dataesr/scientific-paragraphs-categorization huggingface dataset:
First Stage: Vegetation and Background Segmentation. U-net is a deep learning model with encoder-decoder network architecture that is widely used for image semantic segmentation. The model was trained over the labelled images from Dataset #1 (Section 2. 3. 1) to predict two classes: vegetation (green and/or senescent) and background. EfficientNet-B2 architecture  [30]  with weights initialized on ImageNet was used as the backbone architecture. Patches of 512 × 512 pixels were used for training after data augmentation based on the Albumentations library  [31]. The training process was based on a Dice loss function with an Adam optimizer. A predefined decaying learning rate schedule (step based) was used to reach local minima, with an initial value of 0. 01 and reaching at the end 10e -6, which is an usual range in standard multilayer neural networks studies  [32].
Second Stage: Classification of Green and Senescent Vegetation Pixels. The support vector machine (SVM) is an efficient machine learning classification method widely used for image segmentation  [34] [35] [36]. It maps the original features to some higher-dimensional space where the training dataset is separable. Several color spaces and transformations  [37]  were used to classify green and senescent pixels including RGB, HSV, CIELab, grayscale, luminances, CMYK, YCbCr, and YIQ derived from the original RGB values. A total of 23 potential input features were thus computed, namely R, G, and B; H, S, and V; L, a, and b; GE; LA, LB, and LC; C, M, Y, and K; Yi, Cb, and Cr; and Yj, I, and Q. However, the possible redundancy and irrelevancy of some features may decrease the accuracy of the classification.
We then selected the most appropriate inputs using the step forward wrapper method  [38]. Finally, 14 input features were  retained: R, G, B, H, S, a, b, GE, M, YE, Cb, Cr, I,  and Q. This second-stage SVM was 4calibrated over labelled pixels from Dataset #2 (see Section 2. 3. 2). The hyperparameters were tuned using a grid search algorithm following a leave-one-out cross-validation principle. This process led to the optimal values C: 1 and γ: 10 -3 , and kernel rbf was set according to prior knowledge that data are not linearly separable. Scikit 0. 23. 2 with Python 3. 7 was used for implementation  [39].
The 3-Class U-net Model (U-net 3C). A three-class U-net model was used as a reference to evaluate the proposed SegVeg approach (Figure  1 ). However, due to the unavailability of a dataset containing entire images annotated into three classes (background, green, and senescent vegetation), we prepared 3-class masks  Plant Phenomics by applying SegVeg over the RGB images used to train U-net 2C (i. e. , Dataset #1). Indeed, to reduce the annotation effort, the second-stage SVM was trained over pixels extracted from regularly spaced grids, explained in the following dataset sections. Therefore, no manually annotated 3-class masks were available as groundtruth references. The same U-net architecture and hyperparameters used for U-net 2C of the SegVeg approach were also employed here during training.
Training and Testing Datasets 2. 3. 1. Dataset #1: Vegetation and Background Fully Annotated Patches. Eight subdatasets from previous studies were compiled to get a wide range of acquisition conditions, species, crop states, and stages (Table  1 ). The images were acquired with several cameras equipped with different focal length optics and variable distances from the ground. All blurred images or those with poor quality were excluded from our study. The original images were then split into several square patches of 512 × 512 pixels, a size selected to keep sufficient context. A total of 2015 patches were extracted, showing a large diversity as illustrated in Table  2. The ground sampling distance (GSD) ranges were between 0. 3 and 2 mm to capture enough details (Figure  3 ). Considering that image annotation is time consuming, it was subcontracted to a private company, imageannotation.
Dataset #2: Green, Senescent, and Background Annotated Pixels. Dataset #2 is composed of annotated pixels only, extracted from images on which we have affixed regular square matrix (grids) of 8 to 11 pixels. This dataset was used to train and test the SVM stage of the SegVeg approach (on green and senescent pixels). After adding the background pixels, it was also used to evaluate the performances of both the SegVeg and the U-nets (2C and 3C). (1) Image Acquisition and Extraction. Three independent datasets (LITERAL, PHENOMOBILE, and P2S2) were used to train and evaluate the proposed methods. 4 Plant Phenomics (i) The LITERAL dataset was acquired with a handheld system called LITERAL (Figure  4 ). An operator maintains a boom with a Sony RX0 camera fixed at its extremity. The camera faced the ground from nadir at an approximately fixed distance (Table  3 ).
