dataesr/scientific-paragraphs-categorization huggingface dataset:
15 km 3 , this value being the 0. 47-quantile of the statistical distribution of the 66 quaternary submarine landslides deposits analysed in the Alboran Sea. The statistics are taken from the database built within the framework of the ALARM program from interpreted geophysical data collected during a series of oceanographic campaigns that contributed to the characterisation of extreme submarine landslides  (Lafuerza et al. , submitted). From these data, two scenarios of extreme events were chosen to consider different intensity levels of extreme events. The low intensity scenario is a scenario of a SMF of 0. 9 km 3 of displaced sediment with an estimated probability of occurrence estimated of 0. 29 while the high intensity one is a 3. 8 km 3 SMF with an estimated probability of occurrence of 0.
08. The choice for the latter scenario was motivated by the value of displaced sediment volume estimated for the great submarine landslide of Papua New Guinea that was followed by a tsunami in 1998  (Tappin et al. , 2008). Work prior from this study was numerically undertaken to model the generation and propagation phases of the two scenarios chosen. The generation of the two slides were simulated using the numerical code Shaltop® to describe the continuous granular run-out of the sediments of each landslide over a reconstructed 2D swath-bathymetric data. This is a depth-averaged model based on the Saint-Venant hydrostatic equations and the Coulomb-type basal friction considering a Bingham rheology  (Allgeyer et al. , 2019; Bouchut and Westdickenberg, 2004; Lafuerza et al. , submitted; Mangeney et al.
, 2007; Peruzzetto, 2021; Rabaute et al. , 2022). The friction coefficient was set at 100 MPa to ensure the concordance between the slide scars and the deposit extent in the simulations. Then, these slide simulations were defined as the source parameters for the following propagation phase model, which required the coupling between the Shaltop code and the FREe Surface Hydrodynamics using KInetic SchemeS (Freshkiss3D) code, which are both on open access. The latter solves the 3D hydrostatic and incompressible Navier-Stokes equations in context of free surface and variable density flows. The two simulated slides were located at the same site on the northern flank of the Xauen-Tofino bank (Fig. 1 ) where the other landslides were identified  (d'Acremont et al. , 2022; Rodriguez et al. , 2017).
Topographic and bathymetric data Determining accurate tsunami inundation requires precise topographic and bathymetric data to reflect accurately the inundation process. To build a consistent topography-bathymetry with a resolution high enough to be used in the modelling approach, we started from a Shuttle Radar Topography Mission Data (SRTM, Earth Resources Observation And Science Center (EROS), 2017) digital surface model (DSM) of the study area at 1 arc-sec resolution (~30 m) for the topography. The raster was smoothened to remove artefacts and errors (sinks for instance). Using an accurate land register for the city of Martil, indicating the locations of the buildings and housings, the pixels corresponding to the buildings in the DSM were removed to obtain a digital terrain model (DTM).
Hydrodynamic method The hydrodynamic simulations of the tsunami inundation were performed using the numerical code Freshkiss3d® developed with in the ANGE team at Inria  (Allgeyer et al. , 2019). It is an open-source numerical code that can be retrieved from https://freshkiss3d. gitlabpages. inria. fr/fre shkiss3d/index. html. It approximates the 3D incompressible hydrostatic Euler and Navier-Stokes systems with free surfaces. It uses a finite volume and finite element numerical schemes on fixed unstructured meshes and has wet/dry interfaces treatment. When the approximations are solved, the simulations provide several characteristics of the inundation. The first one is the flow rate arriving perpendicularly at the coast. Then the spatial extent of the inundated areas, the water height on land, the flow rate and the velocity rate of the water can be determined.
These results can be withdrawn for every cell of the digital terrain model at different times during the inundation (Fig. 3B ). The Freshkiss3d® model has been validated by  Allgeyer et al. (2019)  through different analytical solutions. No roughness coefficient was to simulate the land occupation. Notice the dynamic equations governing the displacement of the landslide has been simulated with the Shaltop code  (Peruzzetto, 2021).
