dataesr/scientific-paragraphs-categorization huggingface dataset:
Genetic Clustering and Patterns of Reticulation The SplitsTree network (Fig. 3b ) mirrors the general pattern evident in the phylogenetic analyses. The branches within group SR and between SR and RR groups are significantly longer than those within the RR group, in a ratio more similar to the SNAPP results rather than for BI. The RR group has a reticulate history, indicative of hybridization and/or incomplete lineage sorting. Conflicting information is also apparent at the level of deeper relationships within the SR group. When including all individuals, Structure identifies two major clades (Fig. 2  Column a; Supplementary Fig. S3a , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247), roughly corresponding to the SR and RR groups. However, it shows the presence of potential admixture between the VOL.
65   and Table 1. two groups in D. veillonii and D. cherrieri (SRb subgroup). When run only on the subset of individuals from the RR group, Structure further separates three subgroups (Fig. 2  Column b; Supplementary Fig. S3b , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247) with few admixed individuals between them. We further used ancestry graphs implemented in TreeMix  (Pickrell and Pritchard 2012)  to identify patterns of divergence and migration within the RR group only, including D. cherrieri as an outgroup. The stepwise addition of migration events in the analysis resulted in an incremental, consistent inclusion of additional events up to the sixth migration. With the seventh migration, the events changed significantly, so we considered as final the result including six migrations (Supplementary Fig.
Patterns of Adaptive Divergence Lagrange analyses (Supplementary Fig. S5 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247) suggest the most recent common ancestor of all members of this Diospyros group that radiated on New Caledonia was most likely a generalist for the amount of humidity, and it might have occupied both ultramafic and volcanic substrates. The generalist lineage had specialist descendants with regard to climate type relatively early, resulting in large clades of extant taxa sharing the same preference for humidity. In contrast, edaphic specialists evolved in general more recently. Surprisingly, apart from multiple transitions from edaphic generalists to specialists, several shifts from specialists to generalists have also been inferred. In addition, specialists to particular substrates are often not each other's closest relatives, but they are rather related to sister taxa adapted to different substrates.
We have identified 50 RAD regions (GenBank accession numbers KT587203-KT587252) showing parallel patterns, including four DNA regions that have been found to be significantly different in three of the four pairs of species (see "Materials and Methods"). The majority of the identified SNPs (74. 6%) are not fixed differences between species, potentially indicative of polygenic adaptation, as expected for ecological traits  (Savolainen et al. 2013). Using the pairedend read information, we have assembled de novo larger DNA regions of between 390 and 909 bp for the 50 candidate loci and performed functional annotations for them. Only 26 of the loci received at least 1 blast hit, and 15 of them could be successfully annotated (Table  2 ). The combined graph analysis of Blast2Go and a Fisher's exact test against a reference group of RADseq loci indicates an enrichment for regions related to transport (i.
Its effects are visible in the SplitsTree network (Fig. 3b ) as reticulations, the Structure results (Supplementary Fig. S3 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247) as the presence of admixed individuals, and VOL. 65   and Knowles 2006; Glor 2010; Lerner et al. 2011). Therefore, we regard the latter process as less likely to have significantly affected, on a genome-wide scale, the phylogenetic patterns within this group, which radiated fairly recently after passing through the extreme bottleneck associated with arrival on New Caledonia  (Duangjai et al. 2009). The results obtained are also much too structured by taxon and geography to represent this sort of distortion of phylogenetic relationships. Early Divergence with Respect to Macrohabitat and Geographic Distance Grande Terre, the main island of New Caledonia, is split by a mountain range into humid southeastern (2000-4000 mm precipitation per year) and dry northwestern parts (1000 mm precipitation per year) with prevailing winds and rain coming from the southeast  (Maitrepierre 2012).