The 68 available annotated images covered a wide range of wheat genotypes grown at several locations in France, representing different growth stages, soil backgrounds, and illumination conditions (ii) The PHENOMOBILE dataset was acquired with the Phenomobile system (Figure  4 ), an unmanned    [46]. This system uses flashes for image acquisition making the measurements independent of the natural illumination conditions. Images are acquired from nadir at a fixed distance from the top of the canopy (Table  3 ). The 173 available annotated images covered six crops grown in four phenotyping platforms in France (Table  3 ) (iii) The P2S2 dataset is composed of 200 hemispherical and nadir images. The acquisition was designed to provide a large dataset over a wide range of crops, observed under contrasted growth conditions, throughout the crop growth cycle, covering crucial phenological stages.
More details on the dataset can be found in  [42]  Several cameras were used for the acquisition of the three datasets, resulting in differences in image quality and GSD (Table  4 ). Note that the GSD of this dataset (Table  4 ) is consistent with that of the previous dataset (Table  2 ). A total of 441 images of 512 × 512 pixels were finally selected to represent a wide diversity (Figure  3 ). 6 Plant Phenomics (2) Pixel Labelling. The previously mentioned pixel grids were classified into one of the following six classes, namely, green vegetation, senescent vegetation, background, green/ senescent vegetation unsure, unknown, and others. This allowed us to remove pixels with uncertain annotations and potential bias in the training phase. The green/senescent vegetation unsure, unknown, and others were for instance not used in the training and evaluation of the proposed models.
The web based platform, Datatorch  [47] , was used by 2 annotators. A second round of pixel labelling was performed by 2 other reviewers to find a better consensus on the uncertain pixels and to avoid potential bias in building Dataset #2. Among the 441 annotated images (Table  4 ), the unsure classes represented about 16% of the total number of pixels. It can be noticed that for the PHENOMOBILE dataset, the use of integrated flashes during image acquisition provided better pixel interpretation leading to fewer confusions. This dataset is publicly available on Zenodo and can be accessed by following the guidelines at this link https://github. com/ mserouar/SegVeg. (3) Split between Training and Testing Datasets. A total of 19,738 pixels were finally available to perform the training and testing of the SegVeg SVM stage, of which 6132 were used for training and 13,606 for testing (Table  5 ).
Note that for the evaluation of U-net approaches (2C and 3C), the test Dataset #2 evolves by adding the almost 6000 background pixels annotated from the grid (Figure  1 , Supplementary Material figure  S1 ), which are naturally absent in the green/senescent SVM training and evaluation. The LITERAL dataset that represented only a small fraction of the available patches over wheat crops was kept entirely for testing. The PHENOMOBILE dataset was split randomly into training (30%) and testing (70%) dataset (Table  5 ), resulting in 1803 pixels used to train the SVM model. Similarly, P2S2 was randomly split into 4329 pixels for training (about 40%) and the remaining for testing. This allows to get a balanced distribution between the contributions of PHENOMOBILE and P2S2 datasets to the training process as well as maintain a balanced Green/Senescent pixels fraction.
The splitting scheme was chosen according to the concrete theoretical foundation of the SVM algorithm. SVMs are usually not chosen for large-scale data studies because their training complexity is highly dependent on the dataset size (quadratic to the number of observations), which also comes with calculation time issues  [48] [49] [50]. Moreover, the concept of hyperplane and margins does not require a lot of observations during training, and adding observations could lead to poor generalisation properties. A big amount of initial data was hence kept for the validation step, to ensure robustness in predictions and model performances.