Validation and limitations of the tsunami inundation determination methods The latter of these objectives was addressed using four methods of tsunami inundation evaluation that were applied to an SRTM raster. Griffin et al. (2015)  argued that using SRTM digital elevation data may lead to an underestimation of the inundation extent, as the elevation data tends to be overestimated. However, Tufekci-Enginar et al. (2022)  showed that they present fewer errors in inundation modelling than other open-source digital elevation models. The SRTM raster used to study the plain of Martil was modified to limit such errors and it was combined with bathymetric data improving the precision for the hydrodynamic model. It has a 25m mesh resolution which  Griffin et al. (2015)  found to be suitable to analyse tsunami inundation, even though a higher resolution would provide a better understanding of flow progression, especially in rivers.
Acknowledgments This work is part of the  Evaluation of seabed hazards and analysis of risks  in the  Mediterranean Sea (ALARM ) funded by the program  EMERGENCE of Sorbonne University  and the mission for transversal and interdisciplinary  initiatives (MITI)  of  CNRS  and  IRD  institutes. We thank the  Ocean Institute from Sorbonne University  for funding the scholarship of the  PhD Elise Basquin. We are grateful for the help of professor  Driss Nachite  from  Abdelmalek Essaâdi University (Morocco ) who provided the nautical chart that allowed us to have precise bathymetric data. We are also thankful to  Bruno Scalabrino  ( Côte d'Azur University, France ) who provided the DSM data that increased the quality of the analysis. The authors acknowledge  Anne Mangeney  ( IPGP, France ) and  Marc Perruzeto  ( BRGM, France ) for fruitful discussions concerning the capabilities of the Shaltop code for simulation of landslides.
Then, the processing of the images is described, including the generation of the orthoimages from the data acquired by the camera, and the LAI and CCC estimation algorithm based on LUTs. The impacts of illuminations and input variables distributions are analyzed and the relationship between LAI and biomass, canopy nitrogen content and CCC were investigated. Nitrogen content and fresh biomass were measured by cutting all the leaves within each 1 m 2 microplot. The sampled leaves were first weighed to obtain the green fresh biomass, and then heated in an oven at 80 • for 36 h for weighing the dry biomass. Finally, the dried leaves were used for the determination of nitrogen content. The nitrogen contents were measured for rapeseed and sunflower microplots. The LAI measurements were taken over a homogeneous region close to the microplot of each field.
On rapeseed and wheat fields, LAI was measured using the Sony ILCE α5100L RGB camera with 6024 × 4024 pixels inclined at 57 • and 1. 5 m far away from the ground. Ten to fifteen photos were taken close to the microplot along the sowing direction, facing the sun to limit shadow influences. On sunflower fields, around 10 photos were taken using a digital camera equipped with a hemispherical lens at nadir direction. All the images were then processed using the CAN-EYE software (https://www6. paca. inra. fr/can-eye, V6. 3, accessed on 14 May 2016) to retrieve LAI. The leaf chlorophyll content was measured using the SPAD device  [28]  with a minimum of 15 leaves randomly located at the top of the canopy. These SPAD raw readings were converted into arial content of chlorophyll (µg/cm 2 ) using the equation by  [29]  and then averaged over each field.
Orthoimage of Multispectral Reflectance The Pix4D software (https://pix4d. com/, accessed on 14 May 2016) was used to generate the ortho-projected multispectral reflectance image automatically from all the images acquired during each flight. This process included the alignment of images according to the GPS information, the reconstruction of the 3D surface model, the calibration of reflectance of images and the generation of orthomosaic images. The reflectance images were then calibrated using images acquired over a calibration body (isotopic white panel name) prior to each flight  [30]  to obtain the reflectance images. The generated ortho-image had a spatial resolution around 8 cm. It was then aggregated into 1 m by a simple averaging method in order to satisfy the turbid medium assumption over each pixel required by the use of a one-dimension canopy radiative transfer model to retrieve LAI from the reflectance  [31].
Finally, 10 LUTs in the size of 13,824 cases were generated. For the 3 LUTs, a full orthogonal experimental plan was adopted to combine the 9 input variables by splitting the whole range of variation of each variable into a small number of classes. This allowed populating more evenly the 9 dimensions of the space of canopy realization. For wheat fields, 5-10 soil samples were collected in the field and the reflectance was measured in the lab. For the other fields, as no spectrum acquisition was performed, we manually selected several soil pixels from the orthoimage at 8 cm resolution by assuming that the reflectance in the blue, green, red and NIR bands continuously increased and the NDVI was smaller than 0. 2. These ground measured or image selected soil reflectance profiles were compared with a soil database, which includes the reflectance of a large range of soil types  [34].
