dataesr/scientific-paragraphs-categorization huggingface dataset:
The elapsed time for these inplace versions and for an equivalent Fortran implementation are displayed in the bottom axis of Fig. 9. The ranking is the same as for the outplace versions and Pythran is also the faster solution. However, Numpy is even more slower (7. 8 times slower than Pythran with the explicit loops) than for the outplace versions. From this short and simple microbenchmark, we can infer four main points: • Memory allocation takes time! In Python, memory management is automatic and we tend to forget it. An important rule to write efficient code is to reuse the buffers already allocated as much as possible. • Even for this very simple case quite favorable for Numpy (no indexing or slicing), Numpy is three to eight time slower than the Fortran implementations. As long as the execution time is small or that the function represents a small part of the total execution time, this is not an issue.
However, in other cases, Python-Numpy users need to consider other solutions. • Pythran is able to speedup the Numpy code without explicit loops and is as fast as Fortran (even slightly faster in our case) for the codes with explicit loops. • Numba is unable to speedup the Numpy code. It gives very interesting performance for the version with explicit loops without any type annotation but the result is significantly slower than with Pythran and Fortran.
Quality control The package fluidfft currently supplies unit tests covering 93% of its code. These unit tests are run regularly through continuous integration on Travis CI with the most recent releases of fluidfft's dependencies and on Bitbucket Pipelines inside a static Docker container. The tests are run using standard Python interpreter with all supported versions. For fluidfft, the code coverage results are displayed at Codecov. Using third-party packages coverage and tox, it is straightforward to bootstrap the installation with dependencies, test with multiple Python versions and combine the code coverage report, ready for upload. It is also possible to run similar isolated tests using tox or coverage analysis using coverage in a local machine. Up-to-date build status and coverage status are displayed on the landing page of the Bitbucket repository.
We apply a machine learning algorithm to the output from seven fire-enabled DGVMs and a suite of satellite and other observation-based data sets in order to derive emergent relationships between a number of potential drivers of burned area. By comparing the model-and data-derived emergent relationships, we assess the degree to which DGVMs reproduce these relationships. While we make no assumption about the actual physical controls on burned area, this comparison allows us to pinpoint relationships between drivers and burned area that are unrealistically represented in fireenabled DGVMs. 2 Data and methods.
, 2012). We built three sets of RF models. We first built RF models for satellite-observed burned area based on a multitude of predictor variables to derive relationships from data. There are differences between the available burned area data sets; therefore, we used five recent and/or well-established data sets to encompass these uncertainties. The fire-enabled DGVMs do not use some of the predictor variables in the satellite-derived RF. Hence, we built a second set of satellite-derived RF models with a reduced set of predictor variables. The third set of RF models was derived for each FireMIP model using the simulated burned area as the target variable and simulations of gross primary production (averaged over precedent months), biomass and land cover predictor variables, and the population density and climate predictor variables that were used as inputs for the models (according to the FireMIP protocol).
GFED4s GFED4s 0. 25 tions (thermal anomaly) and reflectance changes from the same sensor (MODIS). As an exception, CCI_MERIS uses MERIS reflectances combined with MODIS active fires. We restricted our analysis to burned area data with high observational quality. Observational quality indicates the degree to which missing input satellite imagery or contamina-tion from clouds, smoke, snow, and shadows limit burned area detection. Especially MERIS land observations are subject to substantial gaps in raw data acquisitions  (Tum et al. , 2016). Low observational coverage can result in a strongly underestimated burned area. Here, we used the CCI_MERIS "observed area fraction" layer as a time-variant mask to all burned area data sets and only included estimates for months with observational coverage higher than 80 %.
e. wind speed (WSPEED), or use population density (PopDens) for fire ignitions and/or fire suppression. The model outputs were aggregated to a common spatial resolution of 1. 89 • latitude×2. 5 • longitude. Aggregation was undertaken by averaging the fractional burned area from all high-resolution grid cells that belong to the same coarseresolution grid cell. Nearest neighbour resampling was carried out if less than two high-resolution grid cells were within one coarse-resolution grid cell. Analyses were also undertaken for the same period as the common window of the satellite data  (2005) (2006) (2007) (2008) (2009) (2010) (2011)  and by applying the "observed area mask" from the satellite data.
