dataesr/scientific-paragraphs-categorization huggingface dataset:
Due to the low MAF frequency in the CANDELA data, the exclusion of these SNPs has a negligible impact on prediction accuracy. The lack of informativity of the 7 discarded SNPs in the CANDELA dataset is underlined by the fact that they are all located in the MC1R region, and are important mainly for red hair prediction, a trait nearly absent in the CANDELA data (the few red/reddish individuals with red hair were removed due to their low frequency). Of the 34 SNPs retained for the analyses, 13 had imputed genotypes, all with high imputation quality metrics (IMPUTE2 ′ s INFO > 0. 8 -see Supplementary table  S1 ). Overall, for all the imputed SNPs used in the analyses (either from CAN or HIrisPlex SNP sets), the average imputation quality metric (INFO) was high, 0. 942. In addition, we verified the accuracy of imputed genotypes for these SNPs by comparing with two independently sequenced datasets.
Prediction methods and models evaluated A broad array of statistical methods have been employed in the literature to predict pigmentation traits, such as (multiple) linear  [42, 43]  or (multinomial) logistic regression  [23, 24] , decision trees  [42, 44] , neural networks  [42, 45] , and naïve Bayes classifiers  [37, 46, 47]. Each method has its advantages and disadvantages, and are better suited for certain types of traits, e. g. linear regression for quantitative traits  [42, 43]  and logistic regression for categorical traits  [23, 24]. The overall strategy we used for performing pigmentation prediction in the CANDELA dataset is shown in Fig. 1. Linear Regression (LR) or Multinomial Logistic Regression (MLR) were used as the reference methods for quantitative or categorical traits, respectively.
1B ). For this, the full dataset is split into 10 approximately equal subsets based on a stratified sampling on the trait (so that trait distribution is similar across subsets). Each of the 1/10th subsets is used as test data for evaluating prediction accuracy of methods trained using the other 9/10th of the data. For regression methods (MLR, LR, SR, OR), coefficient parameters are estimated in the training data (Fig. 1C ). For Machine Learning models, the training data is further split into a tuning data (70% of the training data) and validation data (30% of the training data). The parameter space for these methods are tuned creating a grid of all possible combinations of the hyperparameters and the particular combination producing best result on the validation data is selected as the set of optimal combination for the whole training data (see Supplementary Method S2).
Comparison with prediction accuracy from HIrisPlex-S-online Several studies have examined the prediction accuracy of HIrisPlex-S for categorical pigmentation traits using an online tool (https://hir isplex. erasmusmc. nl, referred to here as HIrisPlex-S-Online). This implementation uses MLR prediction models trained in a reference dataset comprising individuals with various continental origins (80% from Europe, 16% from North-Americaincluding individuals of European, African and Asian ancestryand 4% from Africa and Oceania)  [20, 24, 27]. For Eye colour HIrisPlex-S-Online predicts eye colour categories corresponding to those used in the CANDELA dataset (Blue/Grey; Intermediate and Brown/Black). For hair colour, HIrisPlex-S-Online predicts the categories Red, Blond, Brown and Black. The last three correspond to hair categories common in the CANDELA dataset.
However, due to their low frequency, individuals in the CANDELA dataset with Red hair were removed prior to these analyses. We therefore adjusted the prediction probabilities estimated by HIrisPlex-S-Online in the CANDELA dataset, for Blond, Brown and Black by the probability of Red hair colour. Finally, HIrisPlex-S-Online predicts five categories of Skin colour (modified from the Fitzpatrick scale): Very pale, Pale, Intermediate, Dark and Dark-Black. To allow comparison with the three-category predictions from models trained in the CANDELA data, we followed Walsh et al. [26]  in merging the prediction probabilities for Very pale, Pale and Intermediate into one category (Light, corresponding to the "White" category of Fitzpatrick). The Dark and Dark-Black of HIrisPlex-S-Online correspond to the Brown and Black categories of the Fitzpatrick scale  [26].