Evaluation Metrics. Since semantic segmentation classifies each individual pixel, three standard classification metrics derived from the confusion matrix were used to quantify the performances of the methods at the class level: precision, recall, and F1-score (Table  6 ). Further, the overall accuracy and overall F1-score were also computed to get a more global evaluation of the segmentation performances 7 Plant Phenomics (Table  6 ). We also considered the fraction of pixels of a certain class in an image in a given viewing direction. This trait is widely used as a proxy of crop development  [51]  particularly for the green parts characteristic of the photosynthetically active elements  [52]. Finally, regression results RMSE and R 2 were also considered to evaluate the methods. All these metrics were computed over the test dataset (Table  5 ), either directly on the test pixels from the image grids, for grid canopy fractions directly on image grids from which the training pixels have been removed, or finally, on the whole images for U-net 3C step.
7 ) on background and combined green/senescent vegetation pixel grids show that U-net 2C first-stage model classifies well the vegetation from the background pixels, with an overall mean F1-score between 82% and 92%. The F1class values are higher for the vegetation class. Misclassifications are observed when either the background corresponding to algae/moss is classified as vegetation (Supplementary Material figure S2, bottom) or senescent vegetation is confounded with crop residues (Supplementary Material figure S2, top). The P2S2 subdatasets, achieved the best F1 all performances.
Green and Senescent Vegetation Classification Performances of the SVM Only and Full SegVeg Approach. The pixel classification performances were evaluated on the following: (i) applying only the second-stage SegVeg SVM and (ii) applying the full SegVeg approach. Results (Table  8 ) show that the green vegetation pixels are generally well identified for the three subdatasets. ð Þ / Tp + Tn + Fp + Fn ð Þ ð Þ × 100 F1-score F1 class 2 × Tp ð Þ/ 2 × Tp + Fp + Fn ð Þ ð Þ × 100 Overall F1-score F1 All 1/N ð Þ〠 N i=0 F1 -score i × 100 % confidence interval error CI 1:96 × ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi F1 score × 1 -F1 score ð Þ ð Þ /n p RMSE RMSE ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 1/n ð Þ〠 n i=1 y i theorical -y i predicted À Á 2 q R 2 R 2 1 - 〠 y predicted i -y theorical i 2 / 〠 y theorical i -y mean i 2 Canopy fraction CF_class ∑ I i=1 ∑ J j=1 image i, j ð Þ= class ð Þ /∑ I i=1 ∑ J j=1 image i, j ð Þ ð Þ(where i and j are, respectively, the width and height of the image in pixels).
8 Plant Phenomics When using only the SVM, the senescent vegetation pixels show significant confusion with the green vegetation for the LITERAL subdataset. The background pixels are preferentially classified as senescent vegetation, except for the LITERAL subdataset (in SVM rows Table  8 ). This highlights the importance of separating first the vegetation from the background with the U-net 2C model since without using contextual information, e. g. , using only the RGB color information, does not allow to separate well the vegetation from the background pixels, particularly for the senescent vegetation and the darkest pixels as illustrated in Supplementary Material figure S3. 3. 1. 3. Performances of the Full SegVeg Approach. Results obtained over the pixels of test Dataset #2 show that the accuracy and F1 all score of the SegVeg model are for the three subdatasets.
The SegVeg approach classifies generally well the pixels into the three classes because of the good performances of the two stages demonstrated earlier (Table  8  and Figure  5 ). However, a significant amount of misclassification is still observed between the senescent vegetation and the background for the PHENOMOBILE subdataset and between the background and the green vegetation for the LITERAL one (Figure  6 ). A 95% confidence interval (CI) error of 2. 7 and 2. 1%, respectively, for senescent vegetation and background was quantified. This CI is two times higher than that of the green vegetation class. The degraded performances observed on LITERAL images could be explained by the complexity of the images due to the presence of awns that are smaller than the pixel size, inducing confusion between classes (Figure  6 ).
Comparison of the SegVeg Approach with the U-net 3C. Results show that U-net 3C (Table  9 ) performs similarly to SegVeg (Table  8 ) on Dataset #2. The Similitude between the two models has been further studied by looking at differences in each pixel predictions between SegVeg and U-net 3C models. SegVeg pixel predictions were used as groundtruth, i. e. , reference values, in confusion matrix of Table  9Similitude  case. The average accuracy and F1 all values for the Similitude are quite high, 90 and 85, respectively, with high values in the diagonal terms of the confusion matrix. However, on average, SegVeg approach exhibits slightly higher performances compared to U-net 3C. Tables  8  and 9  reveal that the best performances for SegVeg come mostly from a better identification of the background pixels, particularly for the LITERAL dataset.
