dataesr/scientific-paragraphs-categorization huggingface dataset:
METHODS OF SELECTION AND CLUSTERING This part of work has been developed with the MatLab software. The first step is to import the point cloud. We have decided to transform the listing from. ply to table. The. ply format consists of a haeder with specific information of the variables contained in the columns, followed by the point listing and the corresponding values. The first elaboration was the visualization of the point cloud: the generation is slow and difficult to manage; the interaction almost impossible. Given the difficulty of displaying, at the end of each process, we decided to convert the results of operations tested to. ply format. To do this, it was necessary: to save the cloud obtained in. txt; to create a header. mat in which change from time to time, automatically, the number of dots; then to convert it in.
However, not being an exact, but an estimate calculation, some approximations have to be accepted. These approximations prove to be technically adequate and not incisive for the purposes of the goodness of the result: • the first is about the unit surface area, from quadric to planar; • the second depends of the decimation algorithms of the point cloud. Greater the density of the point cloud, smaller the error made in the first approximation. In addition, we need to subtract the half area of the boundary point to refine the calculation. From the test about the different algorithms and software, it shows that the best algorithm uniform decimation, useful to the calculation of the area as described, is within the commercial software, Geomagic. A first test shows that the error is compatible with the decimation, the density and the characteristics of the point cloud.
One of the advantages of this methodology is the choice of operating on the point cloud, which allows splitting the phases of analysis and the data mining from the acquisition technique used. In addition, this work shows that if we work during the data processing we can transform the point cloud into an enriched database: with the implementation software, the use, the management and the data mining is easy, fast and effective for everyone involved in the process of restoration, even if not expert of 3D data handling. (Figure  25 ).
ACKNOWLEDGEMENTS Thanks to  Amélie Bernard  for the maps on figures 1-2-3, created for the master's thesis in " Réhabilitation et Sauvegarde du Patrimoine Architectural ", entitled " Etude de l'église de Caromb: Recherches documentaires et analyse cartographique ", in collaboration with  CICRCP Centre Interdisciplinaire de Conservation et de Restauration du Patrimoine, e l'Université de Nice Sophia Antipolis. We also would like to thank  Philippe Bromley , responsible for the Caromb's  Church Case Study  within the  ANR   MONUMENTUM  project.
Materials The Marine Electronics Model 1512 pipe profiling sonar device is designed to scan filled pipes. The probe emits acoustic waves (2 MHz) and records the strength and the time of the echoes. The device includes three sensors: a transducer driven by a motor (i. e. the sonar itself ) and pitch and roll sensors. The device is connected to a cable providing electrical power and information exchange with a USB control box. A laptop is required to run the ME 1512 system software. In order to obtain a cross-section sewer sediments profile, four steps are necessary: (3) Profiling. Three algorithms are provided by the manufacturer (Marine Electronics 2012), named respectively the maximum, the ¾ maximum and the area algorithms, in order to identify, among the 250 points of each sector, the point corresponding to the water-sediment interface: a) the maximum: for each sector, the algorithm searches the closest point P1 from the sonar with the maximum intensity; b) the ¾ maximum: the algorithm searches the point P1 like the maximum algorithm and then selects the first point located between P1 and the sonar which presents a signal intensity equal to ¾ of the maximum intensity; c) the area: this algorithm selects the peak with the maximum total energy (a peak begins when intensity goes above a threshold and finishes when it returns below it).
(  4 ) Recording. Processed data can be recorded as. img files. Three types are available (A, B and C), with increasing levels of detail. Type C has been chosen for this study. (1) Scanning. The cross-section profile is scanned by internal rotation of the transducer. 400 sectors (angle of 0. 9°) are measured to cover a full 360° rotation. Each sector includes 250 points equally distributed along the ultrasound beam ranging from 125 to 6000 mm (i. e. the distance between two points then varies from 0. 5 mm to 24 mm). The pulse duration could be set from 4 to 20 μs. The sonar also measures roll and pitch angles: recorded images are corrected for the roll but not for the pitch (Marine Electronics 2012). (2) Displaying. Raw data images are displayed using a 256 colour scale, accounting for the roll/pitch position of the sonar (Figure  1 , left).