Results The CANDELA dataset analyzed here consisted of individual genome-wide SNP genotypes and pigmentation traits. In that dataset eye colour was recorded both as categorical and quantitative variables, hair colour as a categorical variable, and skin colour as a quantitative variable (the Melanin Index). In what follows we compare the performance of HIrisPlex-S with SNP sets devised here for the prediction of eye (CAN-E), hair (CAN-H) and skin (CAN-S) from summary statistics of a   S2. Consistent with the genetic correlation of eye, hair and skin pigmentation, some SNPs are shared across the CAN-E/H/S sets, as well as with the HIrisPlex-S set. The overlap between these four SNP sets is shown in Fig. 2.
The main difference in eye-colour prediction across studies lies in the intermediate category. No intermediate eye colours were predicted by HIrisPlex-S-Online in the Venezuelan/-Brazilian sample. Our predictions for the intermediate category in the CANDELA sample have higher accuracy than that reported for this category for HIrisPlex-S-Online in Europeans, both when the prediction model was trained in the CANDELA data or in the reference HIrisPlex-S data (respectively 89% and 85%, versus 73% in Europeans). Prediction accuracy, in the CANDELA sample, of the CAN-E and HIrisPlex-S SNP sets was identical. Estimates for hair colour prediction accuracy using HIrisPlex-S-Online have been reported for a European sample  [39]. Prediction accuracy estimates obtained here for the CANDELA sample are higher than reported in Europeans for all hair colours, except in the case of intermediate hair-colour (i.
e. brown) predicted with HIrisPlex-S-Online. The highest hair-colour prediction accuracy was consistently obtained with the CAN-H SNP set, although the difference relative to HIrisPlex-S trained in the CANDELA data is marginal. Concerning skin colour, we observe that predictions from HIrisPlex-S-Online have markedly lower accuracy in the CANDELA sample than reported for a world-wide sample. Model training in the CANDELA dataset increases prediction accuracy substantially, both for HIrisPlex-S and CAN-S (with CAN-S SNP set marginally outperforming HIrisPlex-S), although the accuracy values obtained for both sets are still below those reported for HIrisPlex-S-Online.
Portability of models for pigmentation prediction in individuals with high Native Ancestry Considering the impact of training datasets in the performance of HIrisPlex-S (Table  1 ), we specifically examined the portability of RF models developed in two training datasets with extreme differences in ancestry (extracted from the CANDELA samplesee Supplementary Fig. S3 ): (i) a highly European training dataset (European ancestry >= 80% and Native American ancestry < 20%) and (ii) a highly Native training dataset (European ancestry < 20% and Native American ancestry >= 80%). We examined the performance of the resulting prediction models in a subset of the highly Native test dataset (Fig. 5 ) in a cross-validation scheme. We observe that models developed in the highly Native training dataset have a better performance than those developed in the highly European training dataset for Chrome, Luminance and MI.
Prediction of skin pigmentation in Native Americans We examined prediction performance of the CAN-S and HIrisPlex-S sets in a highly Native dataset independent of CANDELA by predicting MI in 117 individuals from 17 Native American populations  [35]. As above, we trained RF prediction models using CANDELA individuals with >= 80% native ancestry. Since performance could not be measured directly in this dataset (due to the lack of phenotypic data), we examined the correlation of predicted skin pigmentation (MI) with solar radiation levels at the site of population sampling (Fig. 6 ). Previous surveys of skin pigmentation in native populations from across the world have found a Fig. 5. Portability of prediction models trained in highly European/Native American cohorts. For each continuous trait (cos(H+15), C, L and Melanin Index) we compare prediction accuracy on the same test data (a highly Native ancestry cohort).
Although quantitative variables are intrinsically more informative than categorical ones, forensic applications are mostly interested in the prediction of discrete categories, often just two (e. g. blue v. non-blue eyes) or three (light, intermediate or dark pigmentation). In that setting we find that for the CANDELA dataset there is remarkably little difference in performance between HIrisPlex-S and the much larger CAN SNP sets, particularly for eye and hair colour. As discussed above, this is likely to relate to the lower informativity of categorical traits, to which several other factors could be contributing, such as HIrisPlex-S capturing particularly well the genetic architecture of eye and hair colour, and having been optimized for the prediction of categorical traits. However, our analyses indicate that use of the online implementation of HIrisPlex-S for the prediction of pigmentation traits in Latin Americans should be performed with caution.
