values at shade tree and coffee resprout scale to daily plot scale values for each plant layer during the same year (Figure  1 ). The metamodels were trained on 80% of MAESPA simulation outputs aggregated at daily time-scale and evaluated over the 20% remaining validation data to compute out-of-sample statistics. Both training and testing partitions were sampled based on the dependent variable subgroup percentiles using the "createDataPartition" function in the caret R package  (Kuhn, 2019).
The variability and the interactions between the predictors were checked to insure the range of values was similar in the MAESPA simulation dataset and in the application dataset used for DynACof. The metamodels are not mandatory in DynACof, and can be replaced by any equation or fixed value. Hence, DynACof was also run using standard equations instead of the metamodels to assess their relative contribution to the modeling performance for the plot-scale net radiation (Rn), latent (LE) and sensible (H) heat flux, and gross primary productivity (GPP). For this purpose, the FAO recommended plot scale equation from  Allen et al. (1998)  was chosen as a reference for comparison with the current computation for Rn in DynACof, LE was computed using the Penman-Monteith equation  (Allen et al. , 1998) , H was computed as the difference between Rn and LE, and GPP was computed using a constant light use efficiency () and constant light extinction coefficients ( and ) for both the coffee and the shade tree layers.
(2011)  from the branch to the whole plant. The model computes the total number of newly formed nodes (  ) on the coffee plant for year N, i. e. the number of green wood nodes that will potentially bear flower buds by the end of the first year of the reproductive cycle: 𝐺𝑁 𝑖 = 𝐿𝐴𝐼 𝑖 • 𝑅𝑁𝐿 • 𝐶𝑁 ( 25 ) where  is the number of nodes per  unit at 20 °C, assuming that this parameter is dependent on the growth temperature. is an empirical temperature-dependant correction coefficient that is a function of the mean temperature during the vegetative growth period (  , °). This relationship is derived from  Drinnan and Menzel (1995) : 𝐶𝑁 = 0. 4194773 + 0. 2631364 • 𝑇 𝑔𝑝 -0. 0226364 • 𝑇 𝑔𝑝 2 + 0. 0005455 • 𝑇 𝑔𝑝 3 (26) Note that this coefficient is from one of the only datasets in the literature that links coffee reproductive and vegetative growth to the ambient temperature.
These conditions reflect the need for a drier period followed by an intense rainfall event for optimal dormancy break  (Rodríguez et al. , 2011). The number of buds breaking dormancy () on a given day is computed as: 𝐵𝑢𝑑𝑏𝑟𝑒𝑎𝑘 𝑖 = 𝐵𝑢𝑑𝑆2 𝑖 • 𝑃𝑏𝑟𝑒𝑎𝑘 𝑖 • 𝐶𝐵 (34) where 2  is the number of stage 2 buds in the cohort that have not yet broken dormancy,  is a temperature-dependant correction coefficient, and   is the rate at which they break dormancy, which is related to the leaf water potential (  , ) using two parameters (  and   ) as follows: 𝑃𝑏𝑟𝑒𝑎𝑘 𝑖 = 1 1 + 𝑒 𝑎 𝑝 +𝑏 𝑝 •𝛹 𝑙𝑒𝑎𝑓,𝑖 (35)  is implemented to include the effect of the average canopy temperature on the number of buds initiated during growth of the coffee plant. It is computed from a monotone Hermite spline  (Fritsch and Carlson, 1980)  fitted on the rather unique  Drinnan and Menzel (1995)  dataset relating temperature and blossoming.
More details are available on the help page of the eponym function of the R package of DynACof (https://vezy. github. io/DynACof/reference/CB. html). When buds do break dormancy, they enter the fruit stage by forming a flower (stage 1 fruit). Then, the fruits develop and finally become mature (stage 4) then overripe (stage 5), when they fall onto the ground. Each bud breaking dormancy on day  forms a new cohort of fruits, meaning that fruits forming a fruit cohort can originate from several different bud cohorts. The stage 1 fruits of the cohort then enter the carbon allocation scheme to undergo the successive stages of maturation. Under optimal conditions, the carbon mass of the fruit increases following a logistic growth over the growing period until it becomes overripe. This logistic growth during physiological development can be modeled as: 𝐿 = (1 + 𝑒 𝐹 𝑑𝑑 𝑖𝑛𝑓 -𝑆 𝑑𝑑𝑓𝑟𝑢𝑖𝑡 𝑠 ) -1 (36) where    is the inflexion value of the logistic growth (in degree days),  is the steepness of the logistic growth, and   is the number of degree days accumulated by the fruit since its flowering date.