The user can process the raw data before the next step with four different parameters: threshold (filters out data below a given intensity), maximum (lowers the maximum intensity, therefore assigns the same value to high intensities), colour step (decreases the number of colours used to display the raw data) and blanking (relevant for the next step). one to two seconds are necessary to go through all steps and obtain a cross-section sediment profile. In the laboratory, the following materials have been tested as sediments: sand (Hostun sand, maximum grain size = 500 μm), gravels (diameter 6-10 mm), stones (mean diameter 20-30 mm), low concentration sludge (5-6% in mass) and high concentration sludge (30% in mass). The sludge was sampled from one of the Greater Lyon wastewater treatment plants to investigate loose water-sediment interface and to qualitatively observe how the sensor signal may differ in the presence of such material compared to sand and gravel.
Thanks to this construction, the catamaran and the sonar were kept parallel to the lateral walls of the sewer and horizontal angles between the central axis sewer line and the central axis of the sonar were neglected. In order to correct potential positional mistakes, a graphical tool has been implemented in the Matlab® code, based on superimposition of the theoretical and the measured profiles (pipe and sediments): singularities in pipe (such as sidewalk edges, angles) were helpful for this correction. If the singularity was above the free surface, the distance between the free surface (i. e. the sonar at a known depth below the surface) and this remarkable point has been measured with a ruler for each cross-section.
Access to and conversion of raw data In the standard mode of operation, the ME 1512 sonar delivers only images with a colour scale from 0 to 255 for signal intensity (Figure  1 , left) and interface profiles represented as superimposed white lines after the interface detection algorithm is applied. There is no access to raw data files. In order to test a new algorithm and facilitate data processing, access to raw data is a pre-requisite. For this research, the manufacturer agreed 1) to explain how to access the raw image data and 2) to describe how raw image data are coded in the software tool. The image files (format. img) raw data are coded in hexadecimal notation and need to be translated into numerical values for further calculations. A Matlab® code has been written to extract numerical values from the.
Implementation of a new algorithm The maximum and ¾ maximum algorithms provided by the manufacturer (details of equations are not available to the user) are usually applied to detect the water-sediment interface. From the preliminary laboratory tests, a new algorithm based on the maximum gradient of the intensities along each sector instead of the absolute values of intensities was suggested in  Pouzol (2011)  as a possible solution to improve the accuracy of the position of the water-sediment interface. The proposed maximum gradient algorithm includes three main steps: 1) for each sector, the gradient of the intensity signal is calculated, using the diff function in Matlab®, as a vector with 249 gradient values, 2) the position P k of the maximum gradient in the sector k is determined (Equation (  1 )) and 3) the water-sediment (2) Accurate identification of the boundary between sediment and pipe walls.
Calculation of sediment areas and volumes The cross-section profile of the sewer pipe without sediments is needed for these calculations. It can be either measured (best solution) or extracted from a GIS or any other database. By translation, the validated measured sediment profile is superimposed to the sewer pipe profile. The cross-section area (m 2 ) of sediments is then calculated by integration of the difference between the pipe without sediments and the validated sediment profile, from the left to the right side of the pipe. When successive cross-sections are measured, it is then possible to estimate the sediment volume (m 3 ) by interpolation between cross-sections and integration along the explored sewer reach. The sediment volume is more accurate if the distance between successive cross-sections is short (maximum a few metres).
Conclusions and perspectives A new gradient algorithm has been proposed and tested to better estimate water-sediment and sewer wall interfaces. The maximum gradient algorithm results in a better accuracy, especially for sewer wall detection. This improves the accuracy of the superimposition of the sewer pipe profile with the sediment profile. An additional step of data checking and validation has been added in a Matlab® code aiming to automatically calculate sediment areas and volumes. by at least two of the three algorithms. For each case, five profiles have been measured repeatedly.