6. [55]. However, for forensic applications, in which predictions of interest mostly relate to discrete categories, increasing the number of genetic predictors does not appear to provide much benefit, even for skin colour. The HIrisPlex-S SNP set, optimised precisely for this type of application, already provides excellent prediction accuracy. However, use of HIrisPlex-S for forensic studies in Latin America should carefully consider training of prediction models in reference datasets that more closely match the genetic diversity of the region. Beyond the statistical accuracy of the models used, pigmentation predictions conducted outside scientific research should consider carefully the risks associated with the misinterpretation or misuse of such predictions, particularly in relation to disadvantaged minorities.
Acknowledgments We thank the volunteers for their enthusiastic support for this research. We also thank  Alvaro Alvarado ,  Mónica Ballesteros Romero ,  Ricardo Cebrecos ,  Miguel Ángel Contreras Sieck ,  Francisco de Ávila Becerril ,  Joyce De la Piedra ,  María Teresa Del Solar ,  William Flores ,  Martha Granados Riveros ,  Rosilene Paim ,  Ricardo Gunski ,  Ana Angélica Leal Barbosa ,  Sergeant João Felisberto Menezes Cavalheiro ,  Major Eugênio Correa de Souza Junior ,  Wendy Hart ,  Ilich Jafet Moreno ,  Paola León-Mimila ,  Francisco Quispealaya ,  Diana Rogel Diaz ,  Ruth Rojas , and  Vanessa Sarabia , for assistance with volunteer recruitment, sample processing and data entry. We are very grateful to the institutions that allowed the use of their facilities for the assessment of volunteers, including:  Escuela Nacional de Antropología e Historia  and  Universidad Nacional Autónoma de México (México) ;  Universidade Federal do Rio Grande do Sul (Brazil) ;  13 • Companhia de Comunicações Mecanizada do Exército Brasileiro (Brazil) ;  Pontificia Universidad Católica del Perú ,  Universidad de Lima  and  Universidad Nacional Mayor de San Marcos (Perú).
The French Riviera is very often threatened by flash floods. These hydro-meteorological events, which are fast and violent, have catastrophic consequences on life and property. The development of forecasting tools may help to limit the impacts of these extreme events. Our purpose here is to demonstrate the possibility of using b-flood (a subset of the Basilisk library http://basilisk. fr/, last access: 8 November 2021), which is a 2D tool based on the shallow-water equations and adaptive mesh refinement. The code is first validated using analytical test cases describing different flow regimes. It is then applied to the Toce river valley physical model produced by ENEL-HYDRO in the framework of the CADAM project and on a flash-flood case over the urbanized Toce area produced during the IMPACT project.
, 2019). For these reasons, we have chosen to use a fully integrated model based on the 2D shallow-water equations with rain source terms solved thanks to a finite-volume scheme on an adaptive mesh using the software Basilisk (http://basilisk. fr/, last access: 8 November 2021). The originality of the model is in gathering, using the same code, both the fluvial and urban flood configurations using up-to-date numerical schemes with a forecasting computation time  (Buttinger-Kreuzhuber et al. , 2019; Horváth et al. , 2020)  thanks to the automatic adaptive mesh refinement. In Sect. 2 we present the model, the numerical method, and the adaptive mesh technique. In Sect. 3 we first exhibit the ability of the Basilisk software to catch different types of flow regimes for analytical solutions developed in  MacDonald et al.
Rain The rain is simply treated as S rain = R, where R is the local intensity of the rain (in m s -1 ). This source term is added in the mass equation (Eq. 1) and was validated in a previous study using Basilisk  (Kirstetter et al. , 2016). Note that it is possible to integrate rain from Météo-France RADAR PANTHERE data directly in the code thanks to the read_lamedeau() function (see  METEO-FRANCE, 2021).
Adaptive mesh refinement (AMR) The b-flood software takes advantage (in addition to other techniques) of the adaptive mesh refinement (AMR) technique developed on Basilisk by S. Popinet  (Popinet, 2015). This process is very well explained in van Hooft et al. (  2018 ) and in the sandbox of Van Hooft on the Basilisk website (van Hooft, 2021). We recall here the general mechanism, drawing heavily on the previously cited publications. The adapt_wavelet() function allows us to refine or to enlarge the mesh according to the error estimated by the algorithm between two levels of refinement on one or more scalar or vector fields. The error criterion and the fields concerned are set by the user. If the error estimated is greater than the user-defined criterion, the algorithm will refine the cell into four smaller daughter cells.
