dataesr/scientific-paragraphs-categorization huggingface dataset:
Among them are balanced accuracy, macro F 1 score and their weighted versions  45. To validate our model using unseen data, we prepared an extract from the PLU of Strasbourg, another rapidly developing city in France, which is not included in the main corpus. Preliminary results on that data (i. e. PLU de l'Eurométropole de Strasbourg, CHAPITRE XIV-XVIII, XXIV) demonstrated good performance in general (overall accuracy 81%), but revealed that the Verifiable class has to be investigated in more detail. The lack of examples of this type introduces additional challenges to the learning process. Therefore, we would suggest trying to balance the corpus by artificially generating more examples for underrepresented classes and to perform a cascade classification with separate classifiers by Pertinent and Not pertinent, Strict and Informative, finally Verifiable and Non-verifiable classes.
We intend to explore it in future work. (c) Manually pre-process the documents as it is decribed in Methods (annotation details) and Data records (data formats). To skip this part download Corpus_Manual_Annotation_Consolidated_Version. zip from the corpus repository, extract it to the same directory with the code. (d) Construct label segments from manually annotated documents using segment_construction. py module. To skip this part download Corpus_Extracted_Segments_Consolidated_Version. zip from the corpus repository, extract it to the same directory with the code. (e) Prepare label segments for learning by using data_loader. py. (f) Use segment_classification. py for learning and validation. For detailed examples please refer to the README file provided with the code.
Environmental variables All environmental variables used for the SDM were downloaded in 2. 5 arc minutes resolution from WorldClim version 2. 1 (Coupled Model Intercomparison Project Phase 6 (CMIP6))  (Fick and Hijmans 2017). WorldClim provides data for 19 bioclimatic variables, which are derived from temperature and rainfall data and hence constitute biologically meaningful indicators for species distribution. Global data for the bioclimatic variables is available for past, present, and future climate scenarios  (Fick and Hijmans 2017). Current bioclimatic variables are based on the averages of the years 1970-2000, whereas future bioclimatic variables are derived from extrapolations and comprise the averages of the 20-year periods  2021 -2040 , 2041 -2060 , 2061 -2080 , and 2081 -2100 (Fick and Hijmans 2017).
Future bioclimatic data is available for different General Circulation Models (GCMs) and Shared Socioeconomic Pathways (SSPs). Since the distribution of stem borers is altitude-dependent  (Cugala and Omwega 2001; Mwalusepo et al. 2015a) , elevation was included as an additional environmental variable in this study. Elevation data was also retrieved from WorldClim and is based on Shuttle Radar Topography Mission (SRTM) elevation data  (Fick and Hijmans 2017). For a description of the environmental variables used for modeling, check the supplementary materials. For the projection of the SDMs onto future climatic conditions, the bioclimatic variables were downloaded for three selected GCMs (CanESM5, CNRM-CM6-1, and MIROC6) that have been found to appropriately describe future climate in East Africa.
Distribution modeling approach The open-source software Maxent for modeling species niches and distributions is one of the most popular tools used among experts. We modeled the species distribution with Maxent using the "kuenm" package  (Cobos et al. 2019)   in R. Maxent applies a machine-learning technique for maximum entropy modeling  (Phillips et al. 2006). It uses a collection of georeferenced presence-only records of a species and a set of relevant environmental variables  (Phillips et al. 2006). Maxent builds a model based on the distribution of maximum entropy and a set of constraints provided by the environmental variables that predicts habitat suitability for each grid cell  (Phillips et al. 2006). Each cell in the study area is assigned a probability value between 0 and 1 that categorizes habitat suitability, with 0 indicating no habitat suitability and 1 marking cells that provide a perfectly suitable habitat  (Phillips et al.