The next contribution deals with the loss of phylogenetic diversity with extinction. Are there phylogenetic signals in extinctions? What is the role of extrinsic and intrinsic factors in extinctions, and what is the role of phylogeny in data exploration and analysis  (Grandcolas et al. 2010  )? Are extinction drivers similar to different groups of organisms? What is the role of evolutionary models in the patterns observed? These questions are here explored by  Yessoufou and Davies (chapter "  Reconsidering the Loss of Evolutionary History: How Does Non-random Extinction Prune the Tree -of-Life? "). They fi rst review the main extinction drivers, showing that the most relevant might be quite different among vertebrates, invertebrates and plants. By exploring how non-random extinction prunes the Tree of Life under different models of evolution, they call our attention to the fact that the model of evolution is likely to be a key explanatory of the loss of evolutionary history.
They also argue that more branches are likely to be lost from the Tree of Life under the speciational model of evolution. Many of our considerations about the conservation of the Tree of Life are based on our knowledge of a micro-fraction of the living world, given that we often focus on organisms that are very close to human eyes, like vertebrates, vascular plants, and a few emblematic insects. Likewise, most of the phylogenies used to this purpose are based on molecular data, very often on very small sets of short gene sequences. An advantage of molecular data for phylogenetic inference is provision of a standardized set of characters, often refl ecting the main patterns of relationship of the species in a group of organisms. However, the extent to which these genes portions evolve and refl ect the evolution of other traits is seldom well studied.
In addition, he extends this framework to show how the initial slope of the rarefaction curve ( ΔPD ) can be used as a fl exible measure of phylogenetic evenness , phylogenetic beta-diversity or phylogenetic dispersion , depending on the unit of accumulation. In chapters " Support in Area Prioritization Using Phylogenetic Information " and " Assessing Hotspots of Evolutionary History with Data from Multiple Phylogenies: An Analysis of Endemic Clades from New Caledonia ", the question of resampling and support of the dataset for defi ning priority areas is studied in the framework of evolutionary distinctiveness (ED). In chapter " Support in Area Prioritization Using Phylogenetic Information ", Daniel Rafael Miranda-Esquivel develops one scheme to verify the support for area ranking using a jackknife resampling strategy.
In this proposition, one can evaluate the more adequate index and the support of the area ranking with different probability values when deleting phylogenies, and/or areas and/or species. In chapter " Assessing Hotspots of Evolutionary History with Data from Multiple Phylogenies: An Analysis of Endemic Clades from New Caledonia ", we and our collaborators Antje Ahrends and Pete Hollingsworth, propose a scheme for solving the problem of sampling bias in datasets with phylogenies coming from independent and so, non-standardized, spatial sampling. We use the rarefaction of phylogenies to assess the role of the number of phylogenies, of species richness and of the infl uence of individual phylogenies on site's scores. And then we design a resampling strategy using multiple phylogenies to verify the stability of the results.
2003; Mao et al. 2005; Chiarucci et al. 2008). The original purpose of rarefaction was to allow the comparison of datasets with differing amounts of sampling effort  (Sanders 1968 ). Assemblages can be compared "fairly" when rarefied to the same number of accumulation units  (Gotelli and Colwell 2001). However, rarefaction has broader application than this single purpose. Depending of the unit of accumulation, the shape of the rarefaction curve provides information on ecological evenness  (Olszewski 2004 ) and beta-diversity  (Crist and Veech 2006). Rarefaction of species richness also forms the basis of estimators of species richness, including unseen species  (Colwell and Coddington 1994). In the case of PD, species-based rarefaction curves also allow for a measure of phylogenetic dispersion  (Webb et al.