B-flood: a subset of Basilisk In practice, the b-flood software is a sub-component of the open-source Basilisk software created by  Popinet (2013). This means that it takes advantage of all the features of Basilisk: it is completely free and open-source software. In addition to the AMR described above, it also allows parallel computing. Experienced users will be able to develop their own modules, while casual users will be able to copy and paste the sample scripts given in the rest of this article. It is still recommended to learn how to use the basics of Basilisk before using b-flood, which is made easier thanks to the various tutorials available on the website (http://basilisk. fr/). 3 Evaluation of the performance for test cases.
Analytical test cases It is important to ensure that the numerical schemes we use are consistent. We test them on complete benchmarks, i. e. the transition from subcritical flow, when the wave velocity is higher than the flow velocity to supercritical flow, as well as the transition from supercritical to subcritical flow, which is characterized by the presence of a shock. The following test cases can be found in the software SWASHES published in  Delestre et al. (2013). They are designed to test the validity of implementation of source terms and the consistency of the numerical scheme by comparing the numerical solution with the analytical one.
Conclusions This paper presented b-flood, an open-source Saint-Venant model for simulations of surface flows in two dimensions using adaptive refinement. The code is completely free and open source like the Basilisk software from which it is derived. The model uses a well-balanced scheme that does not prevent water from flowing over steep topography. The validity of the numerical scheme has been tested on two analytical benchmarks. The convergence of the scheme has been observed with a good order of convergence. The code has been then tested on two experimental cases in the Toce Valley, one fluvial and the other urban. The results of the simulation gave satisfactory agreement with the experimental results. Finally, we demonstrated the practical effectiveness of b-flood on a real case of flash flooding on a small watershed in the south of France: the October 2015 flooding of the city of Cannes in the French Riviera.
This will improve b-flood's capability when performing more complete flash-flood simulations, particularly in southern French watersheds. Code and data availability. The address of the relevant Zenodo folder is as follows: https://doi. org/10. 5281/zenodo. 4617606 (Kirstetter and Popinet, 2021). Note that the Basilisk software needs to be installed before visiting this address: http://www. basilisk. fr/ src/INSTALL (last access: 8 November 2021). You will find the (open-source) code and the different data files required to reproduce our results here: http://basilisk. fr/sandbox/b-flood/Readme (Kirstetter et al. , 2019). It should be noted that we do not have the right to distribute the DTM file of the IGN's topography of Cannes or the Météo-France files. The case study of Cannes performed in this paper is therefore unfortunately not reproducible.
Video supplement. The movie representing the water height during the Cannes flood (Cannes-height. mpg) and the video representing the refinement level of the cells of the same simulation (Cannes-level. mpg) can be downloaded from the following address: https://doi. org/10. 5281/zenodo. 5061754 (Kirstetter, 2021). Author contributions. GK developed the code for b-flood and took care of the technical part. OD took care of the test cases and brought his expertise on the different competing codes of b-flood. PYL supervised the project and brought his expertise on the different physical models. SP developed the codes on which b-flood is based (basilisk) and brought his technical expertise on the technical and algorithmic part. CJ was in charge of the acquisition of the funds and supervised the project.
Acknowledgements. We would like to thank  François Bourgin  for the initial idea of this paper. We would like to thank the  IGN  for providing us with the topographic data and  Météo-France  for providing us with the data from their RADAR PANTHERE. We also thank the  AXA Foundation for Research fund  for their financial support at the very beginning of this study. Review statement. This paper was edited by  Bethanna Jackson  and reviewed by two anonymous referees.
Database compilation We compiled a database from 61 studies conducted in 11 countries (eight in Asia and three in Africa), published between 2001 and 2020 (SI Table  1 ; https://doi. org/10. 7910/DVN/H23MVL). The literature search was conducted until June 12, 2020 using Web of Science, Science Direct and Google Scholar using the search terms: site-specific nutrient management (SSNM), SSNM rice, SSNM maize, SSNM wheat, SSNM cereals, SSNM vs farmers' fertilizer practice (FFP). Studies included were peer reviewed journal publications, book chapters, or technical reports that explicitly compared SSNM and FFP in the same fields. Consequently, studies that compared SSNM to other treatments such as a no input control, blanket fertilizer recommendation, or soil test based recommendations were not included  (Dogbe et al.