Discussion 4. 1. Use of Different Color Spaces to Better Separate the Green and Senescent Vegetation. Differences in eye sensitivity among operators impact the perception of colors  [53]  and may therefore induce disagreement among them. Further, first stages of senescence may also create differences between Table  8 : Confusion matrix (in % of the labelled pixels), accuracy, and F1 all values computed for the SVM classification only and using the full SegVeg approach for the three subdatasets (e. g. , pixels from Dataset #2). The diagonal terms of the confusion matrix are indicated in gray color. The colors of the two last columns correspond to the accuracy and F1 all values (dark green, highest; dark red, lowest). 9 Plant Phenomics the labelling of operators, since the yellow and reddish colors observed are in continuity with the green ones in the color space.
This applies both to the labelling process and to the model predictions. Misclassified pixels are also observed preferentially in the highest brightness values (Figure  7(b) ). In such conditions, some authors  [56]  propose to assign the saturated pixels to the most frequently saturated class. In our case, this would degrade the segmentation performances since the saturated pixels may belong to any of the three classes. However, a larger representation of green vegetation particularly with glossy leaves under either clear sky conditions or using flashes is often saturated. The confusions observed for the PHENOMOBILE subdataset and leading to slightly degraded segmentation performances (Figure  8 (a)) are partly due to the use of flashes instead of the natural illumination as in LITERAL and P2S2 subdatasets.
The noncollimated nature of the light emitted by the flashes induces a decrease in the intensity of the radiation that varies as the inverse of the square of the distance to the source. When the source is too close to the top of canopy, pixels tend to be saturated with limited classification potential. To limit this saturation effect, images taken from the PHENOMOBILE were slightly underexposed. Further, the pixels located at the bottom of the scene receive very little illumination and are therefore very dark. The distribution of the brightness for the PHENOMOBILE dataset (Figure  8(b )) shows more darker pixels than the other subdatasets acquired under natural illumination conditions. This is in agreement to the higher confusion between the vegetation and the background presented earlier (Tables  7  and 8 ).
Weak Supervision Is Promising. Because of the unavailability of images fully labelled into the three classes, U-net 3C was trained over masks predicted by the SegVeg model. This weak supervision approach could lead to biased predictions, since SegVeg predicted masks are not perfect as demonstrated previously in Table  8 , and obviously, training will converge to similar SegVeg results. Moreover, U-net 3C was trained over whole images compared to 6132 pixels for SVM classification model. However, the performances of U-net 3C (Table  9 ) are quite close to those of SegVeg (Table  8 ) for the PHENOMOBILE and P2S2 subdatasets, while Seg-Veg performs slightly better over the LITERAL subdataset. Comparison between SegVeg and U-net 3C (Table  9 , "Similitude" case) confirms the consistency between the two models, as expected.
Limitation of the Study. This study is based on segmentation models using shallow and deep learning techniques. It is therefore constrained by the availability of training and testing datasets. The first-stage SegVeg U-net 2C model was trained over a relatively large and diverse database (Table  1 ) containing 2015 images of 512 × 512 pixels. The SegVeg SVM is trained over 6132 pixels extracted from grids applied to the original images, thus showing a wide diversity in species, phenological stages, canopy state, and acquisition conditions. However, the pixels labelled as uncertain (green/ Sen. Veg. unsure, unknown, and other) were not used, forcing the SVM model to extrapolate for these situations. Finally, the training was completed over two subdatasets where the P2S2 is overrepresented as compared to PHENOMOBILE.
This is why the results were presented per subdataset. This also partly explains the differences in performances observed over the three test datasets, with a general trend: P2S2 > PHENOMO-BILE > LITERAL. The evaluation of the models was performed at the pixel level. A large number of pixels was considered here (more than 20,000 pixels, including background class, Table  5 ), along with those extracted from the LITERAL subdataset that were not used in training. The "unsure" pixels were not used to compute the performances, which may also induce small biases in the results since the "unsure" pixels may not be evenly distributed between the three classes of interest. However, we did not have other alternatives, since "unsure" pixels correspond mostly to extremely dark, bright (S3), or mixed pixels.