2002) , effectively the expected PD for some given number of species  (Nipperess and Matsen 2013). A solution for the rarefaction of PD is therefore desirable as it will allow for these applications to be realised for phylogenetically explicit datasets. Rarefaction of Phylogenetic Diversity, using an algorithmic solution of repeated sub-sampling, has now been done several times (see for example  Lozupone and Knight 2008; Turnbaugh et al. 2009; Yu et al. 2012 ). However, an analytical solution for PD rarefaction, similar to that determined by  Hurlbert (1971)  for species richness, is preferable both because its results are exact (not dependent on the number of repeated subsamples) and substantially more computationally efficient. Nipperess and Matsen (2013)  recently published just such a solution for both the mean and variance of PD under rarefaction.
I do not include a solution for variance of PD under rarefaction due to its complexity when given in this form and instead refer the reader to  Nipperess and Matsen (2013). I extend this framework to show how the initial slope of the rarefaction curve (∆PD) can be used as a flexible measure of phylogenetic evenness, phylogenetic beta-diversity or phylogenetic dispersion, depending on the unit of accumulation. I apply PD rarefaction and the derived ∆PD measure to real ecological datasets to demonstrate its usefulness in addressing ecological questions. Finally, I discuss some future directions for the extension and application of PD rarefaction.
Application The following is a demonstration of the application of PD rarefaction, and the derived ∆PD statistics, to real ecological datasets. These applications are not intended to provide definitive answers to ecologically important questions but are, rather, simple demonstrations of how PD rarefaction can allow new analyses to be undertaken and, hopefully, new insights gained. In all these applications, I have used published data on mammals. This is principally for convenience as mammals  (Bininda-Emonds et al. 2007 ) and birds  (Jetz et al. 2012)  are the only major taxonomic groups for which comprehensive specieslevel supertrees are available. I have used an updated version of the mammal supertree of  Bininda-Emonds et al. (2007)  published as supplementary material by  Fritz et al. (2009).
Standardisation of Sampling The most commonly used application for rarefaction is standardisation to allow comparisons to be made between datasets with differing amounts of sampling effort. Standardisation can be achieved by rarefying all datasets back to a common (typically the minimum) number of accumulation units  (Sanders 1968; Gotelli and Colwell 2001). Law et al. (1998)  surveyed bats in ten State Forests of the south-west slopes region of New South Wales, Australia. Survey methods were a combination of ultrasonic detectors, harp-traps, mist-nets and trip-lines. For the purposes of this demonstration, only data from the harp-traps will be used. A harp-trap is a rectangular frame, stringed vertically with nylon line, placed so as to intercept the flight path of low-flying bats  (Tidemann and Woodside 1978).
Phylogenetic Beta-Diversity Phylogenetic beta-diversity is effectively the turnover of branch lengths between samples in space and/or time. Like its species-level equivalent, phylogenetic betadiversity can be measured on a pair-wise basis  (Lozupone and Knight 2005; Bryant et al. 2008; Nipperess et al. 2010)  or as a single value for a set of samples  (Anderson et al. 2010). Rarefaction of PD provides a means for deriving a single value of The Rarefaction of Phylogenetic Diversity: Formulation, Extension and Application beta-diversity for a set of samples of any size via the ∆PD measure, which is a phylogenetic analogue of the additive partitioning approach of  Crist and Veech (2006). Morton et al. (1994)  compiled data on small mammal assemblages for 245 sites in arid Australia. I calculated beta-diversity for two regions from this dataset -Tanami desert and Uluru-Kata Tjuta National Park, Northern Territory.
(2001)  as constituting the Australasian biogeographic realm. Data were sourced from the wildfinder database (http://worldwildlife. org/pages/wildfinder) of the World Wildlife Fund. Eight ecoregions were excluded from the analysis because they had less than two species and thus a ∆PD value could not be calculated. The ecoregions show huge variation in species richness and, as expected, Phylogenetic Diversity is highly dependent on species richness (Fig. 6 ). Tropical ecoregions (such as the central range Montane rainforests, New Guinea) have high species richness and high Phylogenetic Diversity (Fig. 6 , Table  4 ). When considering phylogenetic dispersion, however, other ecoregions show unusually high or low values given their species richness (Table  4 ). The ecoregion with the lowest ∆PD is the New Caledonia dry forests.