Evaluation of data-data and model-data temporal agreement We evaluated the temporal agreement of monthly burned area time series for 2005-2011 between the data sets and between the data sets and the fire-enabled DGVMs based on various model performance metrics  (Janssen and Heuberger, 1995)  on a per-grid cell basis. We selected the Spearman rank correlation coefficient to compare the temporal agreement and the fractional variance (FV) to compare the variability of burned area per grid cells: FV = σ x -σ ref 0. 5 × (σ x + σ ref ) , (1) where σ ref and σ x are the variances of the reference and observed or simulated burned area, respectively. FV ranges be- In the case where a single satellite data set (e. g. x = BA. CCI_MERIS) was compared with the other satellite data sets, this data set was not used in the reference vector.
This approach directly considers the differences between data sets in the computation of model performance metrics and implies that it is impossible for a FireMIP model or for one single satellite data set to reach an optimal correlation of unity or a FV of zero as long as the satellite burned area data sets show differences. We used the median of the correlation coefficient and of the FV for each grid cell to quantify the data-data or model-data agreement over the ensemble of data sets or models. As a single global agreement metric, we computed the percentage of the land area that showed "good" agreement from the spatial patterns of the Spearman correlation Cor and FV: good agreement for an individual grid cell was defined based on a positive and nonrandom relationship (i. e. Cor ≥ 0. 25) and a comparable variance (-0.
Climate data Climate data were taken from the CRUNCEP V5 data set  (Wei et al. , 2014). CRUNCEP provides 6-hourly time series of precipitation, maximum and minimum temperature, and wind speed. From these time series, we derived the monthly mean of daily maximum temperature (CRUNCEP. TMAX) and minimum temperature (CRUNCEP. TMIN), the monthly mean daily diurnal temperature range (CRUNCEP. DTR = TMAX -TMIN), the monthly 90th percentile of daily wind speed (CRUNCEP. WSPEED), the monthly total precipitation (CRUNCEP. P), and the number of wet days per month (CRUNCEP. WET). A wet day was defined as a day with ≥ 0. 1 mm precipitation  (Harris et al. , 2014).
Vegetation productivity Data on gross primary production (GPP) and leaf area index (LAI) were taken to account for the seasonal effects of vegetation productivity and canopy development. GPP was taken from the FLUXCOM data set which is up-scaled from GPP estimates at FLUXNET measurement sites  (Tramontana et al. , 2016). We used the FLUXCOM data set that used satellite and CRUNCEP meteorological data for the upscaling. LAI was taken from MODIS  (Myneni et al. , 2015). GPP and LAI were averaged to monthly mean values (e. g. variable name GPP. orig). To account for seasonal fuel accumulation, we also computed previous-season GPP or LAI values as the mean over the 3 and 6 months before the month of comparison with burned area (e. g. GPP. pre3mon and GPP. pre6mon).
From each FireMIP model, we used the simulated vegetation carbon averaged for the years from 2005 to 2011 as the equivalent to this data set. We used canopy height from  Simard et al. (2011) ; this data set provides a snapshot of the average canopy height in 2005. Factors related to fuel properties, specifically grass height, litter depth, woody litter depth, and the amount of woody litter in different size classes were extracted from the global fuel bed database  (Pettinari and Chuvieco, 2016). This database is based on a land coverbased extrapolation of regional fuel databases for the globe and provides a generic picture of the conditions around 2005.
Socio-economic data We used the annually varying population density data set from the HYDE V3. 1 database (Klein  Goldewijk et al. , 2011) , which was utilized as a forcing data set for the FireMIP simulations. We also used annually varying gross domestic product per capita (GDP; World Bank, 2018), a static satellite-derived index of socio-economic development based on night-time lights for the year 2006  (Elvidge et al. , 2012) , and a data set on cattle density for the year 2007  (Wint and Robinson, 2007).
