dataesr/scientific-paragraphs-categorization huggingface dataset:
Image acquisition system A fully programmable, infrared camera system was built assembling a compact, single-board computer (Raspberry Pi 2 model B) and an infrared camera (Raspberry Pi NoIR V1). The computer was run under Raspbian GNU/ Linux operating system and scripts were developed in Python language to facilitate image capture automation and analysis. The camera was a regular module (Omni-Vision OV5647) where the infrared filter was removed, allowing for the capture of NIR wavelengths in addition to standard VIS light. A blue filter (Roscolux #2007 Storaro Blue) was also set in front of the camera lens to exclude green and red wavelengths and to transmit blue and NIR wavelengths higher than 700 nm (Fig. 1 ). Overall, the light incoming to the camera lens was mainly composed of VIS light filtered for blue and NIR wavelengths which were recorded in the BLUE and RED channels, respectively.
Overall description Software for the automation of image capture, segmentation and analysis was developed in Python 2. 7 (http:// www. python. org). A first python script was developed and uploaded in each Raspberry Pi to control image capture and storage in USB flash disks. Image analysis was performed with another, specifically developed python script based on Numpy (http://www. numpy. org) and OpenCV2 (http:// opencv. org) libraries. This step could be performed in Windows operating systems via an executable programme using py2exe (http://www. py2exe. org). The script can batch process thousands of pictures in a few minutes on a standard personal computer, including storage of final images for rapid control of the procedure and saving the end results (leaf area) directly into a spreadsheet-ready CSV file.
Intensity of pixels was rescaled after NEW CHANNEL computation. Application of Eq. (  1 ) resulted in a narrow range of variations in pixel intensity for this NEW CHAN-NEL (Fig. 2b ). The distribution of pixel intensity in this new channel is bimodal with each mode associated (1) NEW CHANNEL = RED -k × BULE (with k = 0. 5) with either the plant or the background prefiguring the segmentation. A false 2 colour image based on this NEW CHANNEL image can be created at this stage (included in our script but may be skipped) to better visualize the segmentation between plant and background (Fig. 2c ). An adaptive thresholding based on Otsu's binarization  [44]  was then processed on the NEW CHANNEL image (Fig. 2b ). Standard procedures for erosion, opening and closing were then applied in that order to remove noise and closing small holes inside objects using OpenCV library.
Segmentation performance assessment We compared our proposed PYM (raspberry Pi pYthon iMaging) procedure with Rosette Tracker, a state-of-theart, recently published method. Rosette Tracker emerged as the only published method able to estimate plant leaf area, freely available as an ImageJ plugin with minimal parameterization of the analysis software, thereby sharing similar objective as PYM, although working on VIS images  [12]. We therefore retained two different versions of the same camera, either standard for imaging in VIS wavelengths or transformed for (VIS + NIR) BF imaging as above described. Paired pictures were obtained with both cameras from various scenes combining leaves or whole plants of various species over different backgrounds. Automated segmentation of VIS and (VIS + NIR) BF images were performed with their respective method (Rosette Tracker or PYM).
Microclimate in the field experiment A temperature and humidity probe (HMP45 AC; Campbell Scientific Inc. , UK) and a radiation sensor (BF5; Delta-T Devices, UK) connected to a data logger were positioned in the control plot to monitor air temperature and global and diffuse radiations (400-700 nm). Global and diffuse radiations were used to compute radiative balance at the plant level for the different locations under the PVPs by applying a ray tracing algorithm  [46]  to a three-dimensional numerical representation of the whole photovoltaic system. Instantaneous, global incident radiation transmitted at plant level (Radiation inc ) was thus computed every 3 min taking into account actual changes in sun position and orientation of the photovoltaic trackers. Cumulated, global radiation over the whole growing period was then calculated by integrating these instantaneous values.
Image segmentation with contrasted backgrounds Efficiency of the PYM method was first assessed by comparison with Rosette Tracker, a recently published method for estimating plant leaf area from VIS images. Since Rosette Tracker operates with a regular version of the camera while PYM operates with a modified one, able to sense (VIS + NIR) BF wavelengths, paired, VIS and (VIS + NIR) BF images were captured for different plants using both configurations of the camera. Methods were tested with images of one same lettuce plant placed over three contrasted backgrounds (Fig. 3 ) and segmentation was run using scripts developed in Rosette Tracker and PYM respectively. Both segmentation methods correctly estimated the projected plant surface area on a dark background with only a 1% deviation compared to the reference area estimated by manual contour of the plant on original, VIS and (VIS + NIR) BF images (Fig.