This kind of correction has previously been done by including species richness as an explanatory variable in a statistical model and taking the residuals  (Davies et al. 2008)  or by comparison to a null model derived by repeated subsampling  (Davies et al. 2007). The latter method is often used as a statistical test of phylogenetic dispersion (also known as phylogenetic structure) where random draws are taken from a species pool, representing a null community assembly process  (Webb 2000). Such methods are no longer necessary as the exact relationship between species richness and PD is described by the rarefaction curve  (Nipperess and Matsen 2013). Further, the exact analytical solution is computationally efficient, allowing for practical application to very large datasets. By removing the effect of species richness, we can identify "evolutionary hotspots" with higher than expected phylogenetic diversity  (Davies et al.
Number of Replicates Hedges ( 1992 ) presented the number 1825 as the number of replicates needed to obtain an accuracy of ±1 % for a bootstrapping proportion of 95 %. Although the higher the number of replicates the higher the accuracy of the estimation of the bootstrap or jack-knife value,  Pattengale et al. ( 2010 )  introduced a stopping criteria that yield lower fi gures as 500 replicates to get robust bootstrapping values for a 2500 taxa analysis. I randomized each scenario 10,000 times, that could be considered intuitively an appropriate number of replicates to estimate the jack-knife proportion for conservation purposes. For these analyses, I used a modifi ed version of the program Richness  (Posadas et al. 2001 )  to randomize the data and to perform the index calculations [Jrich: available from https://github.
Empirical Examples First Case: The Original Ranking Does Not Mean Support  Posadas et al. ( 2001 )  evaluated the conservation ranking in southern South America areas. They found that depending on the index used, the selected area changed, as the best area could be: Santiago (D), Ñuble (F), Valdivia (H), or the Malvinas islands (K). Also, for a single index, the values could be misleading, as the differences between the W index values are quite small, and the ranking could be an artifact rather than a real result (Table  1  ). I reanalyzed their dataset and found that the best index for this analys is is I s (Fig. 2  ) as this index that has the highest jack-knife frequency. The most stable area using I s or raw W (the second best index), was the Malvinas islands, a candidate to be the best area (Fig.
Introduction Opening wide the sampling window in biodiversity studies is a major goal today, and this leads to two major research challenges. On the one hand is the diffi culty of dealing with big data, as, for example, those from entire genomes. Now that the main barriers to obtain enormous sequences seem to be broken, and that this kind of data is becoming easy and much cheaper to be obtained, the main constraint is the analysis of such huge datasets. On the other hand are the diffi culties associated with synthetizing evidences produced by several independent studies where, by defi nition, the sampling protocols are not standardized. Both issues are at the core of the analysis of phylogenetic diversity for conservation and deserve more attention if we are to produce sound guidelines for conservation.
In the current paper we tackle this topic with the aim to identify sites with high levels of phylogenetic diversity. This type of study raises some methodological challenges. In an ideal world, one would design a sampling strategy involving equal sampling effort (or at least quantifi ed sampling effort) at multiple sites for multiple sets of taxa, sampled for a common and comparable set of characters, and with the data analyzed in a common and comparable analytical framework. Unfortunately such a dataset does not exist presently for New Caledonia. Instead, we have taken an approach to make the best use of the currently available information by mining the literature for tree topologies, and then developing an analytical framework that copes with the shortcomings of the extant dataset. Specifi cally we have pulled together the available data consisting of multiple phylogenies from different groups of organisms, of different levels of species richness , built from different character sets, different analytical methods and partially overlapping geographical locations.