S1  in the Supplement). The purpose of these experiments was to identify the relationship between burned area and each predictor variable from data sets. -"RF. Satellite. fm" for satellite-derived RF experiments: these experiments were also trained against burned area data sets but included only the reduced set of 16 databased predictor variables that are available from both observational data sets and the FireMIP (fm) models. -"RF. FireMIP. fm" for model-derived RF experiments: these experiments used the reduced set of predictor variables with land cover, GPP, biomass, and the response variable burned area taken from simulations of each FireMIP model. The purpose of these experiments was to compare relationships and sensitivities from satelliteand FireMIP-derived RF experiments.
S2 ). The average of all ICE curves corresponds to the PDP. We used the ICEbox package V1. 1. 2 for R for the computation of ICE curves and partial dependencies  (Goldstein et al. , 2013). We computed ICE curves for all predictor variables and from all RF experiments (Supplement Sects. S4 and S5). We computed ICE curves and PDPs based on the global data set to analyse and compare global emergent relationships. The Pearson correlation coefficient was computed between pairs of satellite-and model-derived ICE curves to quantify the agreement of the emergent relationships (Fig. S15 ). We also computed PDPs for each grid cell to produce global maps of partial sensitivities for selected predictor variables. To summarize and map the PDP of each grid cell in a single number, we fitted a linear quantile regression to the median between the partial dependence of burned area and the corresponding predictor variable and mapped the slope of this regression.
Evaluation of temporal burned area dynamics Here we compare the monthly temporal dynamics of burned area from the satellite data sets, FireMIP model simulations, and random forest predictions for the overlapping period from 2005 to 2011. The satellite data sets showed relatively good agreement with each other on average (i. e. "good" is r ≥ 0. 25 and -0. 7 ≤ FV ≤ 0. 7) over 70 % of the global vegetated land area, with the best agreement found in frequently burning grasslands and savannahs (Fig. 2a ). However, individual data sets only showed good agreement over 31 %-56 % of the land area (Fig. S3 ). The largest dissimilarities between burned area data sets occurred in temperate regions with a high land use intensity (e. g. North America, Europe, China), tropical forests, and in sparsely vegetated arid and tundra regions.
Climate The satellite-derived global relationships showed expected patterns between burned area and climate variables: burned area increased exponentially with maximum temperature, decreased with an increasing number of wet days per month, and increased with diurnal temperature range (Fig. 4a-c ). The shapes of the relationships of burned area to climate vari- ables were robust among the burned area data sets (Fig. S11 ). However, burned area data sets show offsets between the relationship curves: for example, the curves that were derived from the GFED4s and CCI_MERIS data sets usually show higher burned area than the curves from the other data sets (Fig. S11 ). These positive offsets are caused by the fact that GFED4s and CCI_MERIS include more small fires and therefore have an overall higher burned area than the other data sets.
Because we use the same climate forcing, and vegetation state variables derived from each model in our machine learning approach, we are able to evaluate the response of fire models independent of their underlying DGVMs. This allows us to derive (as partial dependencies or individual conditional expectations) and evaluate the relationships between predictors and the response for each fire module separately. Hence, we are able to attribute deficiencies in fire-enabled DGVMs to human-and productivity-influences on fire. Previously, a similar approach also used a tree-based machine learning algorithm to evaluate drivers of soil carbon stocks in observational databases and in Earth system models  (Hashimoto et al. , 2017). Unlike classical model benchmarking, such pattern-oriented model evaluation approaches help to diagnose the reasons for model-data mismatches.
, 2016) , and fire radiative energy  (Kaiser et al. , 2012). In summary, besides human-fire interactions, we identified vegetation effects on fire as a main deficiency of fire-enabled dynamic global vegetation models in simulating temporal dynamics of burned area. Code availability. This analysis is based on R (version 3. 3. 2) using the randomForest  (version 4. 6-12)  and ICEbox (version 1. 1. 2) packages. R and previously mentioned packages are available from the Comprehensive R Archive Network (CRAN, https://CRAN. R-project. org/package=randomForest (Breiman and Cutler, 2018) ; ICEbox, https://CRAN. R-project. org/package=ICEbox (Goldstein et al. , 2017) , last access: 9 January 2019). Appendix A.