The inferred phylogenetic relationships and the analyses of niche evolution point to an initial, but fairly slow, divergence with respect to these climatic conditions along major clades (i. e. , the deep splits on the backbone of the phylogenetic tree; Fig. 2  Column c and Supplementary Fig. S5A , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247). Modeling studies have suggested that divergence with respect to the macrohabitat is indeed the first expected stage for rapid radiations  (Gavrilets and Losos 2009; Glor 2010) , although up to now the empirical evidence for this expectation was limited. In Diospyros, this appears to be the result of climatic specialization from a generalist lineage that, however, persists over several cladogenesis events (Supplementary Fig. S5A , available on Dryad at http://dx.
doi. org/10. 5061/dryad. 62247). This may argue against a significant contribution of climatic specialization to diversification in this particular group  (Fine et al. 2014). In Diospyros, this pattern could also be merely the result of allopatric differentiation, with isolation by distance promoted by a geographic barrier between the dry and wet areas that is difficult to cross. A limited dispersal potential of New Caledonian species of Diospyros is supported by the fact that most populations cluster separately within each species (see e. g. , the MP tree in Supplementary Fig. S1 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247, or the SplitsTree network in Fig. 3b ). Very little is known about pollinators and fruit dispersal in these species; most species are dioecious and have fleshy fruits.
On the other hand, D. cherrieri (SRb), D. minimifolia, Diospyros tridentata (both RRc), D. perplexa, and D. pustulata (both RRb) are found in sclerophyll forests on the dry western coast of Grande Terre. Especially when considering multispecies localities (e. g. , L10, L13, and L20; Table  1 ), sympatric species are only distantly related. In contrast, most sister species relationships seem to have been shaped by ecologically driven isolation, in particular with respect to edaphic specialization (Fig. 2  Column e). Indeed, we find a plenitude of independent edaphic shifts (Supplementary Fig. S5b , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247) that occur regionally (Fig. 2 Columns d  and e ), in the absence of obvious geographic barriers. In this context substrate specialization appears to have fostered diversification  (Fine et al.
S6 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247; Table  2 ); another important category plays a role in transport of elements within or between cells. As the New Caledonian soil types are different in heavy metal content and availability of minerals, these specific loci appear potentially adaptive and therefore involved in edaphic specialization, and not simply linked to regions under selection. It is, however, difficult to argue that this differentiation is responsible for particular speciation events, or if they have evolved subsequently  (Glor 2010). A similarly limited number of genomic regions on which positive selection might have acted has also been found in Hawaiian species of Schiedea  (Kapralov et al. 2013) , which exhibit, like the New Caledonian Diospyros species, great morphological and ecological variation.
The Effect of Introgression It is generally accepted that species divergence can occur in the face of persistent and genomewide admixture over long periods of time, as shown for example in Heliconius butterflies  (Martin et al. 2013 ). Furthermore, it has been proposed that the high speciation rates in radiations may be fuelled by frequent hybridization with transgressive segregation (e. g. ,  Seehausen 2004; Gavrilets and Losos 2009; Glor 2010; Losos 2010; Keller et al. 2013). The SplitsTree and Structure analyses provide evidence for considerable admixture among the Diospyros species (Fig. 3b  and Supplementary Fig. S3 , available on Dryad at http://dx. doi. org/10. 5061/dryad. 62247). The grouping of different populations of the nonmonophyletic species like D. minimifolia and D. parviflora has some relationship to provenance.
The identification number of sampling localities are following Figure 1. b Voucher-Codes: JMXXXX: collection number J. Munzinger; Tree No. XXXXX: Tree of New Caledonian Plant Inventory and Permanent Plot Network (NC-PIPPN, Ibanez et al. 2014); NOUXXXXXXX: Herbarium accession number of Noumea herbarium (NOU); WUXXXXXX: Herbarium accession number of the Herbarium of the University Vienna (WU); P: Herbarium of the Natural History Museum Paris; MPU: Herbarium of the University of Montpellier.