Estimation of yield loss and BC efficacy Using the distribution data of the three stem borer species, associated maize yield losses were calculated. Data on maize yields in the study region were downloaded from the Map-SPAM data center (IFPRI 2020) (see supplementary materials). Maize yield losses in the study area were quantified via the AFERIA household survey that assessed data from smallholder households in 12 villages in Taita Hills and the southeastern slope of Mount Kilimanjaro. Taita Hills are situated in southeastern Kenya, at an elevation ranging from 700 to 2000 m above sea level (m. a. s. l), between latitude 3°25′ S and longitude 38°20′ E. The southeastern slope of Mount Kilimanjaro is located in northeast Tanzania in the Pangani River Basin ranging from 700 to 1800 m. a. s. l, between latitude 3°4′ S and longitude 37°4′ E.
A sample of more than 700,000 presence coordinates of different insect species  (Phillips et al. 2009 ) recorded on the African continent was downloaded from GBIF. Based on these records, a kernel density estimate map was constructed using the "MASS" package  (Venables and Ripley 2002)  in R and used to correct for potential sampling bias in the modeling process  (compare Biber-Freudenberger et al. 2016; Fourcade et al. 2014).
Model calibration and best model selection Apart from the inputs required to run Maxent, a wide variety of parameter settings can be individually adjusted to calibrate the best-performing model. For almost all parameters, in-built default settings have been validated by the developers of the software over a wide range of models. However,  Morales et al. (2017)  and  Merow et al. (2013)  indicate that the adoption of Maxent's default settings may not necessarily produce the optimal species distribution model. According to  Radosavljevic and Anderson (2014) , the individual tuning of setting parameters for each species can lead to better performing models. In order to find the best-performing model for predicting habitat suitability for the species under current environmental conditions, we calibrated multiple models with different settings and inputs using the "kuenm" package  (Cobos et al.
Habitat suitability under current and future climatic conditions The mean area under the ROC curve (AUC) values of training data (over 15 replicate model runs) for all five species are above 0. 98 (supplementary materials) which confirms excellent performance of our models. The probability of habitat suitability was modeled for each species for current climatic conditions and different CC scenarios. The current habitat suitability in the study area and predicted future suitability for the period 2081-2100 are shown in Fig. 1. Probability of habitat suitability ranges between 0 (low probability of suitable habitat) and 1 (perfectly suitable habitat). Grid cells with great suitability carry high probability values and are displayed in dark red, whereas cells with low suitability show low probability values and are colored in lighter red and white mitigation and adaption is contrasted with the worst-case scenario SSP5-8.
Our study, however, included elevation as homologous to the bioclimatic variables into model calibration and does not distinguish modeling outputs based on elevation but draws conclusions on country level. However, dynamically downscaled regional climate models (RCMs), as used by  Mwalusepo et al. (2015a) , provide data at a finer resolution and capture mesoclimatic dynamics more accurately which potentially results in better simulations of regional climate than coarse resolution GCMs  (Beaumont et al. 2008; Giorgi 2019). In our study, we explicitly used data for the bioclimatic variables in 2. 5 arc minutes resolution which is based on the latest version of GCMs from CMIP6. This approach is reasonable when examining the effects of climatic changes, i. e. , changes in temperature and precipitation, on species distribution.
Data availability Presence records used for this research constitute a collection of records downloaded from GBIF (https:// www. gbif. org/) and data points provided through various projects by icipe. All presence records are available in a. csv in the supplementary materials. Data for relevant environmental variables used for species distribution modeling, i. e. , bioclimatic variables and elevation, can freely be accessed and downloaded from WorldClim (https:// www. world clim. org/). All modeling outputs of this research, i. e. , predictions on habitat suitability, distribution, maize yield losses by stem borers, and estimated potential of biological control to reduce stem borer-associated maize yield losses, can be accessed in TIF format through the ZEF Data Portal (https:// daten. zef.
This clustering approach is non-linear. It can therefore be used to analyze the overall biodiversity effect of a microbial community. Compared with a linear approach, this non-linear approach allows for the inference of the effect of interactions among species on community properties. Here, we aim to identify microbial taxa that predominantly contribute to the generation of PE in Malagasy Ferralsols, either by their own activities or through their interactions with other microbial taxa. Our approach is based on the comparison between linear and non-linear analyses of the same dataset. Three soil properties associated with SOM decomposition activity were analyzed: basal soil respiration, mineralization of 13 C labeled wheat straw supplied to soil, and the PE generated by the straw supplied  [13].
