dataesr/scientific-paragraphs-categorization huggingface dataset:
One of the main limitations for the lightning risk forecast will be the available dataset and time-scale characteristics. Over many years, numerous methods have been developed for the nowcasting and forecasting of thunderstorm risks. Most of these methods have been based on the use of one type of observation source, such as a rawinsonde  (Haklander and Van Delden, 2003; Senesi and Thenepier, 1997) , a ground lightning network  (Betz et al. , 2008; Pedeboy et al. , 2016) , radar  (Dixon and Wiener, 1993; Kober and Tafferner, 2009; Lang, 2001; Wapler et al. , 2012) , satellite  (Mecikalski, et al. 2013 ), or output from numerical weather prediction models  (Wesoleck, 2012; Bright et al. , 2005; Bouttier and Marchal, 2020, Hill et al. , 2020; Yair et al. , 2010). For short-and long-range forecasts (from several hours to several days), the main source of information used as input for diagnostic tools are the outputs of numerical weather prediction (NWP) models.
The use of belief functions in environmental science is less common, even though several studies have been carried out in hydrology  (Abdallah et al. , 2014). This paper is organized as follows: in a first part, a description of the dataset used as input for each method is given. In a second part, each approach is presented, and the resulting forecasts are compared with cloud-to-ground lightning observations recorded by the World Wide Lightning Location Network (WWLLN), from the University of Washington  (Jacobson et al. , 2006). The last part of this paper contains a discussion and conclusions based on the results of this comparative analysis.
The input dataset of the methodologies The input dataset used for the forecasting of lightning strike risk must be based on relevant meteorological parameters for detecting the development of convective cells, which are able to generate an atmospheric electric field of at least several kV/m. The necessary meteorological conditions that can lead to a lightning strike risk are: -instability of air mass; -the presence of humidity and lifting  (Houze, 1993) ; -the presence of hydrometeors (supercooled liquid water, ice crystal and graupel particle) distributed in the convective cloud. The most significant electrification processes are due to the non-induced charge process in which graupel particles and ice crystals are important keys  (Saunders, 1994; Takadashi, 1978). In convective clouds, these two kinds of hydrometeor may exist simultaneously between the isotherm 0 °C and -40 °C; -the presence of an updraft in the cloud core with wind convergence below the base of the cloud and wind divergence near the cloud top.
The dataset used in this work comes from the output of the NWP model Action de Recherche Petite Grande Echelle (ARPEGE) developed by Météo-France in collaboration with the European Centre for Medium-Range Weather Forecasts (ECMWF)  (Courtier et al. , 1991). ARPEGE is a stretched global and spectral general circulation model with a horizontal resolution range from 7. 5 km over Europe to 36 km at the opposite side of the globe (near 60 °S; 180 °E) in its 2015 version  (Pailleux et al. , 2015). The model supplies, as output, an estimate of the future state of the atmosphere. A relevant list of twelve meteorological parameters has been compiled to help characterize the state of the atmosphere from the standpoint of lightning strike risk. In the following sections, these parameters are called 'predictors'.
The dataset used for the evaluation: lightning detection network In evaluating the methodologies, it is impossible to compare the lightning strike forecasts with direct observations of lightning strikes to aircraft, because there are so few that they are not statistically relevant. The evaluation of the performance of the forecasting methods represented in this work is based on natural cloud-to-ground flash observations. If there are cloud-to-ground lightning flashes, it directly implies that there is a risk of lightning strike for aircraft. If there are risky areas on the forecast with no natural cloud-to-ground lightning, it does not mean that there is no risk. In such areas, the atmospheric electric field might not be strong enough to generate natural lightning flashes, but still strong enough that aircraft would trigger lightning.
On the other hand, the performance is poorer near longitude 40 °W, in the middle of the ocean (far from ground stations for correct detection). In this work, the locations and dates of the lightning strokes constitute the WWLLN information used. For this purpose, stroke data are aggregated into a flash using three items of information: the distance between the first return stroke and all the return strokes of the same lightning flash (noted ∆), the time between the first and last strokes (noted ∆), and the time between two following return strokes in the same lightning flash (noted ∆  ). This method of aggregation is often applied  (Poelman et al. , 2013 (Poelman et al. , , 2014;; Cummins et al. , 1998; Betz et al. , 2008; Schultz et al. , 2015; Lay, 2004; Soula et al. , 2016; Goodman, 2012 , Goodman et al.