, 2015; Islam et al. , 2018; Saito et al. , 2019; Rurinda et al. , 2020). Most of the studies reported in this study were conducted on-farm, and only five were conducted on-station. We only included studies that followed the generic SSNM approach described in at least steps 1 and 2 in Fig. 1   (Dobermann et al. , 2002 (Dobermann et al. , , 2004)). Studies were also rejected if the experimental method was not clearly written and when factors other than fertilizer management differed between SSNM and FFP treatments. By and large, agronomic practices other than nutrient inputs were the same or similar in SSNM and FFP treatments. If data from the same experiments were reported in multiple publications, a paper having data with the most complete dataset was used. Of the reviewed studies, 66, 20, and 14% were on rice, wheat, and maize, respectively; all conducted in Asia, except three papers on rice in Africa (SI Table  1 ).
Agronomic N use efficiency (AEN) was reported in 27 studies, all of which had included a N omission treatment to enable calculation of AEN according to Equation (1). Partial factor of productivity of N (PFP N) was calculated for all studies that reported total N fertilizer added (Equation (2)). AEN = (GY N (kg ha -1 ) -GY 0 (kg ha -1 ))/N rate (kg N ha -1 ) (1) PFP N = GY N (kg ha -1 )/ N rate (kg N ha -1 ) (2) Where GY N is the grain yield in a treatment with N application. GY 0 is the grain yield in a treatment without N application. Means for SSNM and FFP yields and AEN were retrieved from each study as well as the site characteristics including study location, study duration, soil properties and climatic conditions (SI Table  4 ). In few cases where yield and AEN data were only presented in figures, values were manually estimated from the figures.
From the publications, categorical variables were assigned to cropping system, crop species, cropping season, ecosystem (irrigated/rainfed), variety (inbred/hybrid), residue management, crop establishment, type of SSNM recommendation, and type of trial. Crop yield categories were derived from the quartiles of the FFP grain yields of the whole dataset (SI Table  2 ). Values of FFP yield category below 1 st quartile were set as low, values between 1 st and 3 rd quartiles were set as medium, and values above 3 rd quartile were set as high. In some studies, farmers were considered as replicates within the same locality, e. g. within a village. Replicates in individual studies were variable but with highest numbers being 323, 510 and 701 for rice, maize and wheat, respectively. Due to high spatial variability even for neighboring fields, nutrient rates among the fields varied and were reported as a range.
Economic analyses We calculated three economic performance metrics: total fertilizer cost (TFC), gross return, and gross return above fertilizer cost (GRF). For the cost-benefit calculations, we assumed that fertilizer sources were the most common products recorded in the database: urea (46-0-0) for N, DAP (18-46-0) for N and K, and MOP (0-0-60) for K. Fertilizer prices were estimated from the 10-year average across countries listed in the database (indexmundi, 2020) and reported as per unit of nutrient. Values used were US$ 0. 642 kg -1 N, US$ 2. 151 kg -1 P, and US$ 0. 633 kg -1 K. Farmgate prices of paddy rice, maize and wheat were derived from the trend of the market price for the past 25 years (indexmundi, 2020), with slight modifications based on consultations with an economist (Valerien Pede, IRRI personal communication).
Meta-analysis Grain yield, N fertilizer rates, number of N splits, AEN, PFPN and economic parameters were analyzed using MetaWin 2. 1 software  (Rosenburg et al. , 2000). The natural log of the response ratio (the ratio of SSNM to FFP) was used as the effect size in our meta-analysis in Equation (  6 ). In most of the studies, the within-study variance measures for mean yields were not reported. Thus, individual observations were weighted by replication  (Adams et al. , 1997) , as shown in Equation (  7 ). Effect size = ln (Xe/Xc) (6) where Xe is the mean data for SSNM while Xc is the mean data for FFP, which was used as the control. Weights = (N SSNM x N FFP )/(N SSNM + N FFP ) (7) where N SSNM and N FFP are the number of replicates for SSNM and FFP treatments, respectively. The potential for publication bias in the literature was tested to determine the impact of effect size of large studies as compared with small studies  (Borenstein et al.