Dataset The dataset used here was already partly published by  [13]. Ferralsols from the Highlands of Madagascar were collected (May 2014, 57 sites) under herbaceous savannah dominated by the Poaceae Aristida sp. , along gradients of temperature and rainfall. At each site, five soil cores were sampled from 0-10 cm depth and mixed to form a composite soil sample. The 57 composite soil samples were sieved to 2 mm, stored at 4 • C, then sent to the Genosol platform in Dijon, France. A 50 g aliquot of each composite soil sample was subsampled, then freeze-dried prior to genetic analysis. Soils have also been characterized as fully described in  [13]  and characteristics can be consulted in Table  S1. Bacterial and fungal community compositions were assessed on 57 soil samples. Banks of 16S and 18S ribosomal sequences were prepared prior to pyrosequencing analyses as fully described in  [14].
Pyrosequencing was carried out on a GS FLX Titanium (Roche 454 Sequencing System). Biocomputing analysis of the sequences was performed using the GnS-PIPE  [15]  as described in detail in Tardy et al. [16]. In order to compare the datasets efficiently and avoid biased community comparisons, the sample reads were reduced by random selection closed to the lowest datasets (3000 reads for 16S-and 18S-rRNA gene sequences, respectively, for each soil sample). The retained high-quality reads were used for taxonomy-based analysis using similarity approaches and dedicated reference databases from SILVA  [17]. Assignments published in Razanamalala et al. [13]  were made at the highest taxonomic level, i. e. , the phylum level. However, in the present study, we preferred to perform assignments at a more functionally relevant level, i.
e. , the family level. Actually, we tested the class, order, and family phylogenetic levels, and family appeared as the best compromise to improve functional inference without drastically reducing the dataset due to the lack of known species and even genera in the SILVA database. In order to compare this work with that of Razanamalala et al. [13] , the links between families and phyla are given in Table  S2. The relative abundance matrices had a dimension of n = 57 soils for p = 60 bacterial or fungal families, respectively. This number of families was chosen in order to be at the limit of the recommendations of the linear approaches in terms of the number of variables, which must be lower compared with the number of samples (see Section 2. 2. 3). Relative abundances per soil aliquot ranged from 1 to 1486, and from 5 to 1048, out of 3000 sequences, with a median of 4.
Statistical Computations All computations were performed using R-software  [19]. Linear and multi-linear regressions were performed using the lm function, and variance analyses using the aov function of the R-package stats. Clustering analyses were performed using the fclust function of the R-package functClust. Backward stepwise selections were performed using the lm_step and fclust_step functions. Comparison of medians was performed using a nonparametric Kruskal-Wallis test (function kruskal. test of the R-package stats). All statistical tests were performed at the probability threshold of p < 0. 001. All scripts are available on the INRAE dataverse at https://doi. org/10. 15454/LRY3M2 (accessed on 20 March 2023).
This indicates that multi-linear and clustering analyses modeled different aspects of the relationship between microbial families of initial soil communities and SOM decomposition, and this difference was stronger for fungi than for bacteria. The clustering analyses of bacterial and fungal families gathered together in the same dataset confirmed the results (Figures  S15-S20 , summarized in Table  3 ). The variable selection procedure retained less than 14% of total families for a given SOM decomposition function and a given incubation duration, and also rejected 44% of total families showing no relationship for any SOM decomposition function at any incubation duration. This indicates that a small number of microbial families of initial soil communities explained a large part of SOM decomposition after seven and 42 days of incubation.
Identity of Potentially Key Bacterial and Fungal Controlling the Priming Effect The main results of the clustering analyses of bacterial and fungal families gathered together in the same dataset are reported in Figures  1 2 3. Figure  1  shows the clustering of relative abundance classes of the microbial families of initial soil communities identified as key for basal soil respiration (Figure  1a ,b, see Figures  S15a  and S16a ) and straw mineralization (Figure  1c, d , see Figures  S17a  and S18a ) after seven and 42 days of incubation. Figures  2  and 3  report the clustering analyses of key bacterial and fungal families for the priming effect after seven and 42 days of incubation, respectively (see Figures  S19  and S20 ). A midpoint (•) indicates that the family was identified as key by the variable selection procedure and (***) indicates that the significance was at p < 0.