The five closest soil profiles were extracted from the database. A soil brightness factor was then multiplied to the five selected soil profiles to simulate further variations of soil reflectance due to roughness, directional effects of observations and moisture. The same soil reflectance will be used for all three LUTs. For all three LUTs, the PROSAIL model was run in a forward direction two times using different diffuse ratio values (skyl = 0 or 1). The model with skyl = 0 is called 'clear-sky model' in the following context, and the other with skyl = 1 is called 'cloud-sky model'. Note that there is no ground measurement of diffuse ratio since it is not easy in an operational way. To decrease the size and dimension of the LUT, the viewing angle was set to nadir only since we only consider orthoimages.
Conclusions This study proposed a method to generate a field map of LAI and CCC through PRO-SAIL radiative transfer model inversion based on an LUT method using UAV multispectral imageries. Three different distribution laws of LAI were evaluated as inputs to PROSAIL to generate the training database. The results showed that the dense uniform distribution provides the best estimations of LAI, validated by ground measurements, compared to uniform distribution and Gaussian distribution. The impacts of illumination conditions (e. g. , clear or cloudy) on LAI and CCC estimations were then evaluated. The best retrieval strategy was found when using the clear sky PROSAIL model (diffuse ratio = 0) for UAV flights under clear sky conditions, and using the cloudy sky model (diffuse ratio = 1) for flights under cloudy sky conditions.
Acknowledgments:  Wenjuan Li  was supported by the  National Natural Science Foundation of China  ( 42201388 ) and  Central Public-interest Scientific Institution Basal Research Fund  (No. CAAS-ZDRW202107 ). We would like to thank  Matthias Meulien ,  Aurore Argentin  and  Julio Valles  for their valuable help during this study. We also thank  Ovalie, Fred Venault  and  Kamran Irfan  for ground measurements and DELAIR for drone acquisitions.
Questioning The data on age, ethnicity, sex, habits, and health status were collected between 2018 and 2022 when peripheral blood samples were taken from 191 people. In the period from 2020 to 2021, additional questioning of 151 residents of the surveyed villages was conducted by the face-to-face interview method. Questionnaires included detailed data about personal food consumption habits (kind and amounts of consumed foodstuffs per day, how regularly they were consumed, sources of food products of plant and animal origin, questions about health status, length of residence, etc. ). All the collected data were used later for the estimation of individual daily intakes of each food type, as well as the assignment of health status and age groups for people. An example of a detailed questionnaire is provided in the Supplementary Materials (Supplementary Materials, Figure  S1 ).
Genotyping by Multiplex PCR and PCR-RFLP The PCR-RFLP method was used for genotyping polymorphic regions of detoxification genes (GSTT1 (rs1601993659), GSTM1 (rs1183423000), and GSTP1 Ile105Val (rs1695)), DNA repair genes (XRCC1 Arg194Trp (rs1799782), XRCC1 Arg399Gln (rs25487), XRCC3 Thr241Met (rs861539), and XPD Lys751Gln (rs13181)), and antioxidant protection genes (GCLC -129C/T (rs524553), GCLM -588C/T (rs41303970), and GPX4 718CT (Leu220Leu) (rs713041). To set up PCR-RFLP, a preliminary design of primers specific for the studied regions of the SOD3 and GPX4 genes was performed. The design was performed using the online application "Basic Local Alignment Search Tool (primer-BLAST)" developed by the National Center for Biotechnology Information (NCBI). When constructing, genome-wide RefSeq sequences were taken as a basis, including the SOD3 and GPX4 genes (Homo sapience), obtained from the nucleotide database of the National Center for Biotechnology Information (NCBI RefSeq nucleotide database).
