1,721,145 research outputs found
Repeated glacial lake outburst floods in Patagonia
Full text linkFive similar glacial-lake outburst floods (GLOFs) occurred in April, October, December 2008, March and September 2009 in the Northern Patagonia Icefield. On each occasion, Cachet 2 Lake, dammed by the Colonia Glacier, released circa 200-million m3 water into the Colonia River. Refilling has occurred rapidly, such that further outbreak floods can be expected. Pipeflow calculations of the subglacial tunnel drainage and 1D hydraulic models of the river flood give consistent results, with an estimated peak discharge surpassing 3,000 m3 s−1. These floods were larger in magnitude than any flood on record, according to gauged data since 1963. However, geomorphological analysis of the Colonia valley shows physical evidence of former catastrophic outburst floods from a larger glacial-lake, with flood discharges possibly as high as 16,000 m3 s−1. Due to potential impacts of climate change on glacier dynamics in the area, jökulhlaups may increase future flood risks for infrastructure and population. This is particularly relevant in view of the current development of hydropower projects in Chilean Patagonia
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
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
r_rnrfa: Release related to draft paper (May 2016)
No full text<p>Release related to the following draft paper:</p>
<p>RNRFA: an R package to retrieve, filter and visualize data from the UK National River Flow Archive Submitted to the RJournal in May 2016</p>
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Exploring data mining for hydrological modelling
Full text linkTechnological advances in computer science, namely cloud computing and data mining, are reshaping the way the world looks at data. Data are becoming the drivers of discoveries and strategic developments. In environmental sciences, for instance, big volumes of information are produced by monitoring networks, satellites and model simulations and are processed to uncover hidden patterns, correlations and trends to, ultimately, support policy and decision making.
Hydrologists, in particular, use models to simulate river discharges and estimate the concentration of pollutants as well as the risk of floods and droughts. The very first step of any hydrological modelling exercise consists of selecting an appropriate model. However, the choice is often made by the modeller based on his/her expertise rather than on the model's suitability to reproduce the most important processes for the area under study. Since this approach defeats the ``scientific method'' for its lack of reproducibility and consistency across experts as well as locations, a shift towards a data-driven selection process is deemed necessary.
This work presents the design, development and testing results of a completely novel data mining algorithm, called AMCA, able to automatically identify the most suitable model configurations for a given catchment, using minimum data requirements and an inventory of model structures. In the design phase a transdisciplinary approach was adopted, borrowing techniques from the fields of machine learning, signal processing and marketing.
The algorithm was tested on the Severn at Plynlimon flume catchment, in the Plynlimon study area (Wales, UK). This area was selected because of its reliable measurements and the homogeneity of its soils and vegetation. The Framework for Understanding Structural Errors (FUSE) was used as sample model inventory, but the methodology can easily be adapted to others, including more sophisticated model structures.
The model configuration problem, that the AMCA attempts to solve, can be categorised as ``fully unsupervised'' if there is no prior knowledge of interactions and relationships amongst observed data at a certain location and available model structures and parameters. Therefore, the first set of tests was run on a synthetic dataset to evaluate the algorithm's performance against known outcomes. Most of the component of the synthetic model structure were clearly identified by the AMCA, which allowed to proceed with further testing using observed data.
Using real observations, the AMCA efficiently selected the most suitable model structures and, when coupled with association rule mining techniques, could also identify optimal parameter ranges. The performance of the ensemble suggested by the combination of AMCA and association rules was calibrated and validated against four widely used models (Topmodel, ARNOVIC, PRMS and Sacramento). The ensemble configuration always returned the best average efficiency, characterised by the narrowest spread and, therefore, lowest uncertainty.
As final application, the full set of FUSE models was used to predict the effect of land use changes on catchment flows. The predictive uncertainty improved significantly when the prior distributions of model structures and parameters were conditioned using the AMCA approach. It was also noticed that such improvement is due to constrains applied to both model and parameter space, however the parameter space seems to contribute more.
These results confirm that a considerable part of the uncertainty in prediction is due to the definition of the prior choice of the model configuration and that more objective ways to constrain the prior using formal data-driven techniques are needed. AMCA is, however, a procedure that can only be applied to gauged catchment. Future experiments could test whether AMCA configurations could be regionalised or transferred to ungauged catchments on the basis of catchment characteristics.Open Acces
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