Enrichment of the databases should gradually reduce the size of this group in future studies. It has recently been suggested that PE could also be generated by the release of molecules with a high affinity for soil minerals, making it possible to desorb the initially associated labile organic matter rich in N and P  [5]. Representatives of Burkholderiaceae are able to weather minerals by the release of gluconic acid  [43]. However, in our study, Burkholderiaceae have a strong inhibitory role on PE after 42 days of incubation, linked to a density effect (their lowest and highest abundance classes are found in functional groups C and D). At the same time, they have an inhibiting role in straw mineralization. Burkholderiaceae have other capacities, such as exopolysaccharide production, antifungal release, and organic and inorganic p solubilization via siderophores production  [44, 45].
Conclusions The clustering approach makes it possible to highlight taxa with non-linear effects on a flow resulting from complex processes, such as the priming effect, but also the effect on this flow of interactions between certain taxa. This approach has the advantage of not being constrained by the ratio between the number of variables and the number of samples, unlike linear statistical approaches. Normally applied to occurrence matrices, it has been adapted here to an abundance matrix by dividing each taxon into one, two or three abundance classes according to their respective densities. The results obtained are surprising and open up new challenges for their interpretation. This approach has proven its interest and deserves to be applied to more recent datasets resulting from sequencing techniques and more advanced bioinformatics analysis, which could only improve the quality and precision of the results.
Data Availability Statement: DNA sequences were deposited in the European Nucleotide Archive, under the study accession number PRJEB19651. The package functClust is available on R-CRAN at https://CRAN. R-project. org/package=functClust (accessed on 20 March 2023). The datasets and R-codes used will be available on the INRAE dataverse at https://doi. org/10. 15454/LRY3M2 (accessed on 20 March 2023).
Two additional clusters are the circum-Aus (cA) ecotype and circum-Basmati (cB) group  (Wang et al. 2018a, b) , localized along the Himalayan foothills. Recent studies using data from 3000 genomes in addition to 178 newly resequenced landrace accessions refined the description of diversity within Indica and Japonica and revealed steps and routes for the evolution and dissemination of both cultivar groups during their dissemination from the centres of domestication and diversification within Asia  (Gutaker et al. 2020). While this classification appears valid for O. sativa varieties worldwide, there are numerous regions where several types coexist and where hybridization is likely to occur among them. By accessing to the results of the 3 K-RG rice genome resequencing project, we could work on a massive dataset on sequence diversity.
As can be seen in the paper presenting this dataset  (Wang et al. 2018a, b) , there are numerous approaches to query such a large amount of data. In this work, we addressed the expansion of Asian rice to upland cultivation areas in Africa. We observed a reduction of diversity considering the Japonica component of the genome, but we also found evidence of specific introgression, coming from cAus, which produced novel combinations that have not been observed in Asia. We attempted to assess the contribution to local adaptation of a particular introgression of cA on the q arm of chromosome 6 using genomic selection signature analysis. We pinpointed a non-exhaustive list of candidate genes enriched in functions related to the response to abiotic stress (drought) in the introgressed region of cA.
We subdivided the genome into 379 blocks. The genome-wide mean value is D = 0. 039, which is not very far from D = 0 (null hypothesis). The associated Z-score value is 0. 56, which means that D is not significantly different from 0 (D is significant when Z is less than -3 or greater than 3). As a result, we cannot conclude that gene flow occurred between cA and GJ-trp at the genome-wide level. We quantified the admixture proportion (f ), from the P3 population (cA) to the P2 population (African GJ-trp accessions), following the procedure described by S. Martin(https:// github. com/ simon hmart in/ tutor ials/ blob/ master/ ABBA_ BABA_ whole_ genome/ README. md). Consistent with the ideogram plots, the percentage of cA genome in the African GJ-trp found is 0. 22%. Looking at each chromosome independently (Fig.