Description of the three methods used for the estimation of lightning strike hazard All methods use the outputs of the NWP model ARPEGE as their inputs. The dataset used as input, for the three methods, contains the estimate of the twelve predictors (described in Section 1) from the production time of 00:00 UTC in May 2016, with forecast time from 03:00 UTC to 24:00 UTC, with a time step of 3 h.
The NN method The second diagnostic tool applied in the present study is the NN method, developed by ONERA  (Cherrier et al. , 2017)  and adapted to the lightning strike problem. With this method, we try to extract patterns linked to lightning strike risk from a dataset. For this study, information on lightning strike risk is obtained under a dichotomous form (possible risk or no risk), not a level of risk as in the other methods. In 1990, Barnes and Frankel began to investigate the prediction of lightning strikes using NN based on instrumental information such as field mill data, meteorological tower data, or rawinsonde data. More recently, instead of measurements being used as input to NN methods, the estimates of the state of the atmosphere supplied by the outputs of the NWP model have been used to predict thunderstorm risk.
Ukkonen et al. (2017)  have already applied this principle with ERA-40 re-analysis as dataset. The main advantage of using NWP model outputs is the amount of information available to construct the training base and the test base. Nevertheless, it is important to recall that despite the fact that the NWP model improves steadily with more and more accurate forecasts, perfect forecasting is not possible. Sher and Messori (2018)  highlight the risk that noise inside the initial dataset in all parameters can induce information not relevant to the problem of interest. The NN method used in this study comprises two steps. The first is the training phase based on a dataset where information on the state of the atmosphere is associated with a lightning strike. During this phase, the NN learns and the NN's parameters are tuned in order to minimize a cost function.
It also seems that better performance is even possible using convolutional layers, taking predictors from neighboring cells as input. However, this convolutional neural network (CNN) would not be comparable with other methods. The training base was constructed using the value of the meteorological predictors (listed in Table  1 ), in both cases: when a flash has been detected by the WWLLN in a mesh, or not. Datasets were created by collecting three years of data, from 2013 to 2015, over the North Atlantic Ocean. Concerning the statistics used in the LISHAE method: different datasets were calculated for each time step of one day (every 3 hours to be consistent with the time step of the NWP model) and each month (to take into account the monthly variability of the lightning activity over the area).
For the LISHAE method, the SET method allows for an importance weighting between criteria. For each parameter, a preference ordering and an arbitrary importance weighting have been defined (see Table  4 ). For each criterion (Gj), bounds (Gj(bh)) have been defined for each category of risk. For instance, for the parameter LI, the bounds b1, b2, b3 and b4 are 0 K, -3 K, -5. 5 K and -6. 5 K respectively. Consequently, if the value of LI is positive, the risk is zero; if the value of LI is in the interval [-3 K; 0 K], the risk level is 1; if the value of LI is in the interval [-6. 5 K; -5. 5 K], the risk level is 2, and so on. Each bound has been deduced from the quantiles obtained after the creation of a database containing the values of meteorological predictors in lightning conditions (the same database described in step 1 of the LISHAE method).
These bounds defined for the North Atlantic Ocean area take the temporal variation into account. Then, from the test dataset (year 2016 of the ARPEGE model data), in a given mesh and at a given time, an alternative profile called "a" (red line in Figure  6 ) is created, consisting of the 12 predictors. This profile constitutes the input for the SET method that will try to assign a category of risk to this alternative profile. The principle of the SET method, described in Dezert and Tacnet (2012a) and  Dezert et al. , (2021a) , requires four steps: -step 1: the calculation of local concordance and discordance indices between the alternative ai and a profile bh (cf. Figure  6 ) using a sigmoidal model  (Dezert et al. , 2012b). These partial indices are then encapsulated in a Basic Belief Assignment (BBA), defined on the frame of discernment  ≜ {, ̅ }, where c means that the alternative is in agreement with the assertion "ai is at least as good as profile bh" and ̅ means that the alternative is discordant (opposed to the assumption); It is important to mention that, for some parameters, data are missing, and a -9999 value is assigned.