Soil, water, and energy The soil water balance module was inspired by the BILJOU model  (Granier et al. , 1999) , which has already been parameterized for this coffee agroforestry system  (Gómez-Delgado et al. , 2011)  and is given in Appendix A. It has three layers, 0 to 1. 25 m, 1. 25 to 1. 75 m and 1. 75 to 3. 75 m, respectively, to cover the whole root profile of the coffee plants  (Defrenet et al. , 2016). The net radiation of the soil was computed using a MAESPA metamodel, and the partitioning between latent and sensible fluxes was parameterized using the average partitioning from the outputs of the MAESPA simulations  (Vezy et al. , 2018). The shade tree and coffee transpiration () and sensible heat () were simulated using MAESPA metamodels. The net radiation of the shade tree and the coffee layers was computed as the sum of the latent () and the sensible () heat fluxes of each layer.
Inputs and outputs All parameters needed for a DynACof simulation are stored in specific input files for the shade tree, the coffee, the soil, the site and the meteorology. A set of example data is included into the package so any user can start a simulation without any data. This set of example file is also archived as a git repository on github. com The model has 245 output variables, mainly for energy balance, water balance, carbon assimilation and allocation, respiration, plant growth, yield and mortality for each organ type. The full list is available from the documentation (https://vezy. github. io/DynACof/reference/DynACof. html).
The metamodels were built using linear regressions with MAESPA input variables as predictors. The MAESPA dataset created from the simulations of the year 2011 was taken as a representative sample of most of the conditions of the growing cycle, with yearly climate variations, a highly variable shade tree LAI due to almost total leaf fall, and highly variable coffee plant structure, with resprouts ranging from 0 to 5 years old. DynACof was run from January 1979 to the end of December 2016 at a daily time step. The climate inputs to the model came from the Coffee-Flux project between 2009 and 2016, and were computed between 1979 and 2008 using the method and data described in  Hidalgo et al. (2017). The values and sources of the parameters used in DynACof are listed in Table  1  for climate and coffee, in Table  2  for the shade tree species, and in Table  3  for the soil.
This was not the case in our study because shade trees structural data were measured only once for the year 2011, so only one value was given as input to MAESPA. The metamodels for  yielded R 2 and nRMSE similar to those found in  Christina et al. (2016) : R 2 of 0. 87 compared to 0. 87 and 0. 94 for shade tree and coffee respectively in our study, and an RMSE of 0. 20 gC MJ -1 compared to 0. 24 and 0. 10 (shade tree and coffee resp. ) in our study. Yet, despite being generally effective at reproducing a complex model output, applying metamodels to new conditions requires caution, as they can produce unexpected results outside their training values, especially if they use non-linear equations or when there is covariance between predictors. This was of particular concern in the present study because metamodels were fitted using a one year-long simulation of MAESPA only (2011).
(2004 ), van der Vossen et al. (2015)  and of the average yield in Central America  (Söndahl et al. , 2005). Furthermore, the Aquiares farm reported average yields of green beans of around 1333 (±336) kg ha -1 year -1 between 1995 and 2014 for fields close to the experimental plot, confirming that DynACof yield predictions were consistent. However, this comparison is only indicative and should be interpreted with caution because the conversion from whole fruit dry mass into processed green beans was made using a simple parameter (FtS, see Table  1 ) that may change depending on several factors, and because DynACof only simulates potential yield in the absence of fruit diseases or predators. Furthermore, the dataset used to fit the effect of temperature on the buds could overestimate the negative effect because  Drinnan and Menzel (1995)  used coffee plants grown in relatively small pots (10 L), which can restrict root growth and then negatively affect the physiology of the coffee plant  (Ronchi et al.
(2013). Charbonnier et al. (2017)  reported that the higher LUE simulated by MAESPA for coffee plants under higher shade could compensate for most of the reduction in incident PAR, maintaining NPP at a nearly constant level as long as shade remains at low values. In DynACof, GPP was reduced only by 2. 6% for a reduction of 7. 9% in APAR, thanks to an increase of 5. 8% in the LUE between the two periods of shade tree management, i. e. from low LAI in pruned shade trees to higher LAI in free-growing shade trees. Another strength of the model is the prediction of canopy temperature under shade as a driver for plant biology, and water and energy balances, thanks to a full soil module inspired from the BILJOU model and to the MAESPA metamodels. Indeed, predictions of the cumulated AET and net radiation were very close to the nearly continuous measurements made from 2009 to 2015.