ACKNOWLEDGMENTS We thank  G. Schneeweiss ,  F. Balao ,  S. Duangjai ,  F. E. Anderson ,  S. Renner , and two anonymous reviewers for their contribution to the research presented here. lllumina sequencing was performed at the  CSF NGS Unit, Vienna  (http://csf. ac. at/facilities/next-generation-sequencing/; last accessed October 15, 2015). Specimens are deposited in the herbaria of  Noumea (NOU) ,  University of Montpellier II (MPU) , and the  University of Vienna (WU) , staff of these herbaria are acknowledged for various help. Collecting permits were provided by  Environmental Services of North and South Provinces of New Caledonia.
a The identification number of sampling localities are following Figure 1. b Voucher-Codes: JMXXXX: collection number J. Munzinger; Tree No. XXXXX: Tree of New Caledonian Plant Inventory and Permanent Plot Network (NC-PIPPN, Ibanez et al. 2014); NOUXXXXXXX: Herbarium accession number of Noumea herbarium (NOU); WUXXXXXX: Herbarium accession number of the Herbarium of the University Vienna (WU); P: Herbarium of the Natural History Museum Paris; MPU: Herbarium of the University of Montpellier.
Fresh water is considered one of the most important renewable natural resources in the world. Among all the countries, Colombia is one of the places with the highest water supply, and has five watersheds: the Caribbean, Orinoco, Amazon, Pacific and Catatumbo. It is therefore vital to study and evaluate the water quality of the rivers and/ or lotic systems. In recent studies, some scientists made use of biological indices to calculate water quality, while others detected water quality through machine learning techniques. However, these studies do not allow users to easily interpret the results. These investigations motivated us to propose a dataset for generating water quality alerts in Piedras river basin based on the analysis of the K-Means clustering algorithm and C. 4. 5 classification technique.
B. Background Clustering algorithms are among the unsupervised learning methods, which divide a dataset into a number of groups, so that the elements (observations) of a same group are homoge-   Castillo, E. , Gonzales, W. , López, I. , Figueroa, A. , Corrales, D. , Hoyos, M. & Corrales, J. (2015). neous (similar)  (Gurrutxaga, Muguerza, Arbelaitz, Pérez, & Martín, 2011; Arbelaitz, Gurrutxaga, Muguerza, Pérez, & Perona, 2013). Several authors  (Lin & Chen, 2005; Gan, Ma & Wu, 2007)  have classified clustering algorithms into three types, which are presented below.
Partitional algorithms Partitional algorithms assume in advance the number of non-overlapping groups that the dataset should be divided into, determining a split into groups or classes that ensures that an observation belongs to exactly one group  (Gan et al. , 2007; Arbelaitz et al. , 2013). The most commonly used search algorithms are: K-Means and K-Medoids. K-Means defines k centroids (one for each group) and then takes each element of the data set in order to locate the nearest centroid. It recalculates the centroid of each group and redistributes each element of the data set using the same criteria  (Velmurugan & Santhanam, 2010). The K-Means algorithm takes the average value of the objects in a group as a reference point (seed), indicating that the centroids do not need to match the values of the objects in the cluster to which they belong.
Hierarchical algorithms These algorithms establish a hierarchical structure as a result of grouping  (Theodoridis, Pikrakis, Koutroumbas, & Cavouras, 2010). Such techniques decompose the dataset into levels or stages, such that on each level (agglomerative) or bind divide (partition) the previous level groups (  Pang-Ning, Steinbach, & Kumar, 2006; Theodoridis et al. , 2010). The agglomerative analysis method starts with as many groups as there are observations in the dataset. From here, new groups are formed until the end of the process, when all the cases treated are contained in a single group, while the dividing technique is the reverse process, obtaining at the end of the process as many groups as cases have been treated. It should be noted that the hierarchical algorithms used in the environmental sector are actually of the agglomerative type  (Moreno, 2000)  and they are much more understandable (easy to interpret) and effective (less complex) than divisive algorithms, since in the first group, merging the groups corresponds to a high degree of similarity, thus making them more comprehensible,.
Density-based algorithms This class of algorithms divides the dataset into groups, taking into account the density distribution of the elements, so that the groups are formed having a high density of points inside. Among the algorithms used in this area is DBSCAN (Density-Based Spatial Clustering of Applications with Noise), which groups the observations of a dataset into regions of high and low density. Initially, the algorithm defines a set of central objects (objects that have in their neighborhood an amount greater than or equal to a specific threshold point), edge or boundary objects (objects that have in their neighborhood a number of points less than a specified threshold, but which are in the vicinity of a central object) and atypical objects or noise (objects that do not fall into the above categories).