These missing values mainly concern the temperature and the divergence at the top of convective clouds and the altitude and divergence near the bottom of convective clouds. The SET method has been adapted to allow for these "gaps" in the dataset. Moreover, the variability of ARPEGE data in each cell has been studied by calculating the level of randomness, characterized by Shannon's entropy. For each cell of the North Atlantic Ocean, the probability of risk has been estimated by counting the number of criteria (predictors) associated with the category Ch and dividing by ng. The normalized Shannon entropy is given by the following formula: 𝐻(𝑝 𝑖 ) = - 1 𝑙𝑜𝑔 2 𝑛 ℎ. ∑ 𝑝 ℎ 𝑙𝑜𝑔 2 𝑝 ℎ 𝑛 ℎ ℎ=1 The map of the normalized Shannon entropy is shown in Figure  7. Results are obtained using the output of the ARPEGE model for the time production 05/09/2016 at 00:00 UTC and the time step 05/09/2016 at 09:00 UTC.
Comparison of the lightning strike hazard given by the three methods for one case This section presents the lightning strike risk results obtained using the three methods (LISHAE, NN-based, and Soft ELECTRE Tri (SET)). To maintain a uniform basis of comparison, each method was applied to the same dataset. The dataset, containing the 12 predictors, was constructed based on the outputs of the ARPEGE model, for a given time step, from the time production of May 9, 2016 at 00:00 UTC. In the first instance, results obtained for one date and one time step (at 09:00 UTC on May 9, 2016) are presented. Then, secondly, the results are generalized to all time steps in the month of May 2016.
The main difference appears above the latitude of 40°N. To understand the reason for these differences, the lightning strike hazard was estimated using a reduced dataset. This new dataset comprises six predictors: Max of RH, PCONV (accumulated over 3 h), TWC, TIWC, WISO, and NEBCON. The maps of these parameters highlight meteorological conditions that favor the occurrence of a thunderstorm for areas above the latitude 40°N and not previously identified (Figure  11 ). ; -5. 10 -2 ; -1. 10 -2 ; -1. 10 -3 ; 0; 1. 10 -3 ; 1. 10 -2 ; 5. 10 -2 ; 1. 10 -1 m/s); TWC (figure  11b  unit: kg/m²). Isocontours are plotted for the values (1. 10 -4 ; 4. 10 -4 ; 6. 10 -4     8. 10 -4 ;1. 10 -3 ; 5. 10 -3 ; 1. 10 -1 ; 5. 10 -1 kg/m²); TIWC (figure  11c  unit: kg/m²). Isocontours are plotted for the values (1.
10 -4 ; 4. 10 -4 ; 6. 10 -4 8. 10 -4 ;1. 10 -3 ; 5. 10 -3 ; 1. 10 -1 ; 5. 10 -1 kg/m²) and NEBCON (figure  11d  unit: %). Isocontours are plotted for the values  (10; 25; 50; 75; 99 %). In each picture, the red dots indicate the WWLLN data. The NN method was applied to a reduced dataset. The results are present in Figure  12  for the NN parameter 1 (better suited to this new configuration). If we compare Figure  12  with Figure  10 , we again find meshes classified as risky for latitude above 40 °N (Figure  12 ), in contrast with Figure  10. If we focus on this specific area for the different maps of predictors (Figure  8  and Figure  11 ), we see that only a few parameters, such as NEBCON, TIWC and TWC, detect information in this area. Only predictors detecting a signal in areas above 40 °N are used in the second dataset.
Consequently, new areas classified as risky appear with this second dataset compared to Figure  10b. This test highlights the fact that the method classifies an area as risky if most predictors share the information, even if their weighting in the convective development is lower than some parameter, such as LI, for which the information at 40 °N does not appear. This highlights the good results obtained by the NN method in terms of estimation of lightning strike risk. The main disadvantage of the NN method is that it focuses on the most noteworthy features even if they are of less importance, which is a known phenomenon.