Acknowledgments This work received support from  ANRT  for the CIFRE grant of  Mario Serouart , cofunded by  Arvalis. The study was partly supported by several projects including  ANR   PHE-NOME (Programme d'investissement d'avenir) ,  Digitag  ( PIA Institut Convergences Agriculture Numérique   ANR-16-CONV-0004 ), CASDAR LITERAL, and  P2S2  funded by  CNES. Many thanks are due to the people who annotated the datasets, including  Frederic Venault ,  Micheline Debroux ,  Kamran Irfan , and  Gaetan Daubige.
Integration of the unique patient identifier method in a HIS The patient ID operates within a hospital information system. We test our model by integrating it into the electronic pregnancy monitoring system of the University Hospital of Bobo-Dioulasso (e-Lafia). E-Lafia is an information system for monitoring pregnancy. Patient information is collected in e-Lafia through a registration form. This information is stored in a MySQL database. The choice of the e-Lafia HIS is due to the fact that it uses health interoperability standards such as HL7v3, FHIR, DICOM, thus facilitating interaction with our unique identification system.
Procedure for integrating the patient ID system into the e-Lafia HIS The integration of the system consists in writing a code allowing e-Lafia to recover the various features necessary for the automatic generation of the unique ID. Thus e-Lafia retrieves the ID calculation code in the form of a function. This calculation code generates the ID from the patient data collected in the patient registration form during the consultation with the doctor. To realize the procedure we have integrated our function in the HIS patient controller. This function first retrieves the information (features) needed to calculate the ID and then automatically generates the patient's Unique Identifier. Finally, it records the ID in the "patient" table of the e-Lafia database. The figure. represents the search interface of a patient already registered.
Quality testing of the patient ID system It is recommended to regularly perform a quality assessment of the patient ID system in order to minimize the risk of anomalies (duplication and collision). To test the quality of our system we registered 212 patients in the e-Lafia HIS. The data was then exported from the database, specifically the patient table, in SQL format, which we converted to Excel format in order to perform statistical analysis of the data. To check for duplicates and collisions in our system, we used the duplicate detection method in Excel using the following formula reproduced in column F of the table in the figure  4. =SI(NB. SI($E$1:E1;E1)>1;"Doublon";"") Figure 4 For the 212 records, the duplication and collision rate is 0%. We can therefore consider that our Unique Patient Identification system is relatively reliable.
The RT schemes used by THAI GCMs share a number of characteristics: they are based on the two-stream correlated-k approach; the HITRAN database is used for spectroscopic data, MT CKD formalism is used for continuum absorption 2. They take into account the scattering effects of the atmosphere (Rayleigh scattering) and H 2 O clouds (Mie scattering). There are however, several key differences among the schemes, as outlined below. ExoCAM simulations are performed with ExoRT, whose oldest and most published version is used here; it is referred to as n28archean  (Wolf & Toon 2013; Yang et al. 2016). This version has 28 bands covering the spectral range between 0. 2 and 1000 µm. Note that subsequent intercomparison studies have found this version of the radiative transfer to overestimate the greenhouse effect from pure CO 2 atmospheres (Wolf et al.
ACKNOWLEDGMENTS D. E. S. , I. A. B. , F. H. L, J. M. and N. J. M. acknowledge use of the Monsoon2 system, a collaborative facility supplied under the  Joint Weather and Climate Research Programme , a strategic partnership between the Met Office and the  Natural Environment Research Council. We acknowledge support of the  Met Office Academic Partnership secondment program. This work was partly supported by a  Science and Technology Facilities Council  Consolidated Grant ( ST/R000395/1 ),  UKRI   Future Leaders Fellowship  ( MR/T040866/1 ), and the  Leverhulme Trust  ( RPG-2020-82 ). T. F. and M. J. W. acknowledge support from the  GSFC Sellers Exoplanet Environments Collaboration (SEEC) , which is funded in part by the  NASA Planetary Science Divisions Internal Scientist Funding Model. This project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie Grant  Agreement No.