Once the objects are defined, DBSCAN selects a p arbitrary element; if p is a central object, a group in which are located all observations density-reachable p is built. If p is not a central object, another element of the dataset is visited. The process continues until all elements have been assigned to an object. Those outside the groups that have formed objects are called outliers or noise points, while items that are not atypical or core values are called edge points  (Gan et al. , 2007; Pang-Ning et al. , 2006). It is noteworthy that the DBSCAN technique can handle groups of different shapes and sizes, but has limitations when the dataset has tied dimensions, when groups are overlapping, and in the presence sentativas son: la mínima distancia (Enlace Simple), la máxima distancia (Enlace Completo) y la distancia promedio (Enlace Promedio)  (Madhulatha, 2012; Pang-Ning et al.
A. Resultados experimentales Se llevó a cabo una evaluación experimental para determinar el comportamiento de los métodos de agrupación jerárquica y particional aplicados en diferentes conjuntos de datos (Tabla 2), usando solo índices de grupos de validación (IVG) debido a la ausencia de la partición correcta. Los IVG se basan principalmente en el concepto de cohesión del conjunto y el espacio entre ellos  (Pang-Ning et al. , 2006). Las medidas de cohesión determinan el grado de relación u homogeneidad de objetos en un grupo (pertenencia), mientras que la segregación determina Castillo, E. , Gonzales, W. , López, I. , Figueroa, A. ,  Corrales, D. , Hoyos, M. & Corrales, J. (2015). of noise  (González, 2010). Therefore, in this research this technique is not taken into account because of the datasets involved.
A. Experimental results An experimental evaluation was conducted to determine the performance of the methods of hierarchical and partitional clustering applied on different data sets (Table  2 ), using only the Indexes Validation Group (IVG) due to the absence of the correct partition. The IVG is mostly based on the concept of cohesion of the assembly and the spacing between them (Pang-  Ning et al. , 2006). Cohesion measures determine the degree of relationship or homogeneity of objects in a group (membership), while the segregation determines the degree of separation between the groups (non-membership). For the experiments, four datasets derived from the Piedras river database were used, which contain information on the physicochemical and biological samples collected. It should be mentioned that such referrals in the dataset were made taking into account the reduction technique attributes of Principal Component Analysis (PCA), which aims to select the most appropriate subset of features of the original dataset, while discarding redundant attributes (correlated), and any that are inconsistent, irrelevant, etc.
Como se menciono, un valor máximo de la figura del índice Silueta y un valor mínimo del índice DB representan la mejor partición, y por lo tanto, el valor óptimo de K. Del mismo modo, en la Figura 2 el número óptimo de grupos para el método K-Means se encuentra entre 5 y 6 grupos, mientras que para K-Medoids corresponde a 7 grupos. En este caso, las medidas para definir el número apropiado de grupos son mejores para K-Medoids en comparación con K-Means, desde que los valores del índice DB coinciden con el índice Silueta y K=7. Water quality warnings based on cluster analysis in Colombian river basins. Sistemas & Telemática, 13(33), 9-26. cribed in the previous section is evaluated. In the same way, note that this procedure was performed for the four datasets described in Table  2. Figure  2  describes the behavior of the results of the K-Means algorithm (Figure 2a) and K-Medoids (Figure  2b ) respectively on the dataset DS0, where the blue color symbolizes the DB index (based on the similarity measure between two groups denoting the minimum value and the best partition) and red refers to the Silhouette index (combining both cohesion and separation, how greater the Silhouette, more compact and separate the groups, i.
Means obtenido con la mayor precisión percibida en número hace referencia a una agrupación óptima (K=3) versus el algoritmo K-Medoids. Estos resultados indican que los conjuntos de datos DS1 y DS2 son muy similares. La Figura 5 muestra los resultados del conjunto de datos DS3, donde se obtienen resultados similares a DS0. A partir de aquí se puede asumir que existe información redundante entre los conjuntos de datos DS0-DS3 y DS1-DS2. Por tanto, se puede decir que DS3 representa la misma información que DS0, a pesar de que el primero posee menos atributos. Lo mis-and 6 groups, while for the K-Medoids it is 7 groups. In this case, measures to define the appropriate number of groups are better for K-Medoids than for K-Means, since the values of DB agree with Silhouette and K = 7. Similarly, the algorithms' behavior in question applied to the dataset of DS1 (Figure  3 ) is analyzed.