Consequently, good agreement is found between flashes detected by the WWLLN and meshes classified as risky by the method based on the PCR6 rule. Nevertheless, it is important to mention that the SET method with the averaging merge fusion rule has been applied to the dataset of five meteorological parameters (PCONV, LI, CAPE, DIV_B, DIV_T). The results are presented in  Dezert et al. (2021a). For the configuration with five parameters, better results have been found, similar to those obtained using PCR6 with five parameters. This means an improved rate of correct detection and a slightly reduced rate of false alarms. One of the main advantages of the SET method is the possibility of obtaining a decision confidence map representing the probability associated with the risk level determined in a mesh.
The confidence level has been plotted for both fusion methods in Figures 13c (for the average rule) and 13d (for the PCR6 rule). The confidence level is between 0. 8 and 1 for the PCR6 method (Figure  13d ) and ranges from 0 to 1 for the other method. As we observe in Figures  13c  and 13d  implies a difference in terms of risk level between all the methods. This area is classified as no risk by the averaging method and as a risk level of 5 by the PCR6 method. This uncertainty for this area is also found with the other methods. NN assigns no risk based on a dataset of 12 parameters and a non-zero risk with a dataset of six parameters. The LISHAE method assigns a risk level of 3, expressing a significant risk.
Discussion and conclusions In order to estimate the potential in terms of lightning strike risk of the three methods described in Section 2, each method was applied to one month of datasets. For each day of May 2016 and the eight time steps (03:00, 06:00, 09:00, 12:00, 15:00, 18:00, 21:00, 24:00 UTC), a dataset composed of the 12 predictors was constructed based on the ARPEGE simulation runs at 00:00 UTC and 12:00 UTC of each day. For the time steps 03:00, 06:00, 09:00 and 12:00, the time production of 00:00 UTC was chosen with the lead time varying from 3 h to 12 h, whereas for the time step of 15:00, 18:00, 21:00, 24:00, the time production of 12:00 UTC was chosen with the lead time varying from 3 h to 12 h. These choices allow short lead times to be used in order to have the associated smaller errors instead of longer lead times, where errors can be significant.
For each mesh k, we estimate the lightning strike risk using the three methods and compare it to the flashes detected with the WWLLN (as done before). All the meshes between latitude 1 °S to 71 °N and longitude 101 °W to 10 °E with a resolution of 0. 5°×. 0. 5° have been considered to calculate the confusion matrix value (cf. Figure  4 ). In order to allow for temporal variability (seasonal and diurnal), thresholds (for the LISHAE method) and a learning dataset (for the NN method) have been adapted for each time step. Statistical results for the month of May 2016 are shown in Table  10. The results, for one month, indicate that more than 89% of meshes where flashes were detected by the WWLLN are correctly classified by the LISHAE method, the NN method, and the SET PCR6 method. We note a large fluctuation in the false alarm rate from one method to another, from 71% with the LISHAE method to 28% with the NN method.
The short-range estimation of lightning strike risk currently relies solely on the use of NWP model outputs. In this study, three methods have been developed or adapted and used as diagnostic tools with the same dataset as input. All the methods chosen can be easily adaptable to a specific area. Importantly, few changes are needed for the LISHAE and SET methods (only the adaptation of the thresholds), while a new learning base is required for the NN method. Although this would not be difficult to generate, it still constitutes a slight disadvantage of the NN method. Comparison of the results has highlighted the advantages and drawbacks of each method. To summarize, the main characteristics of each method are shown in Table  13. The execution time, the complexity, and the amount of information have been added to the advantages and drawbacks to compare the methods.
Design: We developed an MSI pre-screening tool, MSIntuit, that uses deep learning to identify MSI status from H&E slides. For training, we used 859 slides from the TCGA database. A blind validation was subsequently performed on an independent dataset of 600 consecutive CRC patients. Each slide was digitised using Phillips-UFS and Ventana-DP200 scanners. Thirty dMMR/MSI slides were used for calibration on each scanner. Prediction was then performed on the remaining 570 patients following an automated quality check step. The inter and intra-scanner reliability was studied to assess MSIntuit's robustness. Results: MSIntuit reached a sensitivity and specificity of 97% (95% CI: 93-100%) / 46% (42-50%) on DP200 and of 95% (90-98%) / 47% (43-51%) on UFS scanner. MSIntuit reached excellent agreement on the two scanners (Cohen's κ: 0.