Relative Expansion Rate in the field experiment For each plant, relative expansion rate (RER) of individual plants was computed for the 8 periods separating 2 consecutive image captures. A general decline was evidenced for RER when related to the projected leaf area considered as the mean of initial and final values for each time interval (Fig. 9a ). This indicated that RER was largely determined by the plant surface able to intercept light at each time interval, although with a tendency to decline with plant development for all three treatments. This corresponded to a sub exponential growth pattern as already depicted in other rosette species  [6]. Fitting an exponential model to this data revealed a similar behaviour for the Control and Below_PVPs treatments, with very close parameter values, whereas RER was significantly higher for plants grown between PVPs, specifically at early stages of development (Table  1  and Fig.
A second originality holds in the addition of the blue filter to better discriminate between plant and background which remains a major challenge  [53, 54]. Most backgrounds reflect more blue light than do the plants. This specificity was implemented in our software analysis where, for each pixel, BLUE channel intensity was partly subtracted from RED channel one (mainly composed of NIR and IR wavelengths). As a result, a contrast could be found between plants and background in a large range of conditions using a unique script with stationary parameters for all image analyses. The value of "k" used in this manuscript to weigh BLUE values relative to RED ones was empirically optimized as a first approach, showing good correlation between manually and automatically estimation of plant leaf area.
Although not presented here, the procedure should be easily extrapolated to side-view images. Finally, Raspberry Pi computers offer enough flexibility to fit scripts to multiple applications. For example, the system can be used for remote sensing of plant growth even during the night, using infrared LEDs programmed to light up during image capture. To save memory during image capture, the script developed for image analysis could be uploaded in the Raspberry Pi so that leaf area only could be stored. Segmented images could also be easily controlled by adding a portable display. The PYM method could thereby be appropriate for very large applications or drone assisted ones were the weight of the whole device should be minimized. However, high storage capacity devices are now available at low prices and low weight and are more flexible for further analysis.
Acknowledgements This research was supported by the  ANRT (Association Nationale Recherche Technologie, France)  with the support of Sun'R (Contract  2013/1353 ). This project was also founded by  Région PACA ,  CAPI ,  BPI   FRANCE, Communauté du pays d' Aix ,  Région Rhônes-Alpes  and  Grand Lyon  in the SunAgri2B FUI project. We thank  Rijk Zwaan  breeding company for providing plant seeds. We would also like to thank  Vincent Oury  for helpful general discussions about imaging methods.
Computational Studies Apparent pK a were calculated by using ACD/Percepta software  [48]. The compounds were built, taking into account the prevalent ionic forms at physiological pH (7. 4), using the Insight 2005 Builder module (Accelrys Software Inc. , San Diego, CA, USA). Atomic potentials and charges were assigned using the CFF91 force field  [49]. The conformational space of the compounds was sampled through 200 cycles of simulated annealing (ε = 80*r). The following protocol was applied: The system was heated up to 1000 K over 2000 fs (time step = 3. 0 fs). The temperature of 1000 K was applied to the system for 2000 fs (time step = 3. 0 fs) with the aim of surmounting torsional barriers. Successively, the temperature was linearly reduced to 300 K in 1000 fs (time step = 1. 0 fs). The resulting conformations were then subjected to molecular mechanic (MM) energy minimization within an Insight 2005 Discover module (Accelrys Software Inc.
, San Diego, CA, USA) (CFF91 force field; ε = 80*r) until the maximum RMS derivative was less than 0. 001 kcal/Å, using Conjugate Gradient  [50]  as the minimization algorithm. MM conformers were then subjected to a full geometry optimization by semi-empirical calculations, using the quantum mechanical method PM7  [51]  in the Mopac2012 package  [52]  and EF (Eigenvector Following routine)  [53]  as the geometry optimization algorithm. The GNORM value was set to 0. 01. To reach a full geometry optimization, the criteria for terminating all optimizations were increased by a factor of 100, using the keyword PRECISE. Resulting conformers were ranked by their potential energy values (i. e. , ∆E from the global energy minimum) and grouped into conformational families. The occurrence rates and ∆E GM range for each conformational family were calculated.
The lowest energy minima of each family were then subjected to DFT calculations. All structures were fully optimized at the B3LYP/6-31 + G(d,p) level  [54, 55]  using the conductor-like polarizable continuum model (C-PCM)  [46]. The SCRF/CPCM method allows the calculation of the energy in the presence of a solvent. In this case, all structures were optimized as a solute in an aqueous solution. In order to characterize every structure as minimum and to calculate the Gibbs free energies of reaction, a vibrational analysis was carried out at the same level of theory, using the keyword freq. The RMS force criterion was set to 3 × 10 -4 a. u. The calculations were carried out using the Gaussian 09 package  [56]. Starting from the structure of the Q (i. e. , the starting quinone species) GM conformers, the redox states Q •-, QH • i , QH • ii, QH i -, QH ii -, QH 2 were generated.
Molecular docking procedure Molecular docking is valuable method to study the interaction of protein and ligand and also, is one of the original tools in computational design of drugs. In this work, we docked the complexes with BLG as an important carrier protein. The 3D structures of the metal complexes were obtained using the CIF files of their X-ray crystal structures  (Khorshidifard et al. , 2015). The CIF files were converted to the PDB format by using the Mercury software (http://www. ccdc. cam. ac. uk/). The crystal structure of BLG (PDB ID:1CJ5) was taken from the Brookhaven Protein Data Bank (http://www. rcsb. org/pdb). The resolution of this file was2. 28 Å. Water molecules of the. pdb fileswere deleted, missing hydrogen atoms and Gasteiger charges were added. Flexible-ligand docking was carried out by AutoDock 4.
2. 5. 1 molecular-docking program using the implemented empirical free energy function and the Lamarckian Genetic Algorithm. The Gasteiger charges were added to prepare the macromolecule input file for docking and the Auto Grid was used to calculate Grid. For the docking of metal complexes with BLG, a blind docking with 126 lattice points along X, Y, and Z axes was performed to find the binding site of complexes on BLG with a grid point spacing of 0. 375 Å, to allow the complex to rotate freely. In the next step, the centre of the grid box was located at the binding site and the second docking was performed using a cubic box with 60×60×60 dimensions. 250docking runs with 25,000,000 energy evaluations for each run were performed.
Materials and methods 126 2. 1. Experimental data 127 Detailed quality-assessed data from the USDA 'Hot Serial Cereal' (HSC) experiment  (Grant et al. , 128 2011; Kimball et al. , 2015; Ottman et al. , 2012; Wall et al. , 2011)  and from the 'International Heat Stress 129 Genotype Experiment' (IHSGE) coordinated by CIMMYT  (Reynolds et al. , 1994b)  were used. Both 130 experiments were well watered and fertilized to avoid drought and nutritional stress to assure that 131 temperature would be the main environmental variable. Daily global solar radiation, maximum and 132 minimum air temperature, average wind speed, dew point temperature and precipitation were recorded at 133 weather stations near the experimental plots. The mean daily average air temperature for the growing 134 season (sowing to physiological maturity) was calculated from minimum and maximum daily air 135 temperatures as described in  Asseng et al.
(2015)  and reported in Supplementary Information S2. In both 136 experiments phenological development measurements included: emergence date (Zadock scale 10), 137 anthesis date (Zadock scale 65), and maturity date (Zadock scale 89). From these measurements the 138 number of days from sowing to anthesis (days), from anthesis to maturity (days), and from sowing to 139 maturity (days) were calculated. In both experiments, the plots were kept weed-free, and plant protection 140 methods were used as necessary to minimize damage from pest and diseases. The two data sets are further 141 described in  Asseng et al. (2015). Following is a brief description with focus on the measurement data that 142 were available for this study. 143 The HSC experiment was conducted at Maricopa, AZ, USA (33. 07° N, 111.
End-of-season (i. e. ripeness-maturity) measurements were total above ground dry 152 biomass (t DM ha -1 ), grain yield (t DM ha -1 ), single grain dry mass (mg DM grain -1 ), and grain number 153 (grain m -2 ). Biomass harvest index was calculated as HI = 100 × (grain yield)/(above ground biomass) 154  (%). 155 Data from the IHSGE experiments used in this study includes two spring wheat cultivars (Bacanora 88 156 and Nesser) grown during the  1990-1991 and 1991-1992  winter cropping cycles at hot, irrigated, and low 157 latitude sites in Mexico  (Ciudad Obregon, 27. 34° N, 109. 92° W, 38 m a. s. l. ; and Tlatizapan, 19. 69° N, 158  99. 13° W, 940 m a. s. l. ), Egypt (Aswan, 24. 1° N, 32. 9° E, 200 m a. s. l. ), India (Dharwar, 15. 49° N, 74. 98° 159 E, 940 m a. s. l. ), Sudan  (Wad Medani, 14.
Qualitative information on vernalization requirements and day length 179 response for each cultivar were also provided. 180 The HSC experiment (calibration data set) was used to improve the models. All available measurements 181 from the HSC experiment were provided to modelers to improve and refine the parameterization and 182 processes of their model. The objective was to improve wheat models for the simulation of the impact of 183 high temperature and heat stresses on crop development and growth. Modelling groups were allowed to 184 decide how to improve and implement heat stress impact in their models. 185 The IHSGE experiment (evaluation data set) was used as independent evaluation data set to test single 186 models and model ensemble performances before and after improvement. All measurements of the 187 evaluation data set were withheld from modelers (blind test) with the exception of phenology for all 188 treatments and grain yield for one of the treatments (one year at Ciudad Obregon, Mexico) which was 189 used to calibrate genotypic  coefficients.
Evaluation of model improvement effects on single models and on multi-model ensemble accuracy 196 We evaluated the effect of model improvement on two different performance characteristics, accuracy 197 and uncertainty, and on three model entities: (i) single models (accuracy only); (ii) multi-model ensemble 198 (MME, the ensemble of 15 models in this experiment exercise); and (iii) MME median (e-median). 199 Accuracy was measured using the mean squared error (MSE), the root mean squared error (RMSE), 200 and the root mean squared relative error (RMSRE). 201 For measuring single model error in reproducing the calibration and the evaluation data set we 202 concentrated on the root mean squared relative error (RMSRE). This error indicator has the advantage of 203 giving more equal weight to each measurement, and it's meaningful when comparing very different 204 environments likely to give a broad range of responses  (Martre et al.
Evaluation of model improvement effects on MME prediction uncertainty 218 To assess the MME prediction uncertainty we considered both the variability in MME and the 219 comparison with hindcast (i. e. retrospective forecasts using known inputs and known field measurements) 220  (Wallach et al. , 2015)  using the two available measurement data sets. In order to evaluate the prediction 221 uncertainty of the MME before and after improvement we used the HSC calibration-data set to simulate 222 model hindcast in respect to observed data, and the IHSGE experiment as the "unknown" data set used to 223 simulate model prediction to unknown data and to evaluate the predictive skills of the models in the 224 ensemble. As a measure of uncertainty we used the mean squared error of prediction (MSEP) and its 225 decomposition in prediction squared bias (.
(2015)  as a sufficient number of samples for 27 models) of M' models with 245 replacement, so the same model might be represented more than once in a sample. M N Bi M M i Bi N            (7) where, 13. 5%, previously established through a meta-analysis of field trials  (Taylor et al. , 1999)   is the e-255 median estimate in bootstrap sample b of the ith treatment. 256 All calculations and graphs were made using the R statistical software R 3. 1. 3 (R Core Team, 2013) and 257 the development environment RStudio  (RStudio Team, 2015). Bootstrap sampling used the R function 258 sample. 259.
The cardinal temperatures 271 for these processes were fixed using values reported in the literature or calibrated using the HSC 272 experimental data set. One model (APSIM-Nwheat) added a canopy temperature sub-routine. In addition 273 to the inclusion/modification of heat stress impacts on physiological processes, five models improved 274 processes not directly related to heat stress using the HSC data set or other published data sets (Table  1 ). 275 One model (GLAM-wheat) removed the sub-routine for heat stress effect on grain set and potential 276 harvest index as they observed no substantial improvement and decided not to increase the complexity of 277 their model (Table  1  and Supplementary Methods)   (Chen et al. , 2010; Keating et al. , 2003; Wang et al. , 2002; Zhao et al. , 2015)  Introduction of a nonlinear temperature response function for phenological development and biomass growth.
Effects of model improvement on single models accuracy 297 Figure  2  illustrates the effects of model improvement on the simulations of three treatments of the HSC 298 calibration data set whose mean growing seasonal temperatures were different. In most cases, measured 299 in-season and end-of-season LAI, above ground biomass, and grain yield were in the range of model 300 simulations for both the un-improved and the improved models. Nevertheless, the improved models 301 showed a lower level of variation (measured through the 10th to 90th percentile range of the 15 model 302 simulations). For grain yield and above ground biomass, the improved MME was more precise at high 303 temperatures than the unimproved MME (mean growing season temperature of 22°C and 27°C in Fig. 2 ). 304 Most unimproved and improved models underestimated the impact of high temperature on LAI, but this 305 was true to a lower extent for the improved compared to the un-improved models.
Comparison of RMSRE distribution of the 15 unimproved and improved models 315 for the calibration data set showed a reduction in the median values for RMSRE of most of the variables: 316 53% for days from sowing to maturity, 36% for above ground biomass, 31% for grain yield, 18% for HI, 317 32% for grain number, 12. 4% for single grain dry mass. However, RMSRE range for HI, grain number, 318 and single grain dry mass remained almost unchanged. 319 Figure  4  shows the effect of model improvement on the accuracy (as measured by RMSRE) of each 320 model for grain yield and for the key variables leading to final yield for the calibration data set. In general, 321 models were improved for almost all measured variables. As expected, models that had large errors for a 322 specific variable were the ones that improved the most for that variable.
5 ). For the calibration dataset the reduction of the variability 334 between models and their convergence is an expected result as all the teams used the same dataset to 335 improve and recalibrate their model. For grain yield, an increase in precision was observed for 336 temperature > 24°C for both the calibration and the evaluation data set: grain yield variation decreased by 337 4% and 21% considering the whole temperature range of the calibration and the evaluation data sets, 338 respectively, and by 39% and 26% considering only mean seasonal temperatures >24°C. For the 339 evaluation data set, consistent reduction of the range of variation among models was also observed for HI 340 (20%), grain number (71%), and single grain dry mass (44%) (Fig. 5 ). 341.
Analysis of the prediction skills of the model population showed that the 350 level of prediction error (MSEP) when simulating the "unknown" data set was reduced after improvement 351 by 47% (Fig. 6 ). As the MSEP is the sum of the squared bias for the calibration data set and the variance 352 for the evaluation data set (Eq. 6), changes in bias and variance of MSEP followed the same reduction 353 patterns. 354.
Effect of individual model improvement on MME e-median skill 355 The RMSRE of e-median was reduced by 38% for grain yield and by 46% for above ground biomass, 356 in the calibration data set, and by 2% for grain yield and 11% for above ground biomass in the evaluation 357 data set (Fig. 3 ). The relationship between the number of models in an ensemble and the CV and RMSRE 358 of e-median estimation of grain yield and above ground biomass was analyzed through a bootstrap 359 approach to create a large number of random ensembles of 1 to 15 models. Independently of the number 360 of models in the ensembles, for the evaluation data set the CV of e-median was about 41% lower for 361 improved models compared with unimproved models (Fig. 7, panel A  and B ). 362 Therefore, model improvement decreased variation of e-median in a range between 15% for M' = 1 and 363 7% for M' = 15 for above ground biomass and between 14% at M' = 1, and 9% for M' = 15 for grain 364 yield.
g. from trapezoidal to non-linear). 413 Regarding cardinal temperatures used for describing the temperature response of phenological 414 development and biomass growth (i. e. the minimum, optimum, and maximum temperatures), there was no 415 clear accordance among models, with the exception of the optimum temperature for radiation use 416 efficiency (~20°C) and the minimum temperature for both phenological development and biomass growth 417 (~0°C)  (Wang et al. , unpublished). Some models calibrated the optimum and the maximum temperatures 418 using the calibration data set and the best matching values obtained through calibration might have been 419 influenced by the specificities of each model  (Eitzinger et al. , 2012). 420 Three models added a sub-routine for accounting for heat stress impact on grain number and/or size.