Acknowledgements. We thank the institutions, initiatives, and researchers listed in Table A1 for providing data sets. We thank  Stephane Mangeon  for providing JULES-INFERNO model results to FireMIP. Matthias Forkel  was supported by a  Living Planet Fellowship  from the  European Space Agency , and  Matthias Forkel  and  Wouter Dorigo  were funded by the  TU Wien Wissenschaftspreis 2015 , a personal science award to  Wouter Dorigo. Niels Andela  received funding from the  Gordon and Betty Moore Foundation  (grant no. GBMF3269 ). The MERIS and MODIS Fire_CCI data sets were generated under the  ESA Climate Change Initiative. Sandy P. Harrison  acknowledges the support from the  ERC -funded project  GC2. 0  (Global Change 2. 0: Unlocking the past for a clearer future, grant number  694481 ).
orig Monthly LAI (M) MODIS MODIS MODIS - - LAI. pre3mon Average LAI over the 3 precedent months MODIS: MOD15A2 LAI (My- MODIS - - - (M) neni et al. , 2015) LAI. pre6mon AveragLAI over the 6 precedent months MODIS MODIS - - (M) cVeg Total vegetation carbon (C) C14: Global biomass map (Car- C14 C14 C14 FM valhais et al. , 2014) -OR - FM: simulated cVeg from each FireMIP model CanHeight Canopy height (C) S11: Satellite-derived canopy S11 - - - height (Simard et al. , 2011) G_height Grass height (C) PC16: Global fuel bed database PC16 PC16 - - (Pettinari and Chuvieco, 2016; Pettinari, 2015) L_depth Litter depth (C) PC16 PC16 - - W_1h Woody fuel of the 1 h size class (C) PC16 PC16 - - W_10h Woody fuel of the 10 h size class (C) PC16 - - -.
, 2012) , for which leaf segmentation is simplified by easily identifiable petioles. Elnashef et al. (2019)  showed that tensor-based classification of 3D point cloud allowed stem detection on maize, wheat and cotton. Still, the leaf segmentation task remains complex in monocotyledonous species where leaves are directly connected to the stem, often rolled within each other and frequently crossing neighboring leaves (Das  Choudhury et al. , 2018 , Elnashef et al. , 2019). Currently, existing 3D analysis pipelines are either semi-automatic  (Mccormick et al. , 2017) , automatic but only for a very limited dataset  (Elnashef et al. , 2019) , or implement only a subpart of the phenotyping pipeline, like 3D geometric reconstruction or organ segmentation from a mesh  (Paproki et al. , 2012; Pound et al.
, 2014). As a consequence, to our knowledge, none of these 3D solutions has been tested on large datasets. We present here the open source library Phenomenal for building automated high throughput analysis pipelines such as 3D shoot reconstruction, light interception, organ segmentation, or complex trait extraction (https://github. com/openalea/phenomenal). Phenomenal pipelines are fully compatible with the scientific workflow infrastructure InfraPhenoGrid  (Pradal et al. , 2017)  that allows processing of large dataset on the cloud  (Heidsieck et al. , 2019) , and with the open-source OpenAlea scientific workflow system that provide full provenance of computations and ensure their reproducibility  (Cohen-Boulakia et al. , 2017)  (https://github. com/openalea/openalea). Here, Phenomenal was first used to build a 3D shoot reconstruction pipeline for several species in 8 high-throughput experiments  (Cabrera-Bosquet et al.
Phenomenal was successfully tested against manual measurements, published datasets and synthetic datasets The algorithm was tested on three independent dataset (Fig. 4 ). The first test consisted of comparing outputs of the pipeline with manual measurements performed during one highthroughput experiment on PhenoArch (Fig. 2017 ), where a different 3D acquisition method was used and for which we had access to a limited amount of metadata. The third test involved the use of synthetic data (Fig. 4  G, H, I, and J) in order to directly compare outputs to 'ground truth' data that are perfectly known  (Lobet, 2017) , using maize plants generated with the ADEL maize simulation model  (Fournier & Andrieu, 1998). Comparison with manual measurement -The organ segmentation workflow was run on 39,548 reconstructed 3D maize plants from the experiment ZA16 of the PhenoArch platform (Table  1 , Fig.