Origin of the Introgression We investigated the diversity in and around the main introgressions in order to derive information on the origin and the process that led to the observed patterns. We focussed on the main two cA introgressions in African Japonica accessions on chromosome 6. For the larger one (Int1), we first analysed the first and last 100 kb segments (17. 9-18. 0 Mb 21. 6-21. 7 Mb, respectively) and verified that the clustering patterns assemble the Int1-bearing African upland accessions with cA accessions (Additional file 1: Fig. S15 ). We then extracted polymorphism data within the 3. 8 Mb, between 17. 9 and 21. 7 Mb on chromosome 6 and compared the African upland GJ-trp that bear the introgression with the cA accessions. The African upland accessions that bear the full length Int1 all cluster on one clade in the resulting tree, whereas the two accessions with the smaller cA Int1 fragment (Additional file 1: Fig.
Discussion With this study, we made use of the reference dataset constituted by the 3 K rice genomes to investigate the expansion of O. sativa rice in Africa, with a focus on the GJ-trp subgroup of African upland rice, which are particularly well suited to drought-prone environments and are widely used as parents in breeding programmes  (Saito et al. 2018). Using a PCA analysis and the ADMIXTURE software to infer global population genetic structure, we recovered the well-known three-pole distinction between the Japonica, Indica, and cA. The African accessions show few representatives of the South Asian groups cA and cBas and an over-representation of the tropical forms of Japonica. Madagascar, as a potential bridge between Asia and Africa, has mainly Indica and Japonica varieties with, however, slight differences due to the absence of some of the Asian sub-groups (notably XI-1A, GJtmp and GJ-sbtrp) and to the existence of a specific local group, named Rojo, derived from Indica -Japonica-cA introgression, already documented  (Ahmadi et al.
sdsta te. edu/ go database. The representation is limited to the most significant GO terms. Word cloud displaying the most frequent GO terms related to the respective categories of biological process b and molecular function d. The representativeness of a word is defined by its character size of the introgression. Note however that cA is not frequent in Madagascar either. To determine whether this introgressions could be adaptive, we carried out genomic scans throughout the whole of chromosome 6 and we determined which locations are most likely to be subject to positive selection using the iHS statistic. The cA introgression regions on chromosome 6, which globally correspond to less than one fifth of the chromosome, harbour half of the 18 positive selection signals, including the strongest one.
Genetic Material and Data Source We used data from the 3 K-RG as a core dataset (The 3000 rice genomes project, 2014) available at https:// snp-seek. irri. org/_ downl oad. zul. The metadata we used to match this dataset comes from  (Wang et al. 2018a, b). The 3 K-RG were sampled from 89 countries, with 77. 2% of the accessions originating from Africa and Asia (Fig. 1a ). We selected the whole set of accessions available from Africa (n = 258), which were sampled in 25 countries. From Asia we only selected accessions representing traditional and promising material (based on metadata), i. e. we excluded improved varieties, which could have been influenced by the selection process like hybridization events between rice genetic groups. This resulted in a set of 2452 accessions originating from 21 Asian countries (Additional file 2: Table  S9 ).
Along the same line, we selected four improved varieties, NERICA 1, NERICA 2, NERICA 8, and NERICA 9, expected to be hybrid derivatives between an O. sativa Japonica and O. glaberrima  (Saito et al. 2018) , to serve as a control for gene flow inference in our analyses. This 3 K-RG sequencing project offered over 29 million bi-allelic markers in alignment with the Nipponbare reference genome. The genome sequences average 16X and 29X sequencing depth for African and Asian accessions, respectively. For our dataset (n = 2710), the mean coverage for accessions sampled in Africa is 90% compared to 93% for Asian accessions. The sequencing data, therefore, have a high quality according to these two parameters. A total of 10,459,872 bi-allelic SNPs was obtained after removing loci with more than 1% missing data.