They also can be replaced by any plot-scale equations because they are written as input code. The model can then be calibrated and evaluated using field data. LAI for both shade tree and coffee is a key variable to measure because it is the result, and has an impact on many processes. The biomass increment of each organ can be measured to evaluate the plant NPP. For the coffee, key variables from the reproductive development are useful to evaluate the simulated yield, e. g. time of initiation and number of buds and flowers, and of course yield itself. The soil water content can be measured to evaluate the water balance module, and the leaf and soil temperature for the energy balance module. DynACof can also be modified easily by developers because it was designed as modular as possible, with each module called in sequence at daily time-step.
Conclusion DynACof (Dynamic Agroforestry Coffee Crop Model) was developed to simulate the effects of the environment, the soil, the species of shade tree and the management practices on coffee growth and yield. The shade management module can be set to any shade type and density, under full sun or agroforestry systems, applying pruning or thinning at any age, if required. The model can be used for full rotations at a daily time step for any surface area, from plot to landscape, or even to a region if properly distributed, under current, past or future climate conditions as long as the metamodels, built from MAESPA model simulations, are updated to the target conditions. The model was parameterized and evaluated using a comprehensive and unique dataset for energy and water balance, biomass and NPP from an experimental site in the Aquiares farm in Costa Rica.
A substantial advantage of DynACof being a tree-average plot model is the possibility to parameterize it using plot averages or totals, which are more frequently available from farms (e. g. yield, pruning intensity, coffee quality, etc. ) because data remain scarce, especially under agroforestry management. Two other important features of the model are the simulation of the canopy temperature (instead of air temperature) to control the plant growth according to the shade level, and the use of cohorts of flowers and fruits to consider grouped flowering in sub-tropical conditions and distributed flowering in equatorial climates. The model is implemented as an R and Julia packages for easy sharing and collaboration, and can be easily modified by adding new modules to compute pest attacks, nutrient cycling, soil organic matter decomposition or soil respiration.
The methodology can be further generalized for any type of shade or climate by using different MAESPA simulation sets to train the metamodels. DynACof was built using a set of generic modules (e. g. functions for aerodynamic conductance) that can be used in other models to simulate any type of agroforestry systems or intercropped systems. In conclusion, DynACof concentrates considerable ecophysiological knowledge on coffee, and is an efficient tool to evaluate and optimize coffee crop yield, ecosystem services and their trade-offs in response to climate conditions and management scenarios. It was also designed to predict the impacts of climate change on coffee yield and the potential of changes in management to mitigate such effects. Alexis Perez, Guillermo Ramirez, Rafael Acuna, Manuel Jara, Alonso Barquero for their technical and field support.
https://github. com/VEZY/DynACof_inputs) for convenience. The main input values used for this study are listed on the paragraph 3. 5. The input variables for the meteorology file should provide at least the maximum and minimum air temperature of the day (°C), the RAD or PAR (MJ m -2 d -1 ), and the relative humidity (%) or vapor pressure deficit (hPa). The full list of mandatory and optional inputs the user can provide are available from the documentation (https://vezy. github. io/DynACof/reference/Meteorology. html).
( https://github. com/VEZY/DynACof_inputs) for convenience. The main input values used for this study are listed on the paragraph 3. 5. The input variables for the meteorology file should provide at least the maximum and minimum air temperature of the day (°C), the RAD or PAR (MJ m -2 d -1 ), and the relative humidity (%) or vapor pressure deficit (hPa). The full list of mandatory and optional inputs the user can provide are available from the documentation (https://vezy. github. io/DynACof/reference/Meteorology. html).
Results on synthetic data In this section, we justify the choice of algorithm parameters, then evaluate robustness to noise, and finally compare our approach to a few other competitive methods. We consider an artificial dataset (Dataset A) which consists of a density map of the 20S proteasome corrupted by additive white Gaussian noise and by applying an artificial MW process, which amounts to setting to zero the Fourier coefficients within an artificial wedge shaped mask. Given the ground-truth x , we use two similarity measures for quantitatively evaluating the restoration results x : the peak signal-to-noise ratio (PSNR) and the constrained correlation coefficient (CCC), defined as = x x x s x s x s PSNR , max ( ) ( ( ) ( )) , s S n s S 1 2 (22) E. Moebel and C. Kervrann Journal of Structural Biology: X 4 (2020)  100013 F F F F = x x x x x x k k k k CCC , [ ] [ ] [ ] [ ].
