1,720,960 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Engineering geology characterization of slope deposits and physically-based assessment of shallow landslide susceptibility (Alpi Apuane, Italy)
Full text linkIn this work we present the results of engineering geology characterization of slope deposits and assessment of shallow landslide susceptibility by means of a probabilistic physically-based model for the Western sector of the Alpi Apuane (Northern Apennines, Italy). The Alpi Apuane are a Tertiary metamorphic complex which is undergoing fast tectonic uplift, exhumation and erosion in respect to neighboring regions (the coastal Versilia Plain and Garfagnana Valley). For these reasons, the morphology of the Alpi Apuane is characterized by high relief energy, as highlighted by elevation differences up to around 2,000 m and deep river valleys with steep slopes. Moreover, the study area records annual precipitations among the highest in Italy (up to around 2,500 mm/y) and, especially in the last decades, frequent intense rainfall events (i.e.: 1996, 1998, 2000, 2011, 2013, 2014). In this framework landslides are widespread, especially shallow landslides involving unconsolidated slope deposits overlying bedrock. In order to assess shallow landslide susceptibility, we used a hydrological model coupled to a limit-equilibrium infinite-slope stability model. Reliability of results by physically-based models depends on accuracy of map distribution of input data which, however, is usually almost unknown. Hence, fieldwork and laboratory tasks were carried out to map engineering geology characters of slope deposits. For a set of hundreds of field sampling points, we acquired: depth to the bedrock, geotechnical horizons, unit weight, as well as soil samples for lab analysis. The distribution of points were chosen by observing that engineering geology properties of slope deposits depend on both bedrock lithology and morphometric conditions. Then, for a subset of the sampling points, we performed hydraulic conductivity measurements. Geotechnical determinations allowed us to estimate the friction angle ranges for different slope deposit types. In order to obtain the map distribution of engineering geology parameters, we implemented a spatial analysis by clustering morphometric variables stratified as a function of bedrock lithological units. Multitemporal visual interpretation of orthophotos (2003-2016) allowed us to obtain the database for a new shallow landslide inventory, which later underwent field accuracy assessment. By integrating the inventory to geology, we identified those bedrock lithological units where the infinite-slope assumption for shallow landslide modeling could be reasonably applied. In order to take into account and evaluate the effects of input parameters uncertainty, we implemented the slope stability-hydrological model by means of a Monte Carlo simulation. Assuming that the cohesion of slope deposits changes in space and time depending upon seasonal variation of land cover and precipitations, we calibrated the model by means of a back-analysis aimed at estimating the cohesion intervals which allow for optimization of the final predictive performance within the shallow landslide regions. This task was performed by using both prediction-rate curve and ROC diagrams. Finally, the results of susceptibility assessment, as well as maps/diagrams useful to describe the variability/uncertainty of results are critically discussed
Modelling-mapping slope deposits depth and uncertainty assessment by means of machine learning approaches
Full text linkShallow landslides triggered by heavy rainfall are a common natural phenomenon in mountain areas. Climate changes and increasing urban pressure make this phenomenon a widespread source of natural hazard. For this reason, the interest of scientific community concerning the development of robust shallow landslide susceptibility/ hazard assessment methods for wide areas (regional scale) has steadily increased in the last decades. Many methods are available to achieve this goal, however, researchers are generally focused on statistics (data driven) or physically-based methods. For both approaches, the depth of Slope Deposits (SD: the surficial soil involved by landsliding which covers the bedrock) is an important parameter in order to perform accurate analysis. Furthermore, the SD depth is required for many physically-based models available in the literature. Nevertheless, this information is generally unknown at map scale, which affects uncertainty and reliability of susceptibility/hazard assessments.
In this context, this work is focused on obtaining predictive SD depth maps for wide areas by means of geostatistics methods suitable to consider variability and uncertainty of the input/output data.
The study area is located in Northern Tuscany where, in the last years, we developed research projects on engineering geology characterization of SD. Hence, a large dataset of SD depth obtained by field survey (more than 1,000 oobservations) is used in this work. Many geo-environmental variables such as: geology, land use, morphometric variables, are considered in the analysis. Morphometric variables (eg. flow accumulation, slope and hillslope curvature) are derived from a digital elevation model with cell size of 10 m. Two different machine learning techniques are used to map SD depth: clustering and artificial neural networks. The supervised clustering analysis is performed with probabilistic and fuzzy algorithms. For the unsupervised clustering, the results of various maps obtained by integrating different sets of input variables are spatially combined (data fusion) in order to obtain a single map. The analysis performed with artificial neural networks has been implemented by a feed-forward multi-layer neural network. In order to exploit the field measurement dataset, also the effect of samples geographic neighbourhood were considered.
The results show the feasibility of the methods for regional scale mapping. Moreover the results are discussed and analyzed in order to identify best solutions to evaluate and represent the SD depth uncertainty
A new shallow landslides inventory for Southern Lunigiana (Tuscany, Italy) and analysis of predisposing factors
No full textA new inventory of shallows landslides for a study area located within Southern Lunigiana valley has been obtained by means of digital visual interpretation of orthophoto maps acquired in the years 2003, 2007, 2010 and 2013. A total of 331 shallow landslides occurred during the decade 2003-2013 have been identified, resulting in an average landslide density of 1.1 landslides per square kilometers. A spatial analysis has been implemented to investigate landslide distribution in respect to predisposing factors: bedrock geology, land use and morphometry (elevation, slope steepness, flow accumulation, slope aspect). Results highlight that bedrock lithology and land use are important factors affecting shallow landslide distribution. While all the morphometric parameters analyzed are correlated to shallow landslides density, slope aspect seems to be the prominent conditioning for landslide occurrence
Testing and improving the Rock Mass Quality Index (RQI) in North-Western Tuscany (Italy)
Full text linkMany geo-mechanical classification systems for rock masses have been developed for engineering geology applications. However, most of them are site-specific and they take into account the combination of rock mass data related to different geological and physico-mechanical properties. For these reasons, they may hardly be applied when regional, continuous representation over wide areas (map scale) are necessary (i.e. spatial planning, seismic microzoning).
The aim of this study is to test and improve an existing method for engineering geology mapping of rock masses based on quantitative integration of geological information, fieldwork geo-mechanical measurements, lab determinations and spatial analyses. This method has been applied to a new study area located in North-Western Tuscany (Italy) where due to a complex structural setting, different structural and lithological units of the Northern Apennines chain crop out. Fieldwork measurements were performed for the outcropping geological formations by choosing sets of sites representative of different rock mass characters (lithology, weathering, jointing), both at local and wide scale. For each surface or sub-surface site, a regular grid of measuring points was defined, where each point underwent rebound measurements (R - Schmidt hammer). Frequency of grid points and measurements were chosen in order to obtain reasonable statistical stability of average site rebound values. Following methods from the literature, the Geological Strength Index (GSI) was also estimated for each investigation site. We collected representative rock samples for lab evaluation of unit weight to be used along with R to calculate the Rock Mass Quality Index (RQI). In fact, according to the literature, unit weight is related to weathering and mechanical properties of rocks. A statistical analysis of correlation between both R – GSI and RQI – GSI was performed and the results are presented and discussed. Moreover, the spatial analysis of the whole dataset confirm that the proposed method allows one to recognize different engineering geology characters among different formations, as well as to identify different geo-mechanical units within the same formation. The spatial analysis of RQI also highlights variability among different structural domains of the study area. In conclusion, this study supports this method as suitable for cartographic engineering geology applications
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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