1,721,027 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
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
The optimal multimodel ensemble of bias-corrected CMIP5 climate models over China
No full textA multimodel ensemble of general circulation models (GCM) is a popular approach to assess hydrological impacts of climate change at local, regional, and global scales. The traditional multimodel ensemble approach has not considered different uncertainties across GCMs, which can be evaluated from the comparisons of simulations against observations. This study developed a comprehensive index to generate an optimal ensemble for two main climate fields (precipitation and temperature) for the studies of hydrological impacts of climate change over China. The index is established on the skill score of each bias-corrected model and different multimodel combinations using the outputs from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Results show that the optimal ensemble of the nine selected models accurately captures the characteristics of spatial–temporal variabilities of precipitation and temperature over China. We discussed the uncertainty of subset ensembles of ranking models and optimal ensemble based on historical performance. We found that the optimal subset ensemble of nine models has relative smaller uncertainties compared with other subsets. Our proposed framework to postprocess the multimodel ensemble data has a wide range of applications for climate change assessment and impact studies
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
Image Classification with few samples and Its application in agriculture
No full textDeep learning and computer vision have demonstrated remarkable success across various real-world applications, from autonomous driving to video analysis. This thesis explores the application of advanced deep learning techniques in agriculture, focusing on critical tasks such as disease detection and fine-grained or ultrafine- grained classification of plants and animals. The research primarily addresses two crucial scenarios in smart agriculture: Few-Shot Class Incremental Learning (FSCIL) and Source-Free Domain Adaptation (SFDA). FSCIL targets the continuous learning of novel classes with limited samples, a common challenge in agriculture where new plant or animal species are frequently discovered in the wild and need integration into existing models. A major problem in this process is mitigating catastrophic forgetting of previously learned knowledge while adapting to new data. SFDA, on the other hand, focuses on transferring models trained in one domain to others. This is particularly relevant in agricultural contexts where plants or animals may be at different growth stages, or when models trained on sketches or 3D models need to perform accurately on real-life images.
To tackle these real-world agricultural scenarios, this work develops sophisticated neural network architectures and training methodologies. These approaches enhance fine-grained and ultra-fine-grained visual categorization tasks, crucial for distinguishing between closely related plant species or animals with limited data availability. The research introduces innovative techniques for feature extraction, representation learning, and knowledge transfer. These methods effectively address common challenges in FSCIL and SFDA scenarios, such as catastrophic forgetting and poor adaptation to new classes or domains. Moreover, the thesis proposes novel solutions for domain shift, enabling models to adapt to new environments without access to source domain data. Extensive experiments on various benchmark datasets, including standard and custom ultra-fine-grained datasets specific to agriculture, demonstrate the efficacy of the proposed methods. The contributions advance the state-of-the-art in image classification with few samples and provide practical solutions for real-world smart agricultural challenges. This research lays the groundwork for more efficient and accurate plant identification, crop monitoring, and disease detection systems. Ultimately, it contributes to improving various agricultural practices, showcasing the potential of deep learning in revolutionizing smart agriculture.Thesis (PhD Doctorate)Doctor of PhilosophySchool of Engineering and Built EnvironmentGriffith SciencesFull Tex
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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