Tool's consistency across slides from different blocks of the same tumour. For a subset of 200 patients out of the 600 patients of MPATH-DP200 dataset, 1 to 4 other tumour slides coming from different blocks of the same patient were digitised, resulting in a total of 398 additional slides. We characterise the tumour morphology of these slides (figures 4A, 4C) using a ResNet18 model trained on NCT-CRC-HE-100K dataset  [16]  from the TIAToolbox library  [16, 17]. This classifier takes as input a tile of 112 × 112 μm and outputs a probability for each of the following classes : adipose (ADI), debris (DEB), lymphocytes.
MATERIALS & METHODS Cohort description. Three cohorts were used in our study: a discovery cohort to train our model, an independent development cohort to gain insights about the model performances on an external dataset, and an independent validation cohort, blinded to patients' MSI statuses, to assess the performance of MSIntuit in a one-shot fashion. Inclusion criteria for all cohorts were as follows: unequivocal diagnosis of CRC, available histological slides of resected specimens from the primary tumour, available MSI status. The discovery cohort, denoted TCGA here, is a multicentric cohort of 859 whole slide images (WSI) from 434 patients from the TCGA-COAD database diagnosed in 24 US centres  [11]. 427 Formalin-Fixed Paraffin-Embedded (FFPE) and 432 snap frozen H&E-stained WSIs from these patients were used to develop our model.
As a result of this preprocessing, we obtained n=541 (dMMR/MSI: 87) and n=558 (dMMR/MSI: 90) slides for MPATH-DP200 and MPATH-UFS cohorts respectively. Additionally, to address the issue of variations in data acquisition protocols such as stainers and scanners that may impact deep learning model prediction distributions, we used a calibration strategy to ensure a sensitivity between 93% and 97% was obtained for the blind validation (figure  1B ). For both MPATH-DP200 and MPATH-UFS, 30 slides from the same dMMR/MSI patients were used to define the operating threshold leading to 1/30 misclassification (meaning, 1 slide was classified as MSS-AI, and 29 were classified as Undetermined). The number of slides used in this calibration step was chosen after a sensitivity analysis on several internal datasets showed that 30 slides were sufficient to ensure a high likelihood that the sensitivity of MSIntuit on the remaining samples was at least 93%.
This process led us to choose a threshold of 0. 20045 on the MPATH-UFS dataset and 0. 20202 on the MPATH-DP200 dataset. The similarity of the two thresholds suggests that the variations between UFS and DP200 scanners did not meaningfully impact MSIntuit predictions, despite the model having been trained on data collected using another scanner (Aperio). MSIntuit performance was boosted using self-supervised learning, allowing it to rule out almost half of the MSS population with high sensitivity. During model development, we found that using a feature extractor pre-trained with self-supervised learning on millions of histology tiles yielded a performance improvement. We compared this approach against a method widely used in medical imaging which consists of using a feature extractor trained on the ImageNet dataset, a dataset which does not contain any histology images.
A key technical strength of our approach relies on the use of self-supervised learning to extract features from the histology images. Using this method, we were able to train a feature extractor tailored for histology on 4 million CRC histology images without the need for any labels. As already pointed out by previous studies  [22] [23] [24] , we observed that such feature extractor was more robust to scanner variations and largely outperformed a feature extractor pretrained on ImageNet dataset for MSI prediction task, an approach still widely used in medical imaging. A key objective of this study was to ensure that the MSIntuit tool could be deployed in clinical practice. To examine the impact of using different scanners at different sites, we digitised 600 slides with two different scanners.
SUPPLEMENTAL Supplemental methods Preprocessing of whole-slide images. A preprocessing pipeline was needed to reduce dimensionality and clean the data before training any model. The first step of our pipeline consisted of detecting the tissue on the WSI: a U-Net neural network  [26]  was used to segment part of the image that contains relevant matter, and discard artefacts such as blur, pen marker etc. , as well as the background. This U-Net network was previously trained on 460 H&E and MMR-IHC slides from an internal dataset where tissue was manually annotated, and validated on 115 slides with a Dice score of 0. 96. This network was applied on images of size 2048 x 2048 μm (512 x 512 px, at a resolution of 4 MPP) extracted from the WSI. The second step consisted of splitting the slide into smaller images, called "tiles", of 112 × 112 μm (224 × 224 px, at a resolution of 0.