; Sklar, P. ; de Bakker, P. I. W. ; Daly, M. J. & Sham, P. C. PLINK: a toolset for whole-genome association and population-based linkage analysis. American Journal of Human Genetics, 2007, 81. ), RStudio (RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www. rstudio. com/ (accessed on 10 June 2022). Samples with a quality of less than 98% (the percentage of genotyped SNPs, expressed in call rates) were excluded from the analysis. Bioinformatic analysis of the results of microchip SNP genotyping included the determination of the reliability of the data, the determination of the homozygosity/heterozygosity for the identified SNPs, and the detection and analysis of the pathogenicity of the identified mutations and polymorphisms by comparing the found mutations with mutations identified as pathogenic in known databases and scientific articles.
Genome-Wide Association Studies data catalog (GWAS catalog-https://www. ebi. ac. uk/gwas/) (accessed on 5 June 2022), database of single nucleotide polymorphisms (dbSNP, https://www. ncbi. nlm. nih. gov/ snp/) (accessed on 5 June 2022), ClinVar (https://www. ncbi. nlm. nih. gov/clinvar) (accessed on 5 June 2022), and 1000 Genomes (1000 G-https://www. internationalgenome. org/1000 -genomes-browsers) (accessed on 5 June 2022).
Statistical Methods The results obtained were processed by traditional statistical methods, including the estimation of average values and standard deviations, T statistics, and the estimation of p-values. Cytogenetics data were taken from our earlier publication on the topic  [32]. Differences were declared significant at p < 0. 05. The significance level (p) was determined using Chi2 (χ 2 ) and Student's t-test. The genotype distribution for single nucleotide variants was estimated by matching to Hardy-Weinberg equilibrium (HWE) with 2 degrees of freedom. Variants deviating from HWE with p < 0. 05 were considered statistically significant.
Calculation of Short-Term and Long-Term Risks for Villages After analyzing all the latest data on methods for determining the risk of pesticide pollution  [33] [34] [35] [36] [37] [38] [39] [40] , we took the following approaches, operating with an average indicator as the basis for calculating individual risk. The estimated daily intake (EDI) of pesticide residues found in different food samples is calculated for each age category using the equation below. EDI = C * IR * EF * ED BwxAT , where C is the mean concentration of contaminants (mg/kg), IR is the rate of ingestion of food (kg/day), EF is the frequency of exposure (365 days/year), ED is the duration of exposure (days), Bw is the average body weight (kg) calculated from questionnaire data, and AT is the time over which the dose is averaged (in days).
Since body weight also affects tolerance to various types of toxic substances, this factor is also taken into account in the final equation: EDI = C * Cons Bw , where C is the quantity of a toxic substance in 1 kg (mg/kg). of the product, Cons is the average daily consumption of the product in the given area (kg/day), and Bw is the body weight (kg)  [34]. The acute/short-term HQ assessment (aHQ) was calculated based on the estimated short-term intake (ESTI) and the acute reference dose (ARfD), using the following equation: aHQ = ESTI AR f D * 100% Estimated short-term intake (ESTI) is calculated using the equation below: ESTI = ΣF x HR : P mean body weight , where F-full portion consumption data for the commodity unit and HR:P-the highest residue level. Chronic/long-term HQ assessment (cHQ) is calculated based on the estimated daily intake (EDI) and the acceptable daily intake (ADI)  [18] , using the following equation: cHQ = EDI ADI * 100% Estimated daily intake (EDI) is calculated using the equation below: EDI = C * CR Bw , where C is the mean concentration of pesticide residues (mg/kg), CR is the consumption rate (kg/d), and Bw is the average body weight (kg).
Information on ARfD and ADI is obtained from the JMPR, available at: http:// apps. who. int/pesticide-residues-jmpr-database/Home/Range/All (accessed on 7 October 2021). Hazard identification determines whether exposure to a pesticide is likely to increase the incidence of specific adverse health effects. The hazard description aims to quantify the relationship between dose level and morbidity. Risk assessment compares the potential intake or consumption of pesticide residues with ADI or ARfD  [34] [35] [36]. To characterize the non-carcinogenic risk, the hazard index (HI) coefficient is used, which is calculated as the ratio of the EDI to the acceptable daily intake (ADI) according to the formula below: H I = EADI ADI If the coefficient is less than 1. 0, then there is practically no probability of an adverse impact.