In Figure  2  (a) and (b), note that the results are reversed compared to the previous case; that is, K-Means acquires accurately the appropriate number of groups (K=3), while K-Medoids delivers two possibilities for this value (K=3 or K=4). Now, Figure  4  shows the results obtained when the dataset DS2 is evaluated on K-Means and K-Medoids. Here the same behavior of one data record (DS1) is perceived, wherein the K-Means partitional method obtained accurately perceived in number refers to the op-mo ocurre con DS1 vs. DS2. Este comportamiento indica que las bases de datos DS0 y DS2 podrían estar representadas por DS1 y DS3, respectivamente. A continuación, se resumen los mejores valores de los índices Silueta y DB (mínimo y máximo respectivamente) obtenidos por los algoritmos K-Means y K-Medoids en cada una de las bases de datos.
Sistemas & Telemática, 13(33), [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26]  timal clustering (K=3) versus the K-Medoids algorithm. These results indicate that the DS1 and DS2 datasets are very similar. However, Figure  5  shows the results of the DS3 dataset, where similar results are obtained to those for DS0. From this it is possible to assume that there is redundant information between datasets DS0-DS3 and DS1-DS2. In other words, we can say that DS3 represents the same information as DS0, even though the former has fewer attributes. The same goes for DS1 vs. DS2. This behavior indicates that the datasets DS0 and DS2 can be represented by DS1 and DS3 respectively. Below are summarized the best values of the Silhouette and DB indices (minimum and maximum respectively) obtained by the K-Means algorithm and K-Medoids on each of the datasets.
The best results are obtained when the K-Means algorithm and K-Medoids process data set DS1, because they obtain the minimum values of DB (0. 458 and 1. 101 respectively) and the highest values of Silhouette (0. 773 and 0. 526 respectively). From these results, K-Means reaches the best quality for the DS1 clustering process with K=3, while K-Medoids for DS1 reaches K=3 or K=4. Given these criteria, it can be concluded that the optimal number of groups is K=3 for both cases. Significantly, K-Means, unlike K-Medoids, presents the best values for validation of the DB and Silhouette indexes, the minimum and maximum respectively.
Performance analysis of hierarchical algorithms In this item are evaluated single, complete and average linkage algorithms. The performance analysis was performed for each dataset defined in Table  2 , based on the CCC and Silhouette indices which measure the degree of distortion, and the respective optimal number of clusters. Figure  6  show the behavior of these algorithms for different numbers of clusters (3-20). As clearly noted, all the algorithms have the best values in Silhouette when grouping the data set K=3, obtaining the same optimum number of clusters as the partitional methods. Single Linkage presents the best results for Silhouette compared with Complete and Average Linkage (0. 8, 0. 88, 0. 88, 0. 8 respectively) on the four datasets analyzed. If these results are analyzed in detail, it is noted that the behavior of the datasets is the same as is obtained from para cada conjunto de datos definidos en la Tabla 2, basados en los índices CCC y Silueta con medidas del grado de distorsión, y el número óptimo de agrupaciones de manera respectiva.
Considering this approach, it appears that the best datasets grouped were DS1 and DS2, which obtained the best values of CCC (0. 681 and 0. 69, respectively). However, considering that the greatest value of Silhouette was achieved when the dataset DS1 was grouped, for    (Corrales, Corrales, & Figueroa-Casas, 2015; Pérez, 2003). La Figura 7 muestra el árbol de decisión resultante. El conjunto de datos DS1 consiste en un total de 5590 individuos macroinvertebrados acuáticos, pertenecientes a 63 taxones, además de propiedades como año, mes, código de estación y número de individuos, como lo indica la Tabla 2. Ahora, usando la técnica de agrupación K-Means se dividió el conjunto de datos en tres grupos donde cada uno es interpretado a través del árbol de decisión C. 4. 5, ya descrito. De este modo, that reason DS1 is considered the best dataset, corroborating the results obtained by the partitional algorithm.