1,721,013 research outputs found
RCMIP Phase 1 Data
No full textRCMIP phase 1 dataset accompanying the initial submission of Reduced complexity model intercomparison project phase 1: Protocol, results and initial observations to Geoscientific Model Development. The full author list can be found in the paper.</p
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
On the State of Reduced Complexity Climate Modelling
No full text© 2021 Zebedee Ralph John NichollsReduced complexity climate models are the most computationally efficient end of the climate model hierarchy. They are widely used in climate science research, particularly at the science-policy interface. However, unlike the Coupled Model Intercomparison Project (CMIP) that focuses on more comprehensive global Earth System models, there has never been a systematic intercomparison project targeted specifically at reduced complexity climate models. In this PhD research, I introduce the Reduced-Complexity Model Intercomparison Project (RCMIP) and discuss the state of reduced complexity climate modelling. Beyond the intercomparison and evaluation efforts of RCMIP, I also consider reduced complexity model development and application.
In the first part of the PhD, before introducing RCMIP, I present a CMIP-derived dataset. This dataset solves the many data-handling challenges which researchers face when they wish to extract regional-aggregate timeseries from raw CMIP data. Within the PhD, these regional-aggregate timeseries are a key resource for comparing reduced complexity climate models with the more comprehensive models participating in CMIP. The dataset, which currently comprises over 40 000 timeseries (approximately 8 million model years) for 83 variables from over 50 CMIP5 and CMIP6 models, 40 experiments and up to 100 ensemble members per model, has been made freely available. Whether for overall comparison purposes or for development and calibration of individual modules, the distilled Earth System Model data is a prerequisite for robust development of reduced complexity models.
In the evaluation component of the thesis, I introduce RCMIP and perform two pieces of reduced complexity climate model evaluation. The first part focuses on the ability of reduced complexity climate models to emulate the response of more comprehensive models. I find that reduced complexity climate models can emulate the response of more comprehensive models to within a root-mean-square error of 0.2C over a range of experiments. However, there are clear differences between idealised and scenario-based experiments, with noticeably worse emulation ability in scenario-based experiments. The second evaluation part focuses on reduced complexity climate models' probabilistic distributions. Probabilistic distributions are used to make projections of not only our best-estimate of e.g. global-mean temperature but also the range of possible outcomes e.g. 5th or 95th percentiles. Across the reduced complexity climate models, there is notable variation in their agreement with other lines of evidence and their resulting projections. The variation emphasises the importance of evaluating reduced complexity climate models' probabilistic distributions they are using to identify any key biases before using them. Under the low-emissions SSP1-1.9 scenario median peak warming projections range from 1.3 to 1.7C (relative to 1850-1900, using an observationally-based historical warming estimate of 0.8C between 1850-1900 and 1995-2014). There is a clear need for further research into constraining model projections, particularly given the international community's goal of limiting warming to below 1.5C above pre-industrial in the long-term.
In the application component of the thesis, I consider the question of the remaining carbon budget. In the IPCC's Special Report on Global Warming of 1.5C (SR1.5), the remaining carbon budget was calculated on the assumption that there is a strictly linear relationship between cumulative CO2 emissions and CO2-induced warming in its calculation of the remaining carbon budget. I examined the validity of this assumption using a reduced complexity climate model which explicitly quantifies feedbacks and non-linearities within the climate system. I find that considering non-linearities between cumulative CO2 emissions and CO2-induced warming increases remaining carbon budget estimates by approximately 10%. To provide continuity with existing approaches, I propose an update to the SR1.5 remaining carbon budget assessment framework which allows such non-linearities to be included.
In the development component of the thesis, I present an updated methodology for deriving probabilistic distributions with reduced complexity climate models. Compared to previous work, I add extra variables to the historical constraining analysis and add an extra step, which I refer to as subsampling. With the subsampling, it is possible to derive probabilistic distributions which better reflect other, independent lines of evidence whilst also maintaining the covariance between model parameters e.g. equilibrium climate sensitivity and aerosol effective radiative forcing. The outputs from the development are used for the MAGICC reduced complexity model submission to RCMIP's probabilistic evaluation phase (RCMIP Phase 2). This is a key feature when reduced complexity models are used as `integrators of knowledge' i.e. as ways of transferring insights from one domain to another in a computationally efficient way. The reproducible pipeline used for the derivation provides a basis for future work focussed on each component within the probabilistic derivation methodology e.g. how the likelihood of certain parameter values are given the reduced complexity model output and observations, how the Monte Carlo Markov Chain is configured and how trade-offs between different lines of evidence can be considered in the subsampling step.
I conclude with suggested next steps for reduced complexity climate modelling, based on my experiences performing each step in the development-evaluation-application cycle. There are many areas for future development. I think the key next step is in the evaluation and development of two key reduced complexity climate model parameterisations: the carbon cycle and aerosol effective radiative forcing.
The thesis provides new insights into the behaviour and performance of reduced complexity climate models, particularly understanding of their limitations. Thanks to the methods and approaches developed in the thesis, I was also able to co-develop the assessment pipelines used within the forthcoming IPCC AR6 report - both for the creation of a probabilistic version of MAGICC7 that captures key characteristics of the climate system (such as equilibrium climate sensitivity and transient climate response) and for the categorisation of WG3 emissions scenarios in terms of the their global-mean temperature implications. The insights from the thesis allow users of reduced complexity climate models, most notably at the science-policy interface, to have greater confidence when interpreting reduced complexity climate model results
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
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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