The theoretical linear multiple regression equation is: Y = β 0 + β 1 X 1 + β 2 X 2 +. + βmXm + ε, where β 0 is a free term that determines the value of Y, in the case when all explanatory variables Xj are equal to 0. Data on product contamination level, cytogenetic analysis, and health status of individuals exposed to pesticide contamination were published earlier  [32]. We used published data to calculate individual risks. Genetic status (frequency of aberrations) and health status (diagnosed diseases) were chosen as dependent variables (Y). The risk calculation for both variables was carried out separately. As independent variables (X), the risk factors presented in the equation were selected: age (X 1 ), exceeding the permissible levels of pesticides (X 2 and X 3 ), and the permissible level of heavy metals (X 4 ) in food products, as well as smoking (X 8 ) and alcohol consumption (X 9 ).
Questioning Results A total of 151 people were interviewed in detail, all of whom lived in close proximity to pesticide storage. For each of the respondents, individual eating habits (the amount and type of food consumed per day) were obtained, as well as information on chronic diseases, bad habits, and sources of food. According to the survey, the dietary habits of the population differ by village, which can make it difficult to calculate real risks when using known average values. Table  1  presents data on the ethnicity, sex, and age of the surveyed population cohorts. The medical status of individuals has been evaluated using both clinical examinations and questioning. Cardiovascular diseases (hypertension, ischemia, atherosclerosis, etc. ) were prevalent (38 persons, 25. 17%). The second most common type was chronic respiratory diseases, identified in 10 respondents (6.
Assessing of Long-Term and Short-Term Risks To assess the health risks for surveyed cohorts living in areas contaminated by organochlorine pesticides, we have used the preliminary obtained data on chemical analysis of 24 organochlorine pesticide contents in main food products of plant (apples, pears, bell peppers, tomatoes, and cucumbers) and animal (milk, meat) origin that were grown in contaminated areas  [32]. The average for cohort pesticide intakes was calculated using standard food intake norms and detected pesticides in food products, which were divided into six groups depending on the chemical structure of active POPs (Table  5 ). The acute (short-term) and chronic (long-term) risks of the impact of pesticides on the health of residents of five localities in the Almaty region were determined.
Assessing of Individual Health Risks To assess individual health risks, we introduced additional criteria evaluated using the multiple regression method. Instead of average pesticide intake, which was calculated using standard food intake norm values, we have applied individual pesticide intake rates, obtained from real data on personal food consumption and pesticide contents in food. Additional criteria also included age, smoking, alcohol consumption, existing health status, level of chromosomal mutations, and the genetic status of key genes involved in the biotransformation of organochlorine pesticides. Thus, all indexes, including body weight, were personalized to calculate individual health risks. A special ranking system was introduced to define the indexes of health status and genetic status.
Based on these data, we included other likely risk factors in the multivariate mutation risk analysis by introducing a scoring system for assessing the action of genes. The proposed scoring system for assessing the performance of genetic systems makes it possible to calculate the effect of each gene polymorphism in this system and to give an overall assessment of the performance of the entire genetic system for subsequent inclusion in the formula for multifactorial analysis of individual risks. A similar approach was used in a patented formula to calculate cervical cancer risk based on the methylation of risk genes  [63]. Our study provides the results of a comprehensive analysis of several risk factors for pesticide exposure, taking into account data that was collected individually. A similar study, in which the authors also used a multiple regression method for the evaluation of DNA damage risks from pesticide exposure, also marked GSTP1 and XRCC1 gene polymorphisms among the main risk factors  [64].
After a series of terminal repair, A ligation and sequencing adaptor ligation, the cDNA libraries were completed through size selection and PCR enrichment. Illumina sequencing was conducted after pooling according to the libraries' effective concentration and expected data volume (Novogene Co. , Ltd). We sequenced a minimum of 9Gb per sample, resulting in around 30 million paired-end 150bp reads. We isolated the RNA of cysticercoids of five highly infected workers (between 18 and 51 cysticercoids, median = 23) and five lowly infected workers (four ants with one cysticercoid, one ant with two) from five colonies, which were also included in the host gene expression analyses. The RNA was isolated using the same protocol as the fat body samples and samples were sent to Novogene for RNA amplification using the SMARTer kit and RNA sequencing.