, 2013)  From the results on dataset A (see Fig. 8 (a)), it can be seen how well our method works in the absence of noise ( = 0 n ): a quasi perfect image recovery has been achieved, despite the complexity of the object. Increasing the amount of noise deteriorates the performance, but as it can be observed for = 0. 2 n , the result is still satisfying. For high amounts of noise ( = 0. 4 n ), the object contrast is still greatly enhanced but the MW artifacts could not be completely removed. In Fourier domain (Fig. 8(b )), the MW has been filled up completely when = 0, n whereas for an increasing amount of noise the MW reconstruction is increasingly restrained to the low frequencies. This is because high frequency components of a signal are more affected by noise, which makes them more difficult to recover.
In Fig. 8 (c), the evolution of the PSNR over time shows that the method converges to a stable solution. In Fig. 8 (d), we compare our MH method to the method proposed by  Maggioni et al. (2013). Both methods produce visually identical results in spatial domain, as well as in the spectral domain, as confirmed by the final PSNR values. However, the difference lies in the convergence speed: our method takes about half as long as the original method  (Maggioni et al. , 2013). Even though the synthetic dataset A is a simplified case of data corruption in cryo-ET, it gives a good intuition of the performance of our method.
Comparison to other MW restoration methods We compare our results to those produced by three competing methods (see Fig. 9 ): sMAPEM  (Paavolainen et al. , 2014) , BFLY  (Kováčik et al. , 2014) , and a TV method with spectral constraints  (Moisan, 2001) , each adopting a different strategy to reduce MW artifacts. We implemented the Moisan's method as follows: Our implementation of the  Moisan (2001) 's method: Set an initial state (0). For = … t T 1, , do Fig. 3. Dataset A (2D): comparison of denoising algorithms. First, we compare conventional denoisers (left column) to our MH method (right column). Second, we compare the results obtained by applying three different 2D denoising algorithms: BM3D, NL-Bayes, and ROF denoising. On the bottom, we evaluate the performance in terms of PSNR and CCC values.
It turns out that our MH method performs better than conventional denoisers in all situations. E. Moebel and C. Kervrann Journal of Structural Biology: X 4 (2020)  100013 1. Projection: P = z x ( ) t W t ( ) ( ) 2. Denoising: + z x x min x t ( ) 2 1 end forwhich amounts to alternatively minimizing TV  (Rudin et al. , 1992)  and satisfying the spectral constraints. The sMAPEM algorithm  (Paavolainen et al. , 2014)  is an iterative tomogram reconstruction procedure, designed to reduce MW artifacts and achieve isotropic resolution. In our experiments, we performed the comparison in 2D mainly because sMAPEM is not available in 3D. Our method, BFLY and the TV method operate on tomograms (2D in our case), while sMAPEM operates on projections (1D). These projections were obtained from the dataset A ground-truth, with a tilt-range of -60°-60°and a tilt increment of 2 degrees.
The result of the Moisan's Fig. 4. Dataset A: This figure shows the impact of algorithm parameters. We illustrate these effects in terms of PSNR through iterations. We do not show images because obtained results are visually similar. On the left, the influence of parameter controlling the acceptance rate of the MH sampling, is shown: = × 1. 5 10 5 (blue), = × 10 5 (red), = × 4. 0 10 5 (yellow). Clearly, the choice of affects the convergence speed, however in all cases our method converges to the same result. On the right, the influence of the data fidelity term is presented. We compare the L 1 (24) and L 2 norms (25), the correlation coefficient (  26 ), the PSNR (  22 ), and the mutual information (  27 ). The results are very similar (maximum error of 0. 1 dB between restored images). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.
Experimental datasets Three datasets (B, C and D) have been used to evaluate the performance of the method on experimental data. Dataset B is an experimental sub-tomogram containing a gold particle. Dataset C is an experimental sub-tomogram containing 80S ribosomes attached to a membrane. Dataset D is an experimental sub-tomogram depicting a region of an E. Coli bacteria, and contains unidentified macromolecules next to a membrane. Unlike data-sets B and C, the dataset D is available as single-axis and double-axis data (see Section 1).
Criteria and method for evaluation The evaluation differs depending on the dataset. In dataset B, the  From left to right: the ground truth (reference for PSNR), the corrupted image, the MAP and MMSE estimators computed with our MCMC procedure. As can be observed in the zoomed in regions (the red frames), the MMSE estimator is less noisy than the MAP estimate, with a higher PSNR value. E. Moebel and C. Kervrann Journal of Structural Biology: X 4 (2020)  100013 gold particle is deformed and elongated (ellipse) due to the MW artifacts. Improving the sphericity of the object is thus a good evaluation criterion. For dataset C, we measure the similarity between the central ribosome and a reference (obtained via sub-tomogram averaging). The evaluation criterion is the Fourier shell correlation (FSC), commonly used in cryo-ET  (Van Heel and Schatz, 2005).