4A, B,  and C ), with a failure rate of 0. 07%. On this experiment, 199,487 mature leaves and 168,404 expanding leaves were automatically detected. On the full set of reconstructed mature leaves, only 4. 6% of them were artifacts (1. 4% with maximal leaf width 20% larger than the actual measured maximum, 134 mm, or smaller than the actual measured minimum, 6 mm, and another 3. 2% having width/length ratio larger than 0. 26 or smaller than 0. 009). In contrast, 15. 6% of the detected expanding leaves had unrealistic morphological characteristics. This result is consistent with the intrinsic  On this dataset, the number of leaves was detected without bias, with a mean absolute error of 1. 03 leaf (Fig. 5 ). This error was a bit higher than for the other tests (0. 4 leaf for sorghum data and 0.
2 leaf for synthetic data), probably related to the fact that this dataset focus only on one particular stage of development, characterized by a high proportion of growing leaves (33% of total), hence increasing the probability of occurrence of artefact detection. length (10. 96 cm versus 7. 94 cm, Fig. 6C ) and leaf width (0. 54 cm versus 1. 84 cm, Fig. 6F ). Part of this noise may originate from differences between the information present on the image and on the plant when it is measured in the lab. The tips of maize leaves, for instance, can often break when leaves are manipulated. This is consistent with leaf length being noisier than leaf area. Growing leaves were less accurately measured than mature ones, especially the shortest ones (Fig. S14 C ). These leaves are those located in the center of the whorl, with a tip that cannot always be identified on silhouette images.
The same occurred for leaf width (Fig. S14  F), and therefore leaf area (Fig. S14 I ). similar error level as manual measurements (Fig. 5 ). Stem length estimates for this dataset were slightly biased, with a mean absolute error of 10. 14 cm (Fig. 5 ). This corresponds to approximately 1. 7 times the mean distance between the insertion points of successive leaves, suggesting that the estimation of stem endpoint by Phenomenal may be located one leaf G, H, I, and J). This allows for testing the method accuracy for leaf number and leaf type detection, leaf length, maximal leaf width, leaf area, leaf insertion height, leaf azimuth and mean leaf angle for individual leaves of plants (Fig. 6 ).
Reanalysis of an existing dataset on sorghum - The segmentation of the leaves of the synthetic dataset resulted in an absence of bias and a mean absolute error of 0. 2 leaves (Fig. 5 ), i. e. is two times more accurate than on the sorghum dataset. The comparison of stem length estimates with reference values yielded also very consistent results, with less than an internode error on the location the last ligulated leaf insertion. Morphology and size on mature leaves was accurately measured when compared with their synthetic counterparts (Fig. 6 ). The leaf azimuth angle and leaf path length (from the base of the plant to the tip of the leaf) were estimated almost without error by Phenomenal (1 and 2 % NRMSD respectively), indicating that the computed skeleton was reliable (Fig. 6A  and D ).
A scientific workflow environment for HTP reproducible experiments Phenomenal components, tools and algorithms are packaged as a Python package library under an open-source license using state-of-the-art computational science methodology. Developers can adapt the code and the design of analysis workflows to fit their experimental or processing needs. For quality assurance and continuous deployment, several tools have been set up. After each modification, a set of unit and functional tests are run on the cloud using continuous integration (https://travis-ci. org). When they succeed, the software is continuously deployed on the web for Linux, Windows and Mac operating systems on a Conda channel  (Grüning et al. , 2018). Documentation is also automatically rebuilt after each modification and updated on the web using the readthedocs service (https://readthedocs.
org). To facilitate the installation of the software by end-users as well as administrators of computing infrastructures, Conda packages and Docker images are provided  (Merkel, 2014). A set of tutorials are available as Jupyter Notebooks to enhance the computational understanding of the different steps by developers, the ability for end-user to reproduce the workflow, and the interaction with the 3D reconstruction through a web-based interface  (Kluyver et al. , 2016). Phenomenal has been integrated into the OpenAlea SWfS, providing users with several major features. First, users are able to compose and interact with processing workflows within a visual programming environment: algorithms can be dragged and dropped in the user workspace, parameters can be modified and their behaviors